Application of Graph Theory to Cost-Effective Fire Protection of Chemical Plants During Domino Effects.

PubMed

Khakzad, Nima; Landucci, Gabriele; Reniers, Genserik

2017-09-01

In the present study, we have introduced a methodology based on graph theory and multicriteria decision analysis for cost-effective fire protection of chemical plants subject to fire-induced domino effects. By modeling domino effects in chemical plants as a directed graph, the graph centrality measures such as out-closeness and betweenness scores can be used to identify the installations playing a key role in initiating and propagating potential domino effects. It is demonstrated that active fire protection of installations with the highest out-closeness score and passive fire protection of installations with the highest betweenness score are the most effective strategies for reducing the vulnerability of chemical plants to fire-induced domino effects. We have employed a dynamic graph analysis to investigate the impact of both the availability and the degradation of fire protection measures over time on the vulnerability of chemical plants. The results obtained from the graph analysis can further be prioritized using multicriteria decision analysis techniques such as the method of reference point to find the most cost-effective fire protection strategy. © 2016 Society for Risk Analysis.

Evolving bipartite authentication graph partitions

DOE PAGES

Pope, Aaron Scott; Tauritz, Daniel Remy; Kent, Alexander D.

2017-01-16

As large scale enterprise computer networks become more ubiquitous, finding the appropriate balance between user convenience and user access control is an increasingly challenging proposition. Suboptimal partitioning of users’ access and available services contributes to the vulnerability of enterprise networks. Previous edge-cut partitioning methods unduly restrict users’ access to network resources. This paper introduces a novel method of network partitioning superior to the current state-of-the-art which minimizes user impact by providing alternate avenues for access that reduce vulnerability. Networks are modeled as bipartite authentication access graphs and a multi-objective evolutionary algorithm is used to simultaneously minimize the size of largemore » connected components while minimizing overall restrictions on network users. Lastly, results are presented on a real world data set that demonstrate the effectiveness of the introduced method compared to previous naive methods.« less

Evolving bipartite authentication graph partitions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pope, Aaron Scott; Tauritz, Daniel Remy; Kent, Alexander D.

As large scale enterprise computer networks become more ubiquitous, finding the appropriate balance between user convenience and user access control is an increasingly challenging proposition. Suboptimal partitioning of users’ access and available services contributes to the vulnerability of enterprise networks. Previous edge-cut partitioning methods unduly restrict users’ access to network resources. This paper introduces a novel method of network partitioning superior to the current state-of-the-art which minimizes user impact by providing alternate avenues for access that reduce vulnerability. Networks are modeled as bipartite authentication access graphs and a multi-objective evolutionary algorithm is used to simultaneously minimize the size of largemore » connected components while minimizing overall restrictions on network users. Lastly, results are presented on a real world data set that demonstrate the effectiveness of the introduced method compared to previous naive methods.« less

Complementary Network-Based Approaches for Exploring Genetic Structure and Functional Connectivity in Two Vulnerable, Endemic Ground Squirrels

PubMed Central

Zero, Victoria H.; Barocas, Adi; Jochimsen, Denim M.; Pelletier, Agnès; Giroux-Bougard, Xavier; Trumbo, Daryl R.; Castillo, Jessica A.; Evans Mack, Diane; Linnell, Mark A.; Pigg, Rachel M.; Hoisington-Lopez, Jessica; Spear, Stephen F.; Murphy, Melanie A.; Waits, Lisette P.

2017-01-01

The persistence of small populations is influenced by genetic structure and functional connectivity. We used two network-based approaches to understand the persistence of the northern Idaho ground squirrel (Urocitellus brunneus) and the southern Idaho ground squirrel (U. endemicus), two congeners of conservation concern. These graph theoretic approaches are conventionally applied to social or transportation networks, but here are used to study population persistence and connectivity. Population graph analyses revealed that local extinction rapidly reduced connectivity for the southern species, while connectivity for the northern species could be maintained following local extinction. Results from gravity models complemented those of population graph analyses, and indicated that potential vegetation productivity and topography drove connectivity in the northern species. For the southern species, development (roads) and small-scale topography reduced connectivity, while greater potential vegetation productivity increased connectivity. Taken together, the results of the two network-based methods (population graph analyses and gravity models) suggest the need for increased conservation action for the southern species, and that management efforts have been effective at maintaining habitat quality throughout the current range of the northern species. To prevent further declines, we encourage the continuation of management efforts for the northern species, whereas conservation of the southern species requires active management and additional measures to curtail habitat fragmentation. Our combination of population graph analyses and gravity models can inform conservation strategies of other species exhibiting patchy distributions. PMID:28659969

Complementary Network-Based Approaches for Exploring Genetic Structure and Functional Connectivity in Two Vulnerable, Endemic Ground Squirrels.

PubMed

Zero, Victoria H; Barocas, Adi; Jochimsen, Denim M; Pelletier, Agnès; Giroux-Bougard, Xavier; Trumbo, Daryl R; Castillo, Jessica A; Evans Mack, Diane; Linnell, Mark A; Pigg, Rachel M; Hoisington-Lopez, Jessica; Spear, Stephen F; Murphy, Melanie A; Waits, Lisette P

2017-01-01

The persistence of small populations is influenced by genetic structure and functional connectivity. We used two network-based approaches to understand the persistence of the northern Idaho ground squirrel ( Urocitellus brunneus) and the southern Idaho ground squirrel ( U. endemicus ), two congeners of conservation concern. These graph theoretic approaches are conventionally applied to social or transportation networks, but here are used to study population persistence and connectivity. Population graph analyses revealed that local extinction rapidly reduced connectivity for the southern species, while connectivity for the northern species could be maintained following local extinction. Results from gravity models complemented those of population graph analyses, and indicated that potential vegetation productivity and topography drove connectivity in the northern species. For the southern species, development (roads) and small-scale topography reduced connectivity, while greater potential vegetation productivity increased connectivity. Taken together, the results of the two network-based methods (population graph analyses and gravity models) suggest the need for increased conservation action for the southern species, and that management efforts have been effective at maintaining habitat quality throughout the current range of the northern species. To prevent further declines, we encourage the continuation of management efforts for the northern species, whereas conservation of the southern species requires active management and additional measures to curtail habitat fragmentation. Our combination of population graph analyses and gravity models can inform conservation strategies of other species exhibiting patchy distributions.

Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks

PubMed Central

2014-01-01

Protein-protein interaction (PPI) networks carry vital information on the organization of molecular interactions in cellular systems. The identification of functionally relevant modules in PPI networks is one of the most important applications of biological network analysis. Computational analysis is becoming an indispensable tool to understand large-scale biomolecular interaction networks. Several types of computational methods have been developed and employed for the analysis of PPI networks. Of these computational methods, graph comparison and module detection are the two most commonly used strategies. This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods. Herein, we provide a comprehensive review of the major algorithms employed under each theme, including our recently published frequent subgraph method, for detecting functional modules commonly shared across multiple cancer PPI networks. PMID:24800226

Visibility Graph Based Time Series Analysis.

PubMed

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Support Vector Machine Classification of Major Depressive Disorder Using Diffusion-Weighted Neuroimaging and Graph Theory

PubMed Central

Sacchet, Matthew D.; Prasad, Gautam; Foland-Ross, Lara C.; Thompson, Paul M.; Gotlib, Ian H.

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on “support vector machines” to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities. PMID:25762941

Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.

PubMed

Sacchet, Matthew D; Prasad, Gautam; Foland-Ross, Lara C; Thompson, Paul M; Gotlib, Ian H

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on "support vector machines" to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities.

Applications of network analysis for adaptive management of artificial drainage systems in landscapes vulnerable to sea level rise

NASA Astrophysics Data System (ADS)

Poulter, Benjamin; Goodall, Jonathan L.; Halpin, Patrick N.

2008-08-01

SummaryThe vulnerability of coastal landscapes to sea level rise is compounded by the existence of extensive artificial drainage networks initially built to lower water tables for agriculture, forestry, and human settlements. These drainage networks are found in landscapes with little topographic relief where channel flow is characterized by bi-directional movement across multiple time-scales and related to precipitation, wind, and tidal patterns. The current configuration of many artificial drainage networks exacerbates impacts associated with sea level rise such as salt-intrusion and increased flooding. This suggests that in the short-term, drainage networks might be managed to mitigate sea level rise related impacts. The challenge, however, is that hydrologic processes in regions where channel flow direction is weakly related to slope and topography require extensive parameterization for numerical models which is limited where network size is on the order of a hundred or more kilometers in total length. Here we present an application of graph theoretic algorithms to efficiently investigate network properties relevant to the management of a large artificial drainage system in coastal North Carolina, USA. We created a digital network model representing the observation network topology and four types of drainage features (canal, collector and field ditches, and streams). We applied betweenness-centrality concepts (using Dijkstra's shortest path algorithm) to determine major hydrologic flowpaths based off of hydraulic resistance. Following this, we identified sub-networks that could be managed independently using a community structure and modularity approach. Lastly, a betweenness-centrality algorithm was applied to identify major shoreline entry points to the network that disproportionately control water movement in and out of the network. We demonstrate that graph theory can be applied to solving management and monitoring problems associated with sea level rise for poorly understood drainage networks in advance of numerical methods.

Robust-yet-fragile nature of interdependent networks

NASA Astrophysics Data System (ADS)

Tan, Fei; Xia, Yongxiang; Wei, Zhi

2015-05-01

Interdependent networks have been shown to be extremely vulnerable based on the percolation model. Parshani et al. [Europhys. Lett. 92, 68002 (2010), 10.1209/0295-5075/92/68002] further indicated that the more intersimilar networks are, the more robust they are to random failures. When traffic load is considered, how do the coupling patterns impact cascading failures in interdependent networks? This question has been largely unexplored until now. In this paper, we address this question by investigating the robustness of interdependent Erdös-Rényi random graphs and Barabási-Albert scale-free networks under either random failures or intentional attacks. It is found that interdependent Erdös-Rényi random graphs are robust yet fragile under either random failures or intentional attacks. Interdependent Barabási-Albert scale-free networks, however, are only robust yet fragile under random failures but fragile under intentional attacks. We further analyze the interdependent communication network and power grid and achieve similar results. These results advance our understanding of how interdependency shapes network robustness.

A comparative study of theoretical graph models for characterizing structural networks of human brain.

PubMed

Li, Xiaojin; Hu, Xintao; Jin, Changfeng; Han, Junwei; Liu, Tianming; Guo, Lei; Hao, Wei; Li, Lingjiang

2013-01-01

Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

Visibility Graph Based Time Series Analysis

PubMed Central

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it’s microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks. PMID:26571115

GraphCrunch 2: Software tool for network modeling, alignment and clustering.

PubMed

Kuchaiev, Oleksii; Stevanović, Aleksandar; Hayes, Wayne; Pržulj, Nataša

2011-01-19

Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL") for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other existing tool. Finally, GraphCruch 2 implements an algorithm for clustering nodes within a network based solely on their topological similarities. Using GraphCrunch 2, we demonstrate that eukaryotic and viral PPI networks may belong to different graph model families and show that topology-based clustering can reveal important functional similarities between proteins within yeast and human PPI networks. GraphCrunch 2 is a software tool that implements the latest research on biological network analysis. It parallelizes computationally intensive tasks to fully utilize the potential of modern multi-core CPUs. It is open-source and freely available for research use. It runs under the Windows and Linux platforms.

Interconnectedness and interdependencies of critical infrastructures in the US economy: Implications for resilience

NASA Astrophysics Data System (ADS)

Chopra, Shauhrat S.; Khanna, Vikas

2015-10-01

Natural disasters in 2011 yielded close to 55 billion in economic damages alone in the United States (US), which highlights the need to reduce impacts of such disasters or other deliberate attacks. The US Department of Homeland Security (DHS) identifies a list of 16 Critical Infrastructure Sectors (CIS) whose incapacity due to disruptions would have a debilitating impact on the nation's economy. The goal of this work is to understand the implications of interdependencies among CIS on the resilience of the US economic system as a whole. We develop a framework that combines the empirical economic input-output (EIO) model with graph theory based techniques for understanding interdependencies, interconnectedness and resilience in the US economic system. By representing the US economy as a network, we are able to analyze its topology by separately looking at its unweighted and weighted forms. Topological analysis of the US EIO network suggests that it exhibits small world properties for the unweighted case, and in the weighted case, the throughput of industry sectors follows a power-law with an exponential cutoff. Implications of these topological properties are discussed in the paper. We also simulate hypothetical disruptions on CIS in order to identify industrial sectors that experience the largest economic impacts, and to quantify systemic vulnerability in economic terms. In addition, insights from community detection and hypothetical disruption scenarios help assess vulnerability of individual industrial communities to disruptions on individual CIS. These methodologies also provide insights regarding the extent of coupling between each CIS in the US EIO network. Based on our analysis, we observe that excessive interconnectedness and interdependencies of CIS results in high systemic vulnerability. This information can guide policymakers to design policies that improve resilience of economic networks, and evaluate policies that might indirectly increase coupling between CIS.

Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis.

PubMed

Ding, Xiao Pan; Wu, Si Jia; Liu, Jiangang; Fu, Genyue; Lee, Kang

2017-09-21

The present study examined how different brain regions interact with each other during spontaneous honest vs. dishonest communication. More specifically, we took a complex network approach based on the graph-theory to analyze neural response data when children are spontaneously engaged in honest or dishonest acts. Fifty-nine right-handed children between 7 and 12 years of age participated in the study. They lied or told the truth out of their own volition. We found that lying decreased both the global and local efficiencies of children's functional neural network. This finding, for the first time, suggests that lying disrupts the efficiency of children's cortical network functioning. Further, it suggests that the graph theory based network analysis is a viable approach to study the neural development of deception.

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Sand, Olivier; Janky, Rekin's; Vanderstocken, Gilles; Deville, Yves; van Helden, Jacques

2008-07-01

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources.

Impaired cerebral blood flow networks in temporal lobe epilepsy with hippocampal sclerosis: A graph theoretical approach.

PubMed

Sone, Daichi; Matsuda, Hiroshi; Ota, Miho; Maikusa, Norihide; Kimura, Yukio; Sumida, Kaoru; Yokoyama, Kota; Imabayashi, Etsuko; Watanabe, Masako; Watanabe, Yutaka; Okazaki, Mitsutoshi; Sato, Noriko

2016-09-01

Graph theory is an emerging method to investigate brain networks. Altered cerebral blood flow (CBF) has frequently been reported in temporal lobe epilepsy (TLE), but graph theoretical findings of CBF are poorly understood. Here, we explored graph theoretical networks of CBF in TLE using arterial spin labeling imaging. We recruited patients with TLE and unilateral hippocampal sclerosis (HS) (19 patients with left TLE, and 21 with right TLE) and 20 gender- and age-matched healthy control subjects. We obtained all participants' CBF maps using pseudo-continuous arterial spin labeling and analyzed them using the Graph Analysis Toolbox (GAT) software program. As a result, compared to the controls, the patients with left TLE showed a significantly low clustering coefficient (p=0.024), local efficiency (p=0.001), global efficiency (p=0.010), and high transitivity (p=0.015), whereas the patients with right TLE showed significantly high assortativity (p=0.046) and transitivity (p=0.011). The group with right TLE also had high characteristic path length values (p=0.085), low global efficiency (p=0.078), and low resilience to targeted attack (p=0.101) at a trend level. Lower normalized clustering coefficient (p=0.081) in the left TLE and higher normalized characteristic path length (p=0.089) in the right TLE were found also at a trend level. Both the patients with left and right TLE showed significantly decreased clustering in similar areas, i.e., the cingulate gyri, precuneus, and occipital lobe. Our findings revealed differing left-right network metrics in which an inefficient CBF network in left TLE and vulnerability to irritation in right TLE are suggested. The left-right common finding of regional decreased clustering might reflect impaired default-mode networks in TLE. Copyright © 2016 Elsevier Inc. All rights reserved.

Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network.

PubMed

Li, Shancang; Tryfonas, Theo; Russell, Gordon; Andriotis, Panagiotis

2016-08-01

Mobile systems are facing a number of application vulnerabilities that can be combined together and utilized to penetrate systems with devastating impact. When assessing the overall security of a mobile system, it is important to assess the security risks posed by each mobile applications (apps), thus gaining a stronger understanding of any vulnerabilities present. This paper aims at developing a three-layer framework that assesses the potential risks which apps introduce within the Android mobile systems. A Bayesian risk graphical model is proposed to evaluate risk propagation in a layered risk architecture. By integrating static analysis, dynamic analysis, and behavior analysis in a hierarchical framework, the risks and their propagation through each layer are well modeled by the Bayesian risk graph, which can quantitatively analyze risks faced to both apps and mobile systems. The proposed hierarchical Bayesian risk graph model offers a novel way to investigate the security risks in mobile environment and enables users and administrators to evaluate the potential risks. This strategy allows to strengthen both app security as well as the security of the entire system.

Computing the Edge-Neighbour-Scattering Number of Graphs

NASA Astrophysics Data System (ADS)

Wei, Zongtian; Qi, Nannan; Yue, Xiaokui

2013-11-01

A set of edges X is subverted from a graph G by removing the closed neighbourhood N[X] from G. We denote the survival subgraph by G=X. An edge-subversion strategy X is called an edge-cut strategy of G if G=X is disconnected, a single vertex, or empty. The edge-neighbour-scattering number of a graph G is defined as ENS(G) = max{ω(G/X)-|X| : X is an edge-cut strategy of G}, where w(G=X) is the number of components of G=X. This parameter can be used to measure the vulnerability of networks when some edges are failed, especially spy networks and virus-infected networks. In this paper, we prove that the problem of computing the edge-neighbour-scattering number of a graph is NP-complete and give some upper and lower bounds for this parameter.

PDB2Graph: A toolbox for identifying critical amino acids map in proteins based on graph theory.

PubMed

Niknam, Niloofar; Khakzad, Hamed; Arab, Seyed Shahriar; Naderi-Manesh, Hossein

2016-05-01

The integrative and cooperative nature of protein structure involves the assessment of topological and global features of constituent parts. Network concept takes complete advantage of both of these properties in the analysis concomitantly. High compatibility to structural concepts or physicochemical properties in addition to exploiting a remarkable simplification in the system has made network an ideal tool to explore biological systems. There are numerous examples in which different protein structural and functional characteristics have been clarified by the network approach. Here, we present an interactive and user-friendly Matlab-based toolbox, PDB2Graph, devoted to protein structure network construction, visualization, and analysis. Moreover, PDB2Graph is an appropriate tool for identifying critical nodes involved in protein structural robustness and function based on centrality indices. It maps critical amino acids in protein networks and can greatly aid structural biologists in selecting proper amino acid candidates for manipulating protein structures in a more reasonable and rational manner. To introduce the capability and efficiency of PDB2Graph in detail, the structural modification of Calmodulin through allosteric binding of Ca(2+) is considered. In addition, a mutational analysis for three well-identified model proteins including Phage T4 lysozyme, Barnase and Ribonuclease HI, was performed to inspect the influence of mutating important central residues on protein activity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Massive Scale Cyber Traffic Analysis: A Driver for Graph Database Research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joslyn, Cliff A.; Choudhury, S.; Haglin, David J.

2013-06-19

We describe the significance and prominence of network traffic analysis (TA) as a graph- and network-theoretical domain for advancing research in graph database systems. TA involves observing and analyzing the connections between clients, servers, hosts, and actors within IP networks, both at particular times and as extended over times. Towards that end, NetFlow (or more generically, IPFLOW) data are available from routers and servers which summarize coherent groups of IP packets flowing through the network. IPFLOW databases are routinely interrogated statistically and visualized for suspicious patterns. But the ability to cast IPFLOW data as a massive graph and query itmore » interactively, in order to e.g.\\ identify connectivity patterns, is less well advanced, due to a number of factors including scaling, and their hybrid nature combining graph connectivity and quantitative attributes. In this paper, we outline requirements and opportunities for graph-structured IPFLOW analytics based on our experience with real IPFLOW databases. Specifically, we describe real use cases from the security domain, cast them as graph patterns, show how to express them in two graph-oriented query languages SPARQL and Datalog, and use these examples to motivate a new class of "hybrid" graph-relational systems.« less

A method for independent component graph analysis of resting-state fMRI.

PubMed

Ribeiro de Paula, Demetrius; Ziegler, Erik; Abeyasinghe, Pubuditha M; Das, Tushar K; Cavaliere, Carlo; Aiello, Marco; Heine, Lizette; di Perri, Carol; Demertzi, Athena; Noirhomme, Quentin; Charland-Verville, Vanessa; Vanhaudenhuyse, Audrey; Stender, Johan; Gomez, Francisco; Tshibanda, Jean-Flory L; Laureys, Steven; Owen, Adrian M; Soddu, Andrea

2017-03-01

Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data. Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory. First, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network. Network graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness. This novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.

The Effects of Observation Errors on the Attack Vulnerability of Complex Networks

DTIC Science & Technology

2012-11-01

more detail, to construct a true network we select a topology (erdos- renyi (Erdos & Renyi , 1959), scale-free (Barabási & Albert, 1999), small world...Efficiency of Scale-Free Networks: Error and Attack Tolerance. Physica A, Volume 320, pp. 622-642. 6. Erdos, P. & Renyi , A., 1959. On Random Graphs, I

Preserving Differential Privacy in Degree-Correlation based Graph Generation

PubMed Central

Wang, Yue; Wu, Xintao

2014-01-01

Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as cluster coefficient often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. In this paper, we study the problem of enforcing edge differential privacy in graph generation. The idea is to enforce differential privacy on graph model parameters learned from the original network and then generate the graphs for releasing using the graph model with the private parameters. In particular, we develop a differential privacy preserving graph generator based on the dK-graph generation model. We first derive from the original graph various parameters (i.e., degree correlations) used in the dK-graph model, then enforce edge differential privacy on the learned parameters, and finally use the dK-graph model with the perturbed parameters to generate graphs. For the 2K-graph model, we enforce the edge differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We conduct experiments on four real networks and compare the performance of our private dK-graph models with the stochastic Kronecker graph generation model in terms of utility and privacy tradeoff. Empirical evaluations show the developed private dK-graph generation models significantly outperform the approach based on the stochastic Kronecker generation model. PMID:24723987

Network meta-analysis, electrical networks and graph theory.

PubMed

Rücker, Gerta

2012-12-01

Network meta-analysis is an active field of research in clinical biostatistics. It aims to combine information from all randomized comparisons among a set of treatments for a given medical condition. We show how graph-theoretical methods can be applied to network meta-analysis. A meta-analytic graph consists of vertices (treatments) and edges (randomized comparisons). We illustrate the correspondence between meta-analytic networks and electrical networks, where variance corresponds to resistance, treatment effects to voltage, and weighted treatment effects to current flows. Based thereon, we then show that graph-theoretical methods that have been routinely applied to electrical networks also work well in network meta-analysis. In more detail, the resulting consistent treatment effects induced in the edges can be estimated via the Moore-Penrose pseudoinverse of the Laplacian matrix. Moreover, the variances of the treatment effects are estimated in analogy to electrical effective resistances. It is shown that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modeling and including multi-armed trials are addressed. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

A user exposure based approach for non-structural road network vulnerability analysis

PubMed Central

Jin, Lei; Wang, Haizhong; Yu, Le; Liu, Lin

2017-01-01

Aiming at the dense urban road network vulnerability without structural negative consequences, this paper proposes a novel non-structural road network vulnerability analysis framework. Three aspects of the framework are mainly described: (i) the rationality of non-structural road network vulnerability, (ii) the metrics for negative consequences accounting for variant road conditions, and (iii) the introduction of a new vulnerability index based on user exposure. Based on the proposed methodology, a case study in the Sioux Falls network which was usually threatened by regular heavy snow during wintertime is detailedly discussed. The vulnerability ranking of links of Sioux Falls network with respect to heavy snow scenario is identified. As a result of non-structural consequences accompanied by conceivable degeneration of network, there are significant increases in generalized travel time costs which are measurements for “emotionally hurt” of topological road network. PMID:29176832

Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory

NASA Astrophysics Data System (ADS)

Wang, Na; Li, Dong; Wang, Qiwen

2012-12-01

The visibility graph approach and complex network theory provide a new insight into time series analysis. The inheritance of the visibility graph from the original time series was further explored in the paper. We found that degree distributions of visibility graphs extracted from Pseudo Brownian Motion series obtained by the Frequency Domain algorithm exhibit exponential behaviors, in which the exponential exponent is a binomial function of the Hurst index inherited in the time series. Our simulations presented that the quantitative relations between the Hurst indexes and the exponents of degree distribution function are different for different series and the visibility graph inherits some important features of the original time series. Further, we convert some quarterly macroeconomic series including the growth rates of value-added of three industry series and the growth rates of Gross Domestic Product series of China to graphs by the visibility algorithm and explore the topological properties of graphs associated from the four macroeconomic series, namely, the degree distribution and correlations, the clustering coefficient, the average path length, and community structure. Based on complex network analysis we find degree distributions of associated networks from the growth rates of value-added of three industry series are almost exponential and the degree distributions of associated networks from the growth rates of GDP series are scale free. We also discussed the assortativity and disassortativity of the four associated networks as they are related to the evolutionary process of the original macroeconomic series. All the constructed networks have “small-world” features. The community structures of associated networks suggest dynamic changes of the original macroeconomic series. We also detected the relationship among government policy changes, community structures of associated networks and macroeconomic dynamics. We find great influences of government policies in China on the changes of dynamics of GDP and the three industries adjustment. The work in our paper provides a new way to understand the dynamics of economic development.

A statistical analysis of UK financial networks

NASA Astrophysics Data System (ADS)

Chu, J.; Nadarajah, S.

2017-04-01

In recent years, with a growing interest in big or large datasets, there has been a rise in the application of large graphs and networks to financial big data. Much of this research has focused on the construction and analysis of the network structure of stock markets, based on the relationships between stock prices. Motivated by Boginski et al. (2005), who studied the characteristics of a network structure of the US stock market, we construct network graphs of the UK stock market using same method. We fit four distributions to the degree density of the vertices from these graphs, the Pareto I, Fréchet, lognormal, and generalised Pareto distributions, and assess the goodness of fit. Our results show that the degree density of the complements of the market graphs, constructed using a negative threshold value close to zero, can be fitted well with the Fréchet and lognormal distributions.

Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia

NASA Astrophysics Data System (ADS)

Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke

2017-05-01

Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.

Multisensory integration processing during olfactory-visual stimulation-An fMRI graph theoretical network analysis.

PubMed

Ripp, Isabelle; Zur Nieden, Anna-Nora; Blankenagel, Sonja; Franzmeier, Nicolai; Lundström, Johan N; Freiherr, Jessica

2018-05-07

In this study, we aimed to understand how whole-brain neural networks compute sensory information integration based on the olfactory and visual system. Task-related functional magnetic resonance imaging (fMRI) data was obtained during unimodal and bimodal sensory stimulation. Based on the identification of multisensory integration processing (MIP) specific hub-like network nodes analyzed with network-based statistics using region-of-interest based connectivity matrices, we conclude the following brain areas to be important for processing the presented bimodal sensory information: right precuneus connected contralaterally to the supramarginal gyrus for memory-related imagery and phonology retrieval, and the left middle occipital gyrus connected ipsilaterally to the inferior frontal gyrus via the inferior fronto-occipital fasciculus including functional aspects of working memory. Applied graph theory for quantification of the resulting complex network topologies indicates a significantly increased global efficiency and clustering coefficient in networks including aspects of MIP reflecting a simultaneous better integration and segregation. Graph theoretical analysis of positive and negative network correlations allowing for inferences about excitatory and inhibitory network architectures revealed-not significant, but very consistent-that MIP-specific neural networks are dominated by inhibitory relationships between brain regions involved in stimulus processing. © 2018 Wiley Periodicals, Inc.

Topological patterns in street networks of self-organized urban settlements

NASA Astrophysics Data System (ADS)

Buhl, J.; Gautrais, J.; Reeves, N.; Solé, R. V.; Valverde, S.; Kuntz, P.; Theraulaz, G.

2006-02-01

Many urban settlements result from a spatially distributed, decentralized building process. Here we analyze the topological patterns of organization of a large collection of such settlements using the approach of complex networks. The global efficiency (based on the inverse of shortest-path lengths), robustness to disconnections and cost (in terms of length) of these graphs is studied and their possible origins analyzed. A wide range of patterns is found, from tree-like settlements (highly vulnerable to random failures) to meshed urban patterns. The latter are shown to be more robust and efficient.

Developing an intelligence analysis process through social network analysis

NASA Astrophysics Data System (ADS)

Waskiewicz, Todd; LaMonica, Peter

2008-04-01

Intelligence analysts are tasked with making sense of enormous amounts of data and gaining an awareness of a situation that can be acted upon. This process can be extremely difficult and time consuming. Trying to differentiate between important pieces of information and extraneous data only complicates the problem. When dealing with data containing entities and relationships, social network analysis (SNA) techniques can be employed to make this job easier. Applying network measures to social network graphs can identify the most significant nodes (entities) and edges (relationships) and help the analyst further focus on key areas of concern. Strange developed a model that identifies high value targets such as centers of gravity and critical vulnerabilities. SNA lends itself to the discovery of these high value targets and the Air Force Research Laboratory (AFRL) has investigated several network measures such as centrality, betweenness, and grouping to identify centers of gravity and critical vulnerabilities. Using these network measures, a process for the intelligence analyst has been developed to aid analysts in identifying points of tactical emphasis. Organizational Risk Analyzer (ORA) and Terrorist Modus Operandi Discovery System (TMODS) are the two applications used to compute the network measures and identify the points to be acted upon. Therefore, the result of leveraging social network analysis techniques and applications will provide the analyst and the intelligence community with more focused and concentrated analysis results allowing them to more easily exploit key attributes of a network, thus saving time, money, and manpower.

Characterizing networks formed by P. polycephalum

NASA Astrophysics Data System (ADS)

Dirnberger, M.; Mehlhorn, K.

2017-06-01

We present a systematic study of the characteristic vein networks formed by the slime mold P. polycephalum. Our study is based on an extensive set of graph representations of slime mold networks. We analyze a total of 1998 graphs capturing growth and network formation of P. polycephalum as observed in 36 independent, identical, wet-lab experiments. Relying on concepts from graph theory such as face cycles and cuts as well as ideas from percolation theory, we establish a broad collection of individual observables taking into account various complementary aspects of P. polycephalum networks. As a whole, the collection is intended to serve as a specialized knowledge-base providing a comprehensive characterization of P. polycephalum networks. To this end, it contains individual as well as cumulative results for all investigated observables across all available data series, down to the level of single P. polycephalum graphs. Furthermore we include the raw numerical data as well as various plotting and analysis tools to ensure reproducibility and increase the usefulness of the collection. All our results are publicly available in an organized fashion in the slime mold graph repository (Smgr).

Identifying the Critical Links in Road Transportation Networks: Centrality-based approach utilizing structural properties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chinthavali, Supriya

Surface transportation road networks share structural properties similar to other complex networks (e.g., social networks, information networks, biological networks, and so on). This research investigates the structural properties of road networks for any possible correlation with the traffic characteristics such as link flows those determined independently. Additionally, we define a criticality index for the links of the road network that identifies the relative importance in the network. We tested our hypotheses with two sample road networks. Results show that, correlation exists between the link flows and centrality measures of a link of the road (dual graph approach is followed) andmore » the criticality index is found to be effective for one test network to identify the vulnerable nodes.« less

A Graph Analytic Metric for Mitigating Advanced Persistent Threat

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, John R.; Hogan, Emilie A.

2013-06-04

This paper introduces a novel graph analytic metric that can be used to measure the potential vulnerability of a cyber network to specific types of attacks that use lateral movement and privilege escalation such as the well known Pass The Hash, (PTH). The metric is computed from an oriented subgraph of the underlying cyber network induced by selecting only those edges for which a given property holds between the two vertices of the edge. The metric with respect to a select node on the subgraph is defined as the likelihood that the select node is reachable from another arbitrary nodemore » in the graph. This metric can be calculated dynamically from the authorization and auditing layers during the network security authorization phase and will potentially enable predictive deterrence against attacks such as PTH.« less

Knowledge Representation Issues in Semantic Graphs for Relationship Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barthelemy, M; Chow, E; Eliassi-Rad, T

2005-02-02

An important task for Homeland Security is the prediction of threat vulnerabilities, such as through the detection of relationships between seemingly disjoint entities. A structure used for this task is a ''semantic graph'', also known as a ''relational data graph'' or an ''attributed relational graph''. These graphs encode relationships as typed links between a pair of typed nodes. Indeed, semantic graphs are very similar to semantic networks used in AI. The node and link types are related through an ontology graph (also known as a schema). Furthermore, each node has a set of attributes associated with it (e.g., ''age'' maymore » be an attribute of a node of type ''person''). Unfortunately, the selection of types and attributes for both nodes and links depends on human expertise and is somewhat subjective and even arbitrary. This subjectiveness introduces biases into any algorithm that operates on semantic graphs. Here, we raise some knowledge representation issues for semantic graphs and provide some possible solutions using recently developed ideas in the field of complex networks. In particular, we use the concept of transitivity to evaluate the relevance of individual links in the semantic graph for detecting relationships. We also propose new statistical measures for semantic graphs and illustrate these semantic measures on graphs constructed from movies and terrorism data.« less

CYBERWAR-2012/13: Siegel 2011 Predicted Cyberwar Via ACHILLES-HEEL DIGITS BEQS BEC ZERO-DIGIT BEC of/in ACHILLES-HEEL DIGITS Log-Law Algebraic-Inversion to ONLY BEQS BEC Digit-Physics U Barabasi Network/Graph-Physics BEQS BEC JAMMING Denial-of-Access(DOA) Attacks 2012-Instantiations

NASA Astrophysics Data System (ADS)

Huffmann, Master; Siegel, Edward Carl-Ludwig

2013-03-01

Newcomb-Benford(NeWBe)-Siegel log-law BEC Digit-Physics Network/Graph-Physics Barabasi et.al. evolving-``complex''-networks/graphs BEC JAMMING DOA attacks: Amazon(weekends: Microsoft I.E.-7/8(vs. Firefox): Memorial-day, Labor-day,...), MANY U.S.-Banks:WF,BoA,UB,UBS,...instantiations AGAIN militate for MANDATORY CONVERSION to PARALLEL ANALOG FAULT-TOLERANT but slow(er) SECURITY-ASSURANCE networks/graphs in parallel with faster ``sexy'' DIGITAL-Networks/graphs:``Cloud'', telecomm: n-G,..., because of common ACHILLES-HEEL VULNERABILITY: DIGITS!!! ``In fast-hare versus slow-tortoise race, Slow-But-Steady ALWAYS WINS!!!'' (Zeno). {Euler [#s(1732)] ∑- ∏()-Riemann[Monats. Akad. Berlin (1859)] ∑- ∏()- Kummer-Bernoulli (#s)}-Newcomb [Am.J.Math.4(1),39 (81) discovery of the QUANTUM!!!]-{Planck (01)]}-{Einstein (05)]-Poincar e [Calcul Probabilités,313(12)]-Weyl[Goett. Nach.(14); Math.Ann.77,313(16)]-(Bose (24)-Einstein(25)]-VS. -Fermi (27)-Dirac(27))-Menger [Dimensiontheorie(29)]-Benford [J.Am. Phil.Soc.78,115(38)]-Kac[Maths Stats.-Reason. (55)]- Raimi [Sci.Am.221,109(69)]-Jech-Hill [Proc.AMS,123,3,887(95)] log-function

Network-based machine learning and graph theory algorithms for precision oncology.

PubMed

Zhang, Wei; Chien, Jeremy; Yong, Jeongsik; Kuang, Rui

2017-01-01

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

Analyzing Cyber Security Threats on Cyber-Physical Systems Using Model-Based Systems Engineering

NASA Technical Reports Server (NTRS)

Kerzhner, Aleksandr; Pomerantz, Marc; Tan, Kymie; Campuzano, Brian; Dinkel, Kevin; Pecharich, Jeremy; Nguyen, Viet; Steele, Robert; Johnson, Bryan

2015-01-01

The spectre of cyber attacks on aerospace systems can no longer be ignored given that many of the components and vulnerabilities that have been successfully exploited by the adversary on other infrastructures are the same as those deployed and used within the aerospace environment. An important consideration with respect to the mission/safety critical infrastructure supporting space operations is that an appropriate defensive response to an attack invariably involves the need for high precision and accuracy, because an incorrect response can trigger unacceptable losses involving lives and/or significant financial damage. A highly precise defensive response, considering the typical complexity of aerospace environments, requires a detailed and well-founded understanding of the underlying system where the goal of the defensive response is to preserve critical mission objectives in the presence of adversarial activity. In this paper, a structured approach for modeling aerospace systems is described. The approach includes physical elements, network topology, software applications, system functions, and usage scenarios. We leverage Model-Based Systems Engineering methodology by utilizing the Object Management Group's Systems Modeling Language to represent the system being analyzed and also utilize model transformations to change relevant aspects of the model into specialized analyses. A novel visualization approach is utilized to visualize the entire model as a three-dimensional graph, allowing easier interaction with subject matter experts. The model provides a unifying structure for analyzing the impact of a particular attack or a particular type of attack. Two different example analysis types are demonstrated in this paper: a graph-based propagation analysis based on edge labels, and a graph-based propagation analysis based on node labels.

Comparison analysis on vulnerability of metro networks based on complex network

NASA Astrophysics Data System (ADS)

Zhang, Jianhua; Wang, Shuliang; Wang, Xiaoyuan

2018-04-01

This paper analyzes the networked characteristics of three metro networks, and two malicious attacks are employed to investigate the vulnerability of metro networks based on connectivity vulnerability and functionality vulnerability. Meanwhile, the networked characteristics and vulnerability of three metro networks are compared with each other. The results show that Shanghai metro network has the largest transport capacity, Beijing metro network has the best local connectivity and Guangzhou metro network has the best global connectivity, moreover Beijing metro network has the best homogeneous degree distribution. Furthermore, we find that metro networks are very vulnerable subjected to malicious attacks, and Guangzhou metro network has the best topological structure and reliability among three metro networks. The results indicate that the proposed methodology is feasible and effective to investigate the vulnerability and to explore better topological structure of metro networks.

A graph-theory framework for evaluating landscape connectivity and conservation planning.

PubMed

Minor, Emily S; Urban, Dean L

2008-04-01

Connectivity of habitat patches is thought to be important for movement of genes, individuals, populations, and species over multiple temporal and spatial scales. We used graph theory to characterize multiple aspects of landscape connectivity in a habitat network in the North Carolina Piedmont (U.S.A). We compared this landscape with simulated networks with known topology, resistance to disturbance, and rate of movement. We introduced graph measures such as compartmentalization and clustering, which can be used to identify locations on the landscape that may be especially resilient to human development or areas that may be most suitable for conservation. Our analyses indicated that for songbirds the Piedmont habitat network was well connected. Furthermore, the habitat network had commonalities with planar networks, which exhibit slow movement, and scale-free networks, which are resistant to random disturbances. These results suggest that connectivity in the habitat network was high enough to prevent the negative consequences of isolation but not so high as to allow rapid spread of disease. Our graph-theory framework provided insight into regional and emergent global network properties in an intuitive and visual way and allowed us to make inferences about rates and paths of species movements and vulnerability to disturbance. This approach can be applied easily to assessing habitat connectivity in any fragmented or patchy landscape.

Graph Theory-Based Pinning Synchronization of Stochastic Complex Dynamical Networks.

PubMed

Li, Xiao-Jian; Yang, Guang-Hong

2017-02-01

This paper is concerned with the adaptive pinning synchronization problem of stochastic complex dynamical networks (CDNs). Based on algebraic graph theory and Lyapunov theory, pinning controller design conditions are derived, and the rigorous convergence analysis of synchronization errors in the probability sense is also conducted. Compared with the existing results, the topology structures of stochastic CDN are allowed to be unknown due to the use of graph theory. In particular, it is shown that the selection of nodes for pinning depends on the unknown lower bounds of coupling strengths. Finally, an example on a Chua's circuit network is given to validate the effectiveness of the theoretical results.

The Use of Weighted Graphs for Large-Scale Genome Analysis

PubMed Central

Zhou, Fang; Toivonen, Hannu; King, Ross D.

2014-01-01

There is an acute need for better tools to extract knowledge from the growing flood of sequence data. For example, thousands of complete genomes have been sequenced, and their metabolic networks inferred. Such data should enable a better understanding of evolution. However, most existing network analysis methods are based on pair-wise comparisons, and these do not scale to thousands of genomes. Here we propose the use of weighted graphs as a data structure to enable large-scale phylogenetic analysis of networks. We have developed three types of weighted graph for enzymes: taxonomic (these summarize phylogenetic importance), isoenzymatic (these summarize enzymatic variety/redundancy), and sequence-similarity (these summarize sequence conservation); and we applied these types of weighted graph to survey prokaryotic metabolism. To demonstrate the utility of this approach we have compared and contrasted the large-scale evolution of metabolism in Archaea and Eubacteria. Our results provide evidence for limits to the contingency of evolution. PMID:24619061

Network Analysis Tools: from biological networks to clusters and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Vanderstocken, Gilles; van Helden, Jacques

2008-01-01

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.

Shock waves on complex networks

NASA Astrophysics Data System (ADS)

Mones, Enys; Araújo, Nuno A. M.; Vicsek, Tamás; Herrmann, Hans J.

2014-05-01

Power grids, road maps, and river streams are examples of infrastructural networks which are highly vulnerable to external perturbations. An abrupt local change of load (voltage, traffic density, or water level) might propagate in a cascading way and affect a significant fraction of the network. Almost discontinuous perturbations can be modeled by shock waves which can eventually interfere constructively and endanger the normal functionality of the infrastructure. We study their dynamics by solving the Burgers equation under random perturbations on several real and artificial directed graphs. Even for graphs with a narrow distribution of node properties (e.g., degree or betweenness), a steady state is reached exhibiting a heterogeneous load distribution, having a difference of one order of magnitude between the highest and average loads. Unexpectedly we find for the European power grid and for finite Watts-Strogatz networks a broad pronounced bimodal distribution for the loads. To identify the most vulnerable nodes, we introduce the concept of node-basin size, a purely topological property which we show to be strongly correlated to the average load of a node.

Information jet: Handling noisy big data from weakly disconnected network

NASA Astrophysics Data System (ADS)

Aurongzeb, Deeder

Sudden aggregation (information jet) of large amount of data is ubiquitous around connected social networks, driven by sudden interacting and non-interacting events, network security threat attacks, online sales channel etc. Clustering of information jet based on time series analysis and graph theory is not new but little work is done to connect them with particle jet statistics. We show pre-clustering based on context can element soft network or network of information which is critical to minimize time to calculate results from noisy big data. We show difference between, stochastic gradient boosting and time series-graph clustering. For disconnected higher dimensional information jet, we use Kallenberg representation theorem (Kallenberg, 2005, arXiv:1401.1137) to identify and eliminate jet similarities from dense or sparse graph.

Discrete Mathematical Approaches to Graph-Based Traffic Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joslyn, Cliff A.; Cowley, Wendy E.; Hogan, Emilie A.

2014-04-01

Modern cyber defense and anlaytics requires general, formal models of cyber systems. Multi-scale network models are prime candidates for such formalisms, using discrete mathematical methods based in hierarchically-structured directed multigraphs which also include rich sets of labels. An exemplar of an application of such an approach is traffic analysis, that is, observing and analyzing connections between clients, servers, hosts, and actors within IP networks, over time, to identify characteristic or suspicious patterns. Towards that end, NetFlow (or more generically, IPFLOW) data are available from routers and servers which summarize coherent groups of IP packets flowing through the network. In thismore » paper, we consider traffic analysis of Netflow using both basic graph statistics and two new mathematical measures involving labeled degree distributions and time interval overlap measures. We do all of this over the VAST test data set of 96M synthetic Netflow graph edges, against which we can identify characteristic patterns of simulated ground-truth network attacks.« less

Vulnerabilities, Influences and Interaction Paths: Failure Data for Integrated System Risk Analysis

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Fleming, Land

2006-01-01

We describe graph-based analysis methods for identifying and analyzing cross-subsystem interaction risks from subsystem connectivity information. By discovering external and remote influences that would be otherwise unexpected, these methods can support better communication among subsystem designers at points of potential conflict and to support design of more dependable and diagnosable systems. These methods identify hazard causes that can impact vulnerable functions or entities if propagated across interaction paths from the hazard source to the vulnerable target. The analysis can also assess combined impacts of And-Or trees of disabling influences. The analysis can use ratings of hazards and vulnerabilities to calculate cumulative measures of the severity and importance. Identification of cross-subsystem hazard-vulnerability pairs and propagation paths across subsystems will increase coverage of hazard and risk analysis and can indicate risk control and protection strategies.

Observer-based consensus of networked thrust-propelled vehicles with directed graphs.

PubMed

Cang, Weiye; Li, Zhongkui; Wang, Hanlei

2017-11-01

In this paper, we investigate the consensus problem for networked underactuated thrust-propelled vehicles (TPVs) interacting on directed graphs. We propose distributed observer-based consensus protocols, which avoid the reliance on the measurements of translational velocities and accelerations. Using the input-output analysis, we present necessary and sufficient conditions to ensure that the observer-based protocols can achieve consensus for both the cases without and with constant communication delays, provided that the communication graph contains a directed spanning tree. Simulation examples are finally provided to illustrate the effectiveness of the control schemes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

AVQS: attack route-based vulnerability quantification scheme for smart grid.

PubMed

Ko, Jongbin; Lim, Hyunwoo; Lee, Seokjun; Shon, Taeshik

2014-01-01

A smart grid is a large, consolidated electrical grid system that includes heterogeneous networks and systems. Based on the data, a smart grid system has a potential security threat in its network connectivity. To solve this problem, we develop and apply a novel scheme to measure the vulnerability in a smart grid domain. Vulnerability quantification can be the first step in security analysis because it can help prioritize the security problems. However, existing vulnerability quantification schemes are not suitable for smart grid because they do not consider network vulnerabilities. We propose a novel attack route-based vulnerability quantification scheme using a network vulnerability score and an end-to-end security score, depending on the specific smart grid network environment to calculate the vulnerability score for a particular attack route. To evaluate the proposed approach, we derive several attack scenarios from the advanced metering infrastructure domain. The experimental results of the proposed approach and the existing common vulnerability scoring system clearly show that we need to consider network connectivity for more optimized vulnerability quantification.

Bipartite graphs as models of population structures in evolutionary multiplayer games.

PubMed

Peña, Jorge; Rochat, Yannick

2012-01-01

By combining evolutionary game theory and graph theory, "games on graphs" study the evolutionary dynamics of frequency-dependent selection in population structures modeled as geographical or social networks. Networks are usually represented by means of unipartite graphs, and social interactions by two-person games such as the famous prisoner's dilemma. Unipartite graphs have also been used for modeling interactions going beyond pairwise interactions. In this paper, we argue that bipartite graphs are a better alternative to unipartite graphs for describing population structures in evolutionary multiplayer games. To illustrate this point, we make use of bipartite graphs to investigate, by means of computer simulations, the evolution of cooperation under the conventional and the distributed N-person prisoner's dilemma. We show that several implicit assumptions arising from the standard approach based on unipartite graphs (such as the definition of replacement neighborhoods, the intertwining of individual and group diversity, and the large overlap of interaction neighborhoods) can have a large impact on the resulting evolutionary dynamics. Our work provides a clear example of the importance of construction procedures in games on graphs, of the suitability of bigraphs and hypergraphs for computational modeling, and of the importance of concepts from social network analysis such as centrality, centralization and bipartite clustering for the understanding of dynamical processes occurring on networked population structures.

NEFI: Network Extraction From Images

PubMed Central

Dirnberger, M.; Kehl, T.; Neumann, A.

2015-01-01

Networks are amongst the central building blocks of many systems. Given a graph of a network, methods from graph theory enable a precise investigation of its properties. Software for the analysis of graphs is widely available and has been applied to study various types of networks. In some applications, graph acquisition is relatively simple. However, for many networks data collection relies on images where graph extraction requires domain-specific solutions. Here we introduce NEFI, a tool that extracts graphs from images of networks originating in various domains. Regarding previous work on graph extraction, theoretical results are fully accessible only to an expert audience and ready-to-use implementations for non-experts are rarely available or insufficiently documented. NEFI provides a novel platform allowing practitioners to easily extract graphs from images by combining basic tools from image processing, computer vision and graph theory. Thus, NEFI constitutes an alternative to tedious manual graph extraction and special purpose tools. We anticipate NEFI to enable time-efficient collection of large datasets. The analysis of these novel datasets may open up the possibility to gain new insights into the structure and function of various networks. NEFI is open source and available at http://nefi.mpi-inf.mpg.de. PMID:26521675

A new holistic approach to vulnerability assessment to natural hazards of socio-economic systems by means of Graph Theory

NASA Astrophysics Data System (ADS)

Arosio, Marcello; Martina, Mario L. V.

2017-04-01

In the last years, the relations and interactions between multi-hazards, vulnerability, exposure and resilience spheres are assuming more and more attention and the scientific community recognized that they are very dynamic, complex and interconnected. The traditional approaches define risk as the potential economic, social and environmental consequences due to a hazardous phenomenon in a specific period. Although there have been major improvements in recent years, there are still some limitation in term of a holistic approach that is able to include the emergent value hidden in the relation and interaction between the different spheres. Furthermore, the emergent behaviour of a society makes the collective risk greater than the sum of the parts and this requires a holistic, systematic and integrated approach. For this reason, it is important to consider the connections between elements to assess properly the vulnerability of systems. In a system (e.g. road, hospital and ecological network, etc.), or in a System of System (e.g. socio-technical urban service), there are critical elements that, beyond the intrinsic vulnerability, can be characterize by greater or lower vulnerability because of their physical, geographical, cyber or logical connections. To understand the system response to a perturbation, and therefore its resilience, is necessary not only to represent but also to quantify the relative importance of the elements and their interconnections. To this aim, we propose an innovative approach in the field of natural risk assessment based on the properties of graph G=(N,L). A graph consists of two sets N (nodes) and L (links): the nodes represent the single exposed elements (physical, social, environmental, etc.) to a hazard, while the links (or connections) represent the interaction between the elements. This approach encourages the risk assessment to a new prospective: from reductionist to holistic. The final goal is to provide insight in understanding how to quantify integrated collective vulnerability, resilience and risk.

Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

PubMed

Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

2017-01-01

Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks.

PubMed

Laniau, Julie; Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien; Siegel, Anne

2017-01-01

The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests , which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks

PubMed Central

Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien

2017-01-01

Background The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. Results We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. Conclusion The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype. PMID:29038751

Robustness of Synchrony in Complex Networks and Generalized Kirchhoff Indices

NASA Astrophysics Data System (ADS)

Tyloo, M.; Coletta, T.; Jacquod, Ph.

2018-02-01

In network theory, a question of prime importance is how to assess network vulnerability in a fast and reliable manner. With this issue in mind, we investigate the response to external perturbations of coupled dynamical systems on complex networks. We find that for specific, nonaveraged perturbations, the response of synchronous states depends on the eigenvalues of the stability matrix of the unperturbed dynamics, as well as on its eigenmodes via their overlap with the perturbation vector. Once averaged over properly defined ensembles of perturbations, the response is given by new graph topological indices, which we introduce as generalized Kirchhoff indices. These findings allow for a fast and reliable method for assessing the specific or average vulnerability of a network against changing operational conditions, faults, or external attacks.

Graph Theory-Based Analysis of the Lymph Node Fibroblastic Reticular Cell Network.

PubMed

Novkovic, Mario; Onder, Lucas; Bocharov, Gennady; Ludewig, Burkhard

2017-01-01

Secondary lymphoid organs have developed segregated niches that are able to initiate and maintain effective immune responses. Such global organization requires tight control of diverse cellular components, specifically those that regulate lymphocyte trafficking. Fibroblastic reticular cells (FRCs) form a densely interconnected network in lymph nodes and provide key factors necessary for T cell migration and retention, and foster subsequent interactions between T cells and dendritic cells. Development of integrative systems biology approaches has made it possible to elucidate this multilevel complexity of the immune system. Here, we present a graph theory-based analysis of the FRC network in murine lymph nodes, where generation of the network topology is performed using high-resolution confocal microscopy and 3D reconstruction. This approach facilitates the analysis of physical cell-to-cell connectivity, and estimation of topological robustness and global behavior of the network when it is subjected to perturbation in silico.

AVQS: Attack Route-Based Vulnerability Quantification Scheme for Smart Grid

PubMed Central

Lim, Hyunwoo; Lee, Seokjun; Shon, Taeshik

2014-01-01

A smart grid is a large, consolidated electrical grid system that includes heterogeneous networks and systems. Based on the data, a smart grid system has a potential security threat in its network connectivity. To solve this problem, we develop and apply a novel scheme to measure the vulnerability in a smart grid domain. Vulnerability quantification can be the first step in security analysis because it can help prioritize the security problems. However, existing vulnerability quantification schemes are not suitable for smart grid because they do not consider network vulnerabilities. We propose a novel attack route-based vulnerability quantification scheme using a network vulnerability score and an end-to-end security score, depending on the specific smart grid network environment to calculate the vulnerability score for a particular attack route. To evaluate the proposed approach, we derive several attack scenarios from the advanced metering infrastructure domain. The experimental results of the proposed approach and the existing common vulnerability scoring system clearly show that we need to consider network connectivity for more optimized vulnerability quantification. PMID:25152923

Neurodevelopmental alterations of large-scale structural networks in children with new-onset epilepsy

PubMed Central

Bonilha, Leonardo; Tabesh, Ali; Dabbs, Kevin; Hsu, David A.; Stafstrom, Carl E.; Hermann, Bruce P.; Lin, Jack J.

2014-01-01

Recent neuroimaging and behavioral studies have revealed that children with new onset epilepsy already exhibit brain structural abnormalities and cognitive impairment. How the organization of large-scale brain structural networks is altered near the time of seizure onset and whether network changes are related to cognitive performances remain unclear. Recent studies also suggest that regional brain volume covariance reflects synchronized brain developmental changes. Here, we test the hypothesis that epilepsy during early-life is associated with abnormalities in brain network organization and cognition. We used graph theory to study structural brain networks based on regional volume covariance in 39 children with new-onset seizures and 28 healthy controls. Children with new-onset epilepsy showed a suboptimal topological structural organization with enhanced network segregation and reduced global integration compared to controls. At the regional level, structural reorganization was evident with redistributed nodes from the posterior to more anterior head regions. The epileptic brain network was more vulnerable to targeted but not random attacks. Finally, a subgroup of children with epilepsy, namely those with lower IQ and poorer executive function, had a reduced balance between network segregation and integration. Taken together, the findings suggest that the neurodevelopmental impact of new onset childhood epilepsies alters large-scale brain networks, resulting in greater vulnerability to network failure and cognitive impairment. PMID:24453089

Connectivity: Performance Portable Algorithms for graph connectivity v. 0.1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Slota, George; Rajamanickam, Sivasankaran; Madduri, Kamesh

Graphs occur in several places in real world from road networks, social networks and scientific simulations. Connectivity is a graph analysis software to graph connectivity in modern architectures like multicore CPUs, Xeon Phi and GPUs.

Network-Based Visual Analysis of Tabular Data

ERIC Educational Resources Information Center

Liu, Zhicheng

2012-01-01

Tabular data is pervasive in the form of spreadsheets and relational databases. Although tables often describe multivariate data without explicit network semantics, it may be advantageous to explore the data modeled as a graph or network for analysis. Even when a given table design conveys some static network semantics, analysts may want to look…

Social capital calculations in economic systems: Experimental study

NASA Astrophysics Data System (ADS)

Chepurov, E. G.; Berg, D. B.; Zvereva, O. M.; Nazarova, Yu. Yu.; Chekmarev, I. V.

2017-11-01

The paper describes the social capital study for a system where actors are engaged in an economic activity. The focus is on the analysis of communications structural parameters (transactions) between the actors. Comparison between transaction network graph structure and the structure of a random Bernoulli graph of the same dimension and density allows revealing specific structural features of the economic system under study. Structural analysis is based on SNA-methodology (SNA - Social Network Analysis). It is shown that structural parameter values of the graph formed by agent relationship links may well characterize different aspects of the social capital structure. The research advocates that it is useful to distinguish the difference between each agent social capital and the whole system social capital.

Network analysis for the visualization and analysis of qualitative data.

PubMed

Pokorny, Jennifer J; Norman, Alex; Zanesco, Anthony P; Bauer-Wu, Susan; Sahdra, Baljinder K; Saron, Clifford D

2018-03-01

We present a novel manner in which to visualize the coding of qualitative data that enables representation and analysis of connections between codes using graph theory and network analysis. Network graphs are created from codes applied to a transcript or audio file using the code names and their chronological location. The resulting network is a representation of the coding data that characterizes the interrelations of codes. This approach enables quantification of qualitative codes using network analysis and facilitates examination of associations of network indices with other quantitative variables using common statistical procedures. Here, as a proof of concept, we applied this method to a set of interview transcripts that had been coded in 2 different ways and the resultant network graphs were examined. The creation of network graphs allows researchers an opportunity to view and share their qualitative data in an innovative way that may provide new insights and enhance transparency of the analytical process by which they reach their conclusions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Graph-based network analysis of resting-state functional MRI.

PubMed

Wang, Jinhui; Zuo, Xinian; He, Yong

2010-01-01

In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain's spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain's intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future.

Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks

PubMed Central

Meyer-Bäse, Anke; Roberts, Rodney G.; Illan, Ignacio A.; Meyer-Bäse, Uwe; Lobbes, Marc; Stadlbauer, Andreas; Pinker-Domenig, Katja

2017-01-01

Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters) representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and derive the necessary conditions for (1) area aggregation and time-scale modeling in brain networks and for (2) pinning observability of nodes in dynamic graph networks. Simulation examples are given to illustrate the theoretical concepts. PMID:29051730

Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks.

PubMed

Meyer-Bäse, Anke; Roberts, Rodney G; Illan, Ignacio A; Meyer-Bäse, Uwe; Lobbes, Marc; Stadlbauer, Andreas; Pinker-Domenig, Katja

2017-01-01

Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters) representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and derive the necessary conditions for (1) area aggregation and time-scale modeling in brain networks and for (2) pinning observability of nodes in dynamic graph networks. Simulation examples are given to illustrate the theoretical concepts.

Semantic super networks: A case analysis of Wikipedia papers

NASA Astrophysics Data System (ADS)

Kostyuchenko, Evgeny; Lebedeva, Taisiya; Goritov, Alexander

2017-11-01

An algorithm for constructing super-large semantic networks has been developed in current work. Algorithm was tested using the "Cosmos" category of the Internet encyclopedia "Wikipedia" as an example. During the implementation, a parser for the syntax analysis of Wikipedia pages was developed. A graph based on list of articles and categories was formed. On the basis of the obtained graph analysis, algorithms for finding domains of high connectivity in a graph were proposed and tested. Algorithms for constructing a domain based on the number of links and the number of articles in the current subject area is considered. The shortcomings of these algorithms are shown and explained, an algorithm is developed on their joint use. The possibility of applying a combined algorithm for obtaining the final domain is shown. The problem of instability of the received domain was discovered when starting an algorithm from two neighboring vertices related to the domain.

Brain Graph Topology Changes Associated with Anti-Epileptic Drug Use

PubMed Central

Levin, Harvey S.; Chiang, Sharon

2015-01-01

Abstract Neuroimaging studies of functional connectivity using graph theory have furthered our understanding of the network structure in temporal lobe epilepsy (TLE). Brain network effects of anti-epileptic drugs could influence such studies, but have not been systematically studied. Resting-state functional MRI was analyzed in 25 patients with TLE using graph theory analysis. Patients were divided into two groups based on anti-epileptic medication use: those taking carbamazepine/oxcarbazepine (CBZ/OXC) (n=9) and those not taking CBZ/OXC (n=16) as a part of their medication regimen. The following graph topology metrics were analyzed: global efficiency, betweenness centrality (BC), clustering coefficient, and small-world index. Multiple linear regression was used to examine the association of CBZ/OXC with graph topology. The two groups did not differ from each other based on epilepsy characteristics. Use of CBZ/OXC was associated with a lower BC. Longer epilepsy duration was also associated with a lower BC. These findings can inform graph theory-based studies in patients with TLE. The changes observed are discussed in relation to the anti-epileptic mechanism of action and adverse effects of CBZ/OXC. PMID:25492633

VTAC: virtual terrain assisted impact assessment for cyber attacks

NASA Astrophysics Data System (ADS)

Argauer, Brian J.; Yang, Shanchieh J.

2008-03-01

Overwhelming intrusion alerts have made timely response to network security breaches a difficult task. Correlating alerts to produce a higher level view of intrusion state of a network, thus, becomes an essential element in network defense. This work proposes to analyze correlated or grouped alerts and determine their 'impact' to services and users of the network. A network is modeled as 'virtual terrain' where cyber attacks maneuver. Overlaying correlated attack tracks on virtual terrain exhibits the vulnerabilities exploited by each track and the relationships between them and different network entities. The proposed impact assessment algorithm utilizes the graph-based virtual terrain model and combines assessments of damages caused by the attacks. The combined impact scores allow to identify severely damaged network services and affected users. Several scenarios are examined to demonstrate the uses of the proposed Virtual Terrain Assisted Impact Assessment for Cyber Attacks (VTAC).

Multiple perspective vulnerability analysis of the power network

NASA Astrophysics Data System (ADS)

Wang, Shuliang; Zhang, Jianhua; Duan, Na

2018-02-01

To understand the vulnerability of the power network from multiple perspectives, multi-angle and multi-dimensional vulnerability analysis as well as community based vulnerability analysis are proposed in this paper. Taking into account of central China power grid as an example, correlation analysis of different vulnerability models is discussed. Then, vulnerabilities produced by different vulnerability metrics under the given vulnerability models and failure scenarios are analyzed. At last, applying the community detecting approach, critical areas of central China power grid are identified, Vulnerable and robust communities on both topological and functional perspective are acquired and analyzed. The approach introduced in this paper can be used to help decision makers develop optimal protection strategies. It will be also useful to give a multiple vulnerability analysis of the other infrastructure systems.

Sparse dictionary learning for resting-state fMRI analysis

NASA Astrophysics Data System (ADS)

Lee, Kangjoo; Han, Paul Kyu; Ye, Jong Chul

2011-09-01

Recently, there has been increased interest in the usage of neuroimaging techniques to investigate what happens in the brain at rest. Functional imaging studies have revealed that the default-mode network activity is disrupted in Alzheimer's disease (AD). However, there is no consensus, as yet, on the choice of analysis method for the application of resting-state analysis for disease classification. This paper proposes a novel compressed sensing based resting-state fMRI analysis tool called Sparse-SPM. As the brain's functional systems has shown to have features of complex networks according to graph theoretical analysis, we apply a graph model to represent a sparse combination of information flows in complex network perspectives. In particular, a new concept of spatially adaptive design matrix has been proposed by implementing sparse dictionary learning based on sparsity. The proposed approach shows better performance compared to other conventional methods, such as independent component analysis (ICA) and seed-based approach, in classifying the AD patients from normal using resting-state analysis.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hagberg, Aric; Swart, Pieter; S Chult, Daniel

NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility mades NetworkX ideal for representing networks found in many different scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distributionmore » and many more. NetworkX can read and write various graph formats for eash exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdoes-Renyi, Small World, and Barabasi-Albert models, are included. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks.« less

Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games

PubMed Central

Peña, Jorge; Rochat, Yannick

2012-01-01

By combining evolutionary game theory and graph theory, “games on graphs” study the evolutionary dynamics of frequency-dependent selection in population structures modeled as geographical or social networks. Networks are usually represented by means of unipartite graphs, and social interactions by two-person games such as the famous prisoner’s dilemma. Unipartite graphs have also been used for modeling interactions going beyond pairwise interactions. In this paper, we argue that bipartite graphs are a better alternative to unipartite graphs for describing population structures in evolutionary multiplayer games. To illustrate this point, we make use of bipartite graphs to investigate, by means of computer simulations, the evolution of cooperation under the conventional and the distributed N-person prisoner’s dilemma. We show that several implicit assumptions arising from the standard approach based on unipartite graphs (such as the definition of replacement neighborhoods, the intertwining of individual and group diversity, and the large overlap of interaction neighborhoods) can have a large impact on the resulting evolutionary dynamics. Our work provides a clear example of the importance of construction procedures in games on graphs, of the suitability of bigraphs and hypergraphs for computational modeling, and of the importance of concepts from social network analysis such as centrality, centralization and bipartite clustering for the understanding of dynamical processes occurring on networked population structures. PMID:22970237

Graph Frequency Analysis of Brain Signals

PubMed Central

Huang, Weiyu; Goldsberry, Leah; Wymbs, Nicholas F.; Grafton, Scott T.; Bassett, Danielle S.; Ribeiro, Alejandro

2016-01-01

This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations. We relate graph frequency with principal component analysis when the networks of interest denote functional connectivity. The methods are utilized to analyze brain networks and signals as subjects master a simple motor skill. We observe that brain signals corresponding to different graph frequencies exhibit different levels of adaptability throughout learning. Further, we notice a strong association between graph spectral properties of brain networks and the level of exposure to tasks performed, and recognize the most contributing and important frequency signatures at different levels of task familiarity. PMID:28439325

Network representation of protein interactions: Theory of graph description and analysis.

PubMed

Kurzbach, Dennis

2016-09-01

A methodological framework is presented for the graph theoretical interpretation of NMR data of protein interactions. The proposed analysis generalizes the idea of network representations of protein structures by expanding it to protein interactions. This approach is based on regularization of residue-resolved NMR relaxation times and chemical shift data and subsequent construction of an adjacency matrix that represents the underlying protein interaction as a graph or network. The network nodes represent protein residues. Two nodes are connected if two residues are functionally correlated during the protein interaction event. The analysis of the resulting network enables the quantification of the importance of each amino acid of a protein for its interactions. Furthermore, the determination of the pattern of correlations between residues yields insights into the functional architecture of an interaction. This is of special interest for intrinsically disordered proteins, since the structural (three-dimensional) architecture of these proteins and their complexes is difficult to determine. The power of the proposed methodology is demonstrated at the example of the interaction between the intrinsically disordered protein osteopontin and its natural ligand heparin. © 2016 The Protein Society.

Classification of pregnancy and labor contractions using a graph theory based analysis.

PubMed

Nader, N; Hassan, M; Falou, W; Diab, A; Al-Omar, S; Khalil, M; Marque, C

2015-08-01

In this paper, we propose a new framework to characterize the electrohysterographic (EHG) signals recorded during pregnancy and labor. The approach is based on the analysis of the propagation of the uterine electrical activity. The processing pipeline includes i) the estimation of the statistical dependencies between the different recorded EHG signals, ii) the characterization of the obtained connectivity matrices using network measures and iii) the use of these measures in clinical application: the classification between pregnancy and labor. Due to its robustness to volume conductor, we used the imaginary part of coherence in order to produce the connectivity matrix which is then transformed into a graph. We evaluate the performance of several graph measures. We also compare the results with the parameter mostly used in the literature: the peak frequency combined with the propagation velocity (PV +PF). Our results show that the use of the network measures is a promising tool to classify labor and pregnancy contractions with a small superiority of the graph strength over PV+PF.

Graph Theoretical Framework of Brain Networks in Multiple Sclerosis: A Review of Concepts.

PubMed

Fleischer, Vinzenz; Radetz, Angela; Ciolac, Dumitru; Muthuraman, Muthuraman; Gonzalez-Escamilla, Gabriel; Zipp, Frauke; Groppa, Sergiu

2017-11-01

Network science provides powerful access to essential organizational principles of the human brain. It has been applied in combination with graph theory to characterize brain connectivity patterns. In multiple sclerosis (MS), analysis of the brain networks derived from either structural or functional imaging provides new insights into pathological processes within the gray and white matter. Beyond focal lesions and diffuse tissue damage, network connectivity patterns could be important for closely tracking and predicting the disease course. In this review, we describe concepts of graph theory, highlight novel issues of tissue reorganization in acute and chronic neuroinflammation and address pitfalls with regard to network analysis in MS patients. We further provide an outline of functional and structural connectivity patterns observed in MS, spanning from disconnection and disruption on one hand to adaptation and compensation on the other. Moreover, we link network changes and their relation to clinical disability based on the current literature. Finally, we discuss the perspective of network science in MS for future research and postulate its role in the clinical framework. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Analysis of Resting-State fMRI Topological Graph Theory Properties in Methamphetamine Drug Users Applying Box-Counting Fractal Dimension.

PubMed

Siyah Mansoory, Meysam; Oghabian, Mohammad Ali; Jafari, Amir Homayoun; Shahbabaie, Alireza

2017-01-01

Graph theoretical analysis of functional Magnetic Resonance Imaging (fMRI) data has provided new measures of mapping human brain in vivo. Of all methods to measure the functional connectivity between regions, Linear Correlation (LC) calculation of activity time series of the brain regions as a linear measure is considered the most ubiquitous one. The strength of the dependence obligatory for graph construction and analysis is consistently underestimated by LC, because not all the bivariate distributions, but only the marginals are Gaussian. In a number of studies, Mutual Information (MI) has been employed, as a similarity measure between each two time series of the brain regions, a pure nonlinear measure. Owing to the complex fractal organization of the brain indicating self-similarity, more information on the brain can be revealed by fMRI Fractal Dimension (FD) analysis. In the present paper, Box-Counting Fractal Dimension (BCFD) is introduced for graph theoretical analysis of fMRI data in 17 methamphetamine drug users and 18 normal controls. Then, BCFD performance was evaluated compared to those of LC and MI methods. Moreover, the global topological graph properties of the brain networks inclusive of global efficiency, clustering coefficient and characteristic path length in addict subjects were investigated too. Compared to normal subjects by using statistical tests (P<0.05), topological graph properties were postulated to be disrupted significantly during the resting-state fMRI. Based on the results, analyzing the graph topological properties (representing the brain networks) based on BCFD is a more reliable method than LC and MI.

The investigation of social networks based on multi-component random graphs

NASA Astrophysics Data System (ADS)

Zadorozhnyi, V. N.; Yudin, E. B.

2018-01-01

The methods of non-homogeneous random graphs calibration are developed for social networks simulation. The graphs are calibrated by the degree distributions of the vertices and the edges. The mathematical foundation of the methods is formed by the theory of random graphs with the nonlinear preferential attachment rule and the theory of Erdôs-Rényi random graphs. In fact, well-calibrated network graph models and computer experiments with these models would help developers (owners) of the networks to predict their development correctly and to choose effective strategies for controlling network projects.

Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

PubMed

Jie, Biao; Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang

2018-05-01

As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer's disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.

Visibility graph analysis on heartbeat dynamics of meditation training

NASA Astrophysics Data System (ADS)

Jiang, Sen; Bian, Chunhua; Ning, Xinbao; Ma, Qianli D. Y.

2013-06-01

We apply the visibility graph analysis to human heartbeat dynamics by constructing the complex networks of heartbeat interval time series and investigating the statistical properties of the network before and during chi and yoga meditation. The experiment results show that visibility graph analysis can reveal the dynamical changes caused by meditation training manifested as regular heartbeat, which is closely related to the adjustment of autonomous neural system, and visibility graph analysis is effective to evaluate the effect of meditation.

BFL: a node and edge betweenness based fast layout algorithm for large scale networks

PubMed Central

Hashimoto, Tatsunori B; Nagasaki, Masao; Kojima, Kaname; Miyano, Satoru

2009-01-01

Background Network visualization would serve as a useful first step for analysis. However, current graph layout algorithms for biological pathways are insensitive to biologically important information, e.g. subcellular localization, biological node and graph attributes, or/and not available for large scale networks, e.g. more than 10000 elements. Results To overcome these problems, we propose the use of a biologically important graph metric, betweenness, a measure of network flow. This metric is highly correlated with many biological phenomena such as lethality and clusters. We devise a new fast parallel algorithm calculating betweenness to minimize the preprocessing cost. Using this metric, we also invent a node and edge betweenness based fast layout algorithm (BFL). BFL places the high-betweenness nodes to optimal positions and allows the low-betweenness nodes to reach suboptimal positions. Furthermore, BFL reduces the runtime by combining a sequential insertion algorim with betweenness. For a graph with n nodes, this approach reduces the expected runtime of the algorithm to O(n2) when considering edge crossings, and to O(n log n) when considering only density and edge lengths. Conclusion Our BFL algorithm is compared against fast graph layout algorithms and approaches requiring intensive optimizations. For gene networks, we show that our algorithm is faster than all layout algorithms tested while providing readability on par with intensive optimization algorithms. We achieve a 1.4 second runtime for a graph with 4000 nodes and 12000 edges on a standard desktop computer. PMID:19146673

Brain gray matter structural network in myotonic dystrophy type 1.

PubMed

Sugiyama, Atsuhiko; Sone, Daichi; Sato, Noriko; Kimura, Yukio; Ota, Miho; Maikusa, Norihide; Maekawa, Tomoko; Enokizono, Mikako; Mori-Yoshimura, Madoka; Ohya, Yasushi; Kuwabara, Satoshi; Matsuda, Hiroshi

2017-01-01

This study aimed to investigate abnormalities in structural covariance network constructed from gray matter volume in myotonic dystrophy type 1 (DM1) patients by using graph theoretical analysis for further clarification of the underlying mechanisms of central nervous system involvement. Twenty-eight DM1 patients (4 childhood onset, 10 juvenile onset, 14 adult onset), excluding three cases from 31 consecutive patients who underwent magnetic resonance imaging in a certain period, and 28 age- and sex- matched healthy control subjects were included in this study. The normalized gray matter images of both groups were subjected to voxel based morphometry (VBM) and Graph Analysis Toolbox for graph theoretical analysis. VBM revealed extensive gray matter atrophy in DM1 patients, including cortical and subcortical structures. On graph theoretical analysis, there were no significant differences between DM1 and control groups in terms of the global measures of connectivity. Betweenness centrality was increased in several regions including the left fusiform gyrus, whereas it was decreased in the right striatum. The absence of significant differences between the groups in global network measurements on graph theoretical analysis is consistent with the fact that the general cognitive function is preserved in DM1 patients. In DM1 patients, increased connectivity in the left fusiform gyrus and decreased connectivity in the right striatum might be associated with impairment in face perception and theory of mind, and schizotypal-paranoid personality traits, respectively.

Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph.

PubMed

Ma, Hong-Wu; Zhao, Xue-Ming; Yuan, Ying-Jin; Zeng, An-Ping

2004-08-12

Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account. In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the same subset are really functionally related. The rational decomposition of metabolic networks, and subsequent studies of the subsets, make it more amenable to understand the inherent organization and functionality of metabolic networks at the modular level. http://genome.gbf.de/bioinformatics/

Investigating the effects of streamline-based fiber tractography on matrix scaling in brain connective network.

PubMed

Jan, Hengtai; Chao, Yi-Ping; Cho, Kuan-Hung; Kuo, Li-Wei

2013-01-01

Investigating the brain connective network using the modern graph theory has been widely applied in cognitive and clinical neuroscience research. In this study, we aimed to investigate the effects of streamline-based fiber tractography on the change of network properties and established a systematic framework to understand how an adequate network matrix scaling can be determined. The network properties, including degree, efficiency and betweenness centrality, show similar tendency in both left and right hemispheres. By employing the curve-fitting process with exponential law and measuring the residuals, the association between changes of network properties and threshold of track numbers is found and an adequate range of investigating the lateralization of brain network is suggested. The proposed approach can be further applied in clinical applications to improve the diagnostic sensitivity using network analysis with graph theory.

Topological Characterization of Carbon Graphite and Crystal Cubic Carbon Structures.

PubMed

Siddiqui, Wei Gao Muhammad Kamran; Naeem, Muhammad; Rehman, Najma Abdul

2017-09-07

Graph theory is used for modeling, designing, analysis and understanding chemical structures or chemical networks and their properties. The molecular graph is a graph consisting of atoms called vertices and the chemical bond between atoms called edges. In this article, we study the chemical graphs of carbon graphite and crystal structure of cubic carbon. Moreover, we compute and give closed formulas of degree based additive topological indices, namely hyper-Zagreb index, first multiple and second multiple Zagreb indices, and first and second Zagreb polynomials.

Discriminating micropathogen lineages and their reticulate evolution through graph theory-based network analysis: the case of Trypanosoma cruzi, the agent of Chagas disease.

PubMed

Arnaud-Haond, Sophie; Moalic, Yann; Barnabé, Christian; Ayala, Francisco José; Tibayrenc, Michel

2014-01-01

Micropathogens (viruses, bacteria, fungi, parasitic protozoa) share a common trait, which is partial clonality, with wide variance in the respective influence of clonality and sexual recombination on the dynamics and evolution of taxa. The discrimination of distinct lineages and the reconstruction of their phylogenetic history are key information to infer their biomedical properties. However, the phylogenetic picture is often clouded by occasional events of recombination across divergent lineages, limiting the relevance of classical phylogenetic analysis and dichotomic trees. We have applied a network analysis based on graph theory to illustrate the relationships among genotypes of Trypanosoma cruzi, the parasitic protozoan responsible for Chagas disease, to identify major lineages and to unravel their past history of divergence and possible recombination events. At the scale of T. cruzi subspecific diversity, graph theory-based networks applied to 22 isoenzyme loci (262 distinct Multi-Locus-Enzyme-Electrophoresis -MLEE) and 19 microsatellite loci (66 Multi-Locus-Genotypes -MLG) fully confirms the high clustering of genotypes into major lineages or "near-clades". The release of the dichotomic constraint associated with phylogenetic reconstruction usually applied to Multilocus data allows identifying putative hybrids and their parental lineages. Reticulate topology suggests a slightly different history for some of the main "near-clades", and a possibly more complex origin for the putative hybrids than hitherto proposed. Finally the sub-network of the near-clade T. cruzi I (28 MLG) shows a clustering subdivision into three differentiated lesser near-clades ("Russian doll pattern"), which confirms the hypothesis recently proposed by other investigators. The present study broadens and clarifies the hypotheses previously obtained from classical markers on the same sets of data, which demonstrates the added value of this approach. This underlines the potential of graph theory-based network analysis for describing the nature and relationships of major pathogens, thereby opening stimulating prospects to unravel the organization, dynamics and history of major micropathogen lineages.

Deep graphs-A general framework to represent and analyze heterogeneous complex systems across scales.

PubMed

Traxl, Dominik; Boers, Niklas; Kurths, Jürgen

2016-06-01

Network theory has proven to be a powerful tool in describing and analyzing systems by modelling the relations between their constituent objects. Particularly in recent years, a great progress has been made by augmenting "traditional" network theory in order to account for the multiplex nature of many networks, multiple types of connections between objects, the time-evolution of networks, networks of networks and other intricacies. However, existing network representations still lack crucial features in order to serve as a general data analysis tool. These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales. In this paper, we introduce a collection of definitions resulting in a framework that, on the one hand, entails and unifies existing network representations (e.g., network of networks and multilayer networks), and on the other hand, generalizes and extends them by incorporating the above features. To implement these features, we first specify the nodes and edges of a finite graph as sets of properties (which are permitted to be arbitrary mathematical objects). Second, the mathematical concept of partition lattices is transferred to the network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges allows us to aggregate, compute, and allocate information on and between arbitrary groups of nodes. The derived partition lattice of a graph, which we denote by deep graph, constitutes a concise, yet comprehensive representation that enables the expression and analysis of heterogeneous properties, relations, and interactions on all scales of a complex system in a self-contained manner. Furthermore, to be able to utilize existing network-based methods and models, we derive different representations of multilayer networks from our framework and demonstrate the advantages of our representation. On the basis of the formal framework described here, we provide a rich, fully scalable (and self-explanatory) software package that integrates into the PyData ecosystem and offers interfaces to popular network packages, making it a powerful, general-purpose data analysis toolkit. We exemplify an application of deep graphs using a real world dataset, comprising 16 years of satellite-derived global precipitation measurements. We deduce a deep graph representation of these measurements in order to track and investigate local formations of spatio-temporal clusters of extreme precipitation events.

NV: Nessus Vulnerability Visualization for the Web

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harrison, Lane; Spahn, Riley B; Iannacone, Michael D

2012-01-01

Network vulnerability is a critical component of network se- curity. Yet vulnerability analysis has received relatively lit- tle attention from the security visualization community. In this paper we describe nv, a web-based Nessus vulnerability visualization. Nv utilizes treemaps and linked histograms to allow system administrators to discover, analyze, and man- age vulnerabilities on their networks. In addition to visual- izing single Nessus scans, nv supports the analysis of sequen- tial scans by showing which vulnerabilities have been fixed, remain open, or are newly discovered. Nv was also designed to operate completely in-browser, to avoid sending sensitive data to outside servers.more » We discuss the design of nv, as well as provide case studies demonstrating vulnerability analysis workflows which include a multiple-node testbed and data from the 2011 VAST Challenge.« less

Resting-state theta band connectivity and graph analysis in generalized social anxiety disorder.

PubMed

Xing, Mengqi; Tadayonnejad, Reza; MacNamara, Annmarie; Ajilore, Olusola; DiGangi, Julia; Phan, K Luan; Leow, Alex; Klumpp, Heide

2017-01-01

Functional magnetic resonance imaging (fMRI) resting-state studies show generalized social anxiety disorder (gSAD) is associated with disturbances in networks involved in emotion regulation, emotion processing, and perceptual functions, suggesting a network framework is integral to elucidating the pathophysiology of gSAD. However, fMRI does not measure the fast dynamic interconnections of functional networks. Therefore, we examined whole-brain functional connectomics with electroencephalogram (EEG) during resting-state. Resting-state EEG data was recorded for 32 patients with gSAD and 32 demographically-matched healthy controls (HC). Sensor-level connectivity analysis was applied on EEG data by using Weighted Phase Lag Index (WPLI) and graph analysis based on WPLI was used to determine clustering coefficient and characteristic path length to estimate local integration and global segregation of networks. WPLI results showed increased oscillatory midline coherence in the theta frequency band indicating higher connectivity in the gSAD relative to HC group during rest. Additionally, WPLI values positively correlated with state anxiety levels within the gSAD group but not the HC group. Our graph theory based connectomics analysis demonstrated increased clustering coefficient and decreased characteristic path length in theta-based whole brain functional organization in subjects with gSAD compared to HC. Theta-dependent interconnectivity was associated with state anxiety in gSAD and an increase in information processing efficiency in gSAD (compared to controls). Results may represent enhanced baseline self-focused attention, which is consistent with cognitive models of gSAD and fMRI studies implicating emotion dysregulation and disturbances in task negative networks (e.g., default mode network) in gSAD.

Neurodevelopmental alterations of large-scale structural networks in children with new-onset epilepsy.

PubMed

Bonilha, Leonardo; Tabesh, Ali; Dabbs, Kevin; Hsu, David A; Stafstrom, Carl E; Hermann, Bruce P; Lin, Jack J

2014-08-01

Recent neuroimaging and behavioral studies have revealed that children with new onset epilepsy already exhibit brain structural abnormalities and cognitive impairment. How the organization of large-scale brain structural networks is altered near the time of seizure onset and whether network changes are related to cognitive performances remain unclear. Recent studies also suggest that regional brain volume covariance reflects synchronized brain developmental changes. Here, we test the hypothesis that epilepsy during early-life is associated with abnormalities in brain network organization and cognition. We used graph theory to study structural brain networks based on regional volume covariance in 39 children with new-onset seizures and 28 healthy controls. Children with new-onset epilepsy showed a suboptimal topological structural organization with enhanced network segregation and reduced global integration compared with controls. At the regional level, structural reorganization was evident with redistributed nodes from the posterior to more anterior head regions. The epileptic brain network was more vulnerable to targeted but not random attacks. Finally, a subgroup of children with epilepsy, namely those with lower IQ and poorer executive function, had a reduced balance between network segregation and integration. Taken together, the findings suggest that the neurodevelopmental impact of new onset childhood epilepsies alters large-scale brain networks, resulting in greater vulnerability to network failure and cognitive impairment. Copyright © 2014 Wiley Periodicals, Inc.

Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory.

PubMed

Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas

2015-11-01

Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation. In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD. Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%. Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD. Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease. Copyright © 2015 International Federation of Clinical Neurophysiology. All rights reserved.

Graph configuration model based evaluation of the education-occupation match

PubMed Central

2018-01-01

To study education—occupation matchings we developed a bipartite network model of education to work transition and a graph configuration model based metric. We studied the career paths of 15 thousand Hungarian students based on the integrated database of the National Tax Administration, the National Health Insurance Fund, and the higher education information system of the Hungarian Government. A brief analysis of gender pay gap and the spatial distribution of over-education is presented to demonstrate the background of the research and the resulted open dataset. We highlighted the hierarchical and clustered structure of the career paths based on the multi-resolution analysis of the graph modularity. The results of the cluster analysis can support policymakers to fine-tune the fragmented program structure of higher education. PMID:29509783

Graph configuration model based evaluation of the education-occupation match.

PubMed

Gadar, Laszlo; Abonyi, Janos

2018-01-01

To study education-occupation matchings we developed a bipartite network model of education to work transition and a graph configuration model based metric. We studied the career paths of 15 thousand Hungarian students based on the integrated database of the National Tax Administration, the National Health Insurance Fund, and the higher education information system of the Hungarian Government. A brief analysis of gender pay gap and the spatial distribution of over-education is presented to demonstrate the background of the research and the resulted open dataset. We highlighted the hierarchical and clustered structure of the career paths based on the multi-resolution analysis of the graph modularity. The results of the cluster analysis can support policymakers to fine-tune the fragmented program structure of higher education.

Quantitative evaluation of simulated functional brain networks in graph theoretical analysis.

PubMed

Lee, Won Hee; Bullmore, Ed; Frangou, Sophia

2017-02-01

There is increasing interest in the potential of whole-brain computational models to provide mechanistic insights into resting-state brain networks. It is therefore important to determine the degree to which computational models reproduce the topological features of empirical functional brain networks. We used empirical connectivity data derived from diffusion spectrum and resting-state functional magnetic resonance imaging data from healthy individuals. Empirical and simulated functional networks, constrained by structural connectivity, were defined based on 66 brain anatomical regions (nodes). Simulated functional data were generated using the Kuramoto model in which each anatomical region acts as a phase oscillator. Network topology was studied using graph theory in the empirical and simulated data. The difference (relative error) between graph theory measures derived from empirical and simulated data was then estimated. We found that simulated data can be used with confidence to model graph measures of global network organization at different dynamic states and highlight the sensitive dependence of the solutions obtained in simulated data on the specified connection densities. This study provides a method for the quantitative evaluation and external validation of graph theory metrics derived from simulated data that can be used to inform future study designs. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Detecting altered connectivity patterns in HIV associated neurocognitive impairment using mutual connectivity analysis

NASA Astrophysics Data System (ADS)

Abidin, Anas Zainul; D'Souza, Adora M.; Nagarajan, Mahesh B.; Wismüller, Axel

2016-03-01

The use of functional Magnetic Resonance Imaging (fMRI) has provided interesting insights into our understanding of the brain. In clinical setups these scans have been used to detect and study changes in the brain network properties in various neurological disorders. A large percentage of subjects infected with HIV present cognitive deficits, which are known as HIV associated neurocognitive disorder (HAND). In this study we propose to use our novel technique named Mutual Connectivity Analysis (MCA) to detect differences in brain networks in subjects with and without HIV infection. Resting state functional MRI scans acquired from 10 subjects (5 HIV+ and 5 HIV-) were subject to standard preprocessing routines. Subsequently, the average time-series for each brain region of the Automated Anatomic Labeling (AAL) atlas are extracted and used with the MCA framework to obtain a graph characterizing the interactions between them. The network graphs obtained for different subjects are then compared using Network-Based Statistics (NBS), which is an approach to detect differences between graphs edges while controlling for the family-wise error rate when mass univariate testing is performed. Applying this approach on the graphs obtained yields a single network encompassing 42 nodes and 65 edges, which is significantly different between the two subject groups. Specifically connections to the regions in and around the basal ganglia are significantly decreased. Also some nodes corresponding to the posterior cingulate cortex are affected. These results are inline with our current understanding of pathophysiological mechanisms of HIV associated neurocognitive disease (HAND) and other HIV based fMRI connectivity studies. Hence, we illustrate the applicability of our novel approach with network-based statistics in a clinical case-control study to detect differences connectivity patterns.

Disrupted topology of the resting state structural connectome in middle-aged APOE ε4 carriers.

PubMed

Korthauer, L E; Zhan, L; Ajilore, O; Leow, A; Driscoll, I

2018-05-24

The apolipoprotein E (APOE) ε4 allele is the best characterized genetic risk factor for Alzheimer's disease to date. Older APOE ε4 carriers (aged 60 + years) are known to have disrupted structural and functional connectivity, but less is known about APOE-associated network integrity in middle age. The goal of this study was to characterize APOE-related differences in network topology in middle age, as disentangling the early effects of healthy versus pathological aging may aid early detection of Alzheimer's disease and inform treatments. We performed resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) in healthy, cognitively normal, middle-aged adults (age 40-60; N = 76, 38 APOE ε4 carriers). Graph theoretical analysis was used to calculate local and global efficiency of 1) a whole brain rs-fMRI network; 2) a whole brain DTI network; and 3) the resting state structural connectome (rsSC), an integrated functional-structural network derived using functional-by-structural hierarchical (FSH) mapping. Our results indicated no APOE ε4-associated differences in network topology of the rs-fMRI or DTI networks alone. However, ε4 carriers had significantly lower global and local efficiency of the integrated rsSC compared to non-carriers. Furthermore, ε4 carriers were less resilient to targeted node failure of the rsSC, which mimics the neuropathological process of Alzheimer's disease. Collectively, these findings suggest that integrating multiple neuroimaging modalities and employing graph theoretical analysis may reveal network-level vulnerabilities that may serve as biomarkers of age-related cognitive decline in middle age, decades before the onset of overt cognitive impairment. Copyright © 2018. Published by Elsevier Inc.

A Network Analysis of Social Balance in Conflict in the Maghreb

DTIC Science & Technology

2013-03-01

relationships from Table 6 shown in graph form. Graph by author using Pajek ( Batagelj & Mrvar , 1996 ...author using Pajek ( Batagelj & Mrvar , 1996 )................................................................................................ 4-17 Figure...negative and black is positive. By author using Pajek ( Batagelj & Mrvar , 1996 ). ..... 4-21 Figure 23: Network Graph of 10 Actor Network (Post-French

Aligning Biomolecular Networks Using Modular Graph Kernels

NASA Astrophysics Data System (ADS)

Towfic, Fadi; Greenlee, M. Heather West; Honavar, Vasant

Comparative analysis of biomolecular networks constructed using measurements from different conditions, tissues, and organisms offer a powerful approach to understanding the structure, function, dynamics, and evolution of complex biological systems. We explore a class of algorithms for aligning large biomolecular networks by breaking down such networks into subgraphs and computing the alignment of the networks based on the alignment of their subgraphs. The resulting subnetworks are compared using graph kernels as scoring functions. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit. Our experiments using Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository of protein-protein interaction data demonstrate that the performance of the proposed algorithms (as measured by % GO term enrichment of subnetworks identified by the alignment) is competitive with some of the state-of-the-art algorithms for pair-wise alignment of large protein-protein interaction networks. Our results also show that the inter-species similarity scores computed based on graph kernels can be used to cluster the species into a species tree that is consistent with the known phylogenetic relationships among the species.

GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.

PubMed

Hosseini, S M Hadi; Hoeft, Fumiko; Kesler, Shelli R

2012-01-01

In recent years, graph theoretical analyses of neuroimaging data have increased our understanding of the organization of large-scale structural and functional brain networks. However, tools for pipeline application of graph theory for analyzing topology of brain networks is still lacking. In this report, we describe the development of a graph-analysis toolbox (GAT) that facilitates analysis and comparison of structural and functional network brain networks. GAT provides a graphical user interface (GUI) that facilitates construction and analysis of brain networks, comparison of regional and global topological properties between networks, analysis of network hub and modules, and analysis of resilience of the networks to random failure and targeted attacks. Area under a curve (AUC) and functional data analyses (FDA), in conjunction with permutation testing, is employed for testing the differences in network topologies; analyses that are less sensitive to the thresholding process. We demonstrated the capabilities of GAT by investigating the differences in the organization of regional gray-matter correlation networks in survivors of acute lymphoblastic leukemia (ALL) and healthy matched Controls (CON). The results revealed an alteration in small-world characteristics of the brain networks in the ALL survivors; an observation that confirm our hypothesis suggesting widespread neurobiological injury in ALL survivors. Along with demonstration of the capabilities of the GAT, this is the first report of altered large-scale structural brain networks in ALL survivors.

Minimizing Expected Maximum Risk from Cyber-Attacks with Probabilistic Attack Success

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhuiyan, Tanveer H.; Nandi, Apurba; Medal, Hugh

The goal of our work is to enhance network security by generating partial cut-sets, which are a subset of edges that remove paths from initially vulnerable nodes (initial security conditions) to goal nodes (critical assets), on an attack graph given costs for cutting an edge and a limited overall budget.

BRAPH: A graph theory software for the analysis of brain connectivity

PubMed Central

Mijalkov, Mite; Kakaei, Ehsan; Pereira, Joana B.; Westman, Eric; Volpe, Giovanni

2017-01-01

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment. PMID:28763447

BRAPH: A graph theory software for the analysis of brain connectivity.

PubMed

Mijalkov, Mite; Kakaei, Ehsan; Pereira, Joana B; Westman, Eric; Volpe, Giovanni

2017-01-01

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH-BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer's disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson's patients with mild cognitive impairment.

Social and place-focused communities in location-based online social networks

NASA Astrophysics Data System (ADS)

Brown, Chloë; Nicosia, Vincenzo; Scellato, Salvatore; Noulas, Anastasios; Mascolo, Cecilia

2013-06-01

Thanks to widely available, cheap Internet access and the ubiquity of smartphones, millions of people around the world now use online location-based social networking services. Understanding the structural properties of these systems and their dependence upon users' habits and mobility has many potential applications, including resource recommendation and link prediction. Here, we construct and characterise social and place-focused graphs by using longitudinal information about declared social relationships and about users' visits to physical places collected from a popular online location-based social service. We show that although the social and place-focused graphs are constructed from the same data set, they have quite different structural properties. We find that the social and location-focused graphs have different global and meso-scale structure, and in particular that social and place-focused communities have negligible overlap. Consequently, group inference based on community detection performed on the social graph alone fails to isolate place-focused groups, even though these do exist in the network. By studying the evolution of tie structure within communities, we show that the time period over which location data are aggregated has a substantial impact on the stability of place-focused communities, and that information about place-based groups may be more useful for user-centric applications than that obtained from the analysis of social communities alone.

Graph theoretical analysis of complex networks in the brain

PubMed Central

Stam, Cornelis J; Reijneveld, Jaap C

2007-01-01

Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern. PMID:17908336

Comparative analysis of two discretizations of Ricci curvature for complex networks.

PubMed

Samal, Areejit; Sreejith, R P; Gu, Jiao; Liu, Shiping; Saucan, Emil; Jost, Jürgen

2018-06-05

We have performed an empirical comparison of two distinct notions of discrete Ricci curvature for graphs or networks, namely, the Forman-Ricci curvature and Ollivier-Ricci curvature. Importantly, these two discretizations of the Ricci curvature were developed based on different properties of the classical smooth notion, and thus, the two notions shed light on different aspects of network structure and behavior. Nevertheless, our extensive computational analysis in a wide range of both model and real-world networks shows that the two discretizations of Ricci curvature are highly correlated in many networks. Moreover, we show that if one considers the augmented Forman-Ricci curvature which also accounts for the two-dimensional simplicial complexes arising in graphs, the observed correlation between the two discretizations is even higher, especially, in real networks. Besides the potential theoretical implications of these observations, the close relationship between the two discretizations has practical implications whereby Forman-Ricci curvature can be employed in place of Ollivier-Ricci curvature for faster computation in larger real-world networks whenever coarse analysis suffices.

Integrating Sediment Connectivity into Water Resources Management Trough a Graph Theoretic, Stochastic Modeling Framework.

NASA Astrophysics Data System (ADS)

Schmitt, R. J. P.; Castelletti, A.; Bizzi, S.

2014-12-01

Understanding sediment transport processes at the river basin scale, their temporal spectra and spatial patterns is key to identify and minimize morphologic risks associated to channel adjustments processes. This work contributes a stochastic framework for modeling bed-load connectivity based on recent advances in the field (e.g., Bizzi & Lerner, 2013; Czubas & Foufoulas-Georgiu, 2014). It presents river managers with novel indicators from reach scale vulnerability to channel adjustment in large river networks with sparse hydrologic and sediment observations. The framework comprises three steps. First, based on a distributed hydrological model and remotely sensed information, the framework identifies a representative grain size class for each reach. Second, sediment residence time distributions are calculated for each reach in a Monte-Carlo approach applying standard sediment transport equations driven by local hydraulic conditions. Third, a network analysis defines the up- and downstream connectivity for various travel times resulting in characteristic up/downstream connectivity signatures for each reach. Channel vulnerability indicators quantify the imbalance between up/downstream connectivity for each travel time domain, representing process dependent latency of morphologic response. Last, based on the stochastic core of the model, a sensitivity analysis identifies drivers of change and major sources of uncertainty in order to target key detrimental processes and to guide effective gathering of additional data. The application, limitation and integration into a decision analytic framework is demonstrated for a major part of the Red River Basin in Northern Vietnam (179.000 km2). Here, a plethora of anthropic alterations ranging from large reservoir construction to land-use changes results in major downstream deterioration and calls for deriving concerted sediment management strategies to mitigate current and limit future morphologic alterations.

An intelligent allocation algorithm for parallel processing

NASA Technical Reports Server (NTRS)

Carroll, Chester C.; Homaifar, Abdollah; Ananthram, Kishan G.

1988-01-01

The problem of allocating nodes of a program graph to processors in a parallel processing architecture is considered. The algorithm is based on critical path analysis, some allocation heuristics, and the execution granularity of nodes in a program graph. These factors, and the structure of interprocessor communication network, influence the allocation. To achieve realistic estimations of the executive durations of allocations, the algorithm considers the fact that nodes in a program graph have to communicate through varying numbers of tokens. Coarse and fine granularities have been implemented, with interprocessor token-communication duration, varying from zero up to values comparable to the execution durations of individual nodes. The effect on allocation of communication network structures is demonstrated by performing allocations for crossbar (non-blocking) and star (blocking) networks. The algorithm assumes the availability of as many processors as it needs for the optimal allocation of any program graph. Hence, the focus of allocation has been on varying token-communication durations rather than varying the number of processors. The algorithm always utilizes as many processors as necessary for the optimal allocation of any program graph, depending upon granularity and characteristics of the interprocessor communication network.

Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines

NASA Astrophysics Data System (ADS)

Qayyum, Abdul; Saad, Naufal M.; Kamel, Nidal; Malik, Aamir Saeed

2018-01-01

The monitoring of vegetation near high-voltage transmission power lines and poles is tedious. Blackouts present a huge challenge to power distribution companies and often occur due to tree growth in hilly and rural areas. There are numerous methods of monitoring hazardous overgrowth that are expensive and time-consuming. Accurate estimation of tree and vegetation heights near power poles can prevent the disruption of power transmission in vulnerable zones. This paper presents a cost-effective approach based on a convolutional neural network (CNN) algorithm to compute the height (depth maps) of objects proximal to power poles and transmission lines. The proposed CNN extracts and classifies features by employing convolutional pooling inputs to fully connected data layers that capture prominent features from stereo image patches. Unmanned aerial vehicle or satellite stereo image datasets can thus provide a feasible and cost-effective approach that identifies threat levels based on height and distance estimations of hazardous vegetation and other objects. Results were compared with extant disparity map estimation techniques, such as graph cut, dynamic programming, belief propagation, and area-based methods. The proposed method achieved an accuracy rate of 90%.

TreeNetViz: revealing patterns of networks over tree structures.

PubMed

Gou, Liang; Zhang, Xiaolong Luke

2011-12-01

Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns. © 2011 IEEE

Semantic web for integrated network analysis in biomedicine.

PubMed

Chen, Huajun; Ding, Li; Wu, Zhaohui; Yu, Tong; Dhanapalan, Lavanya; Chen, Jake Y

2009-03-01

The Semantic Web technology enables integration of heterogeneous data on the World Wide Web by making the semantics of data explicit through formal ontologies. In this article, we survey the feasibility and state of the art of utilizing the Semantic Web technology to represent, integrate and analyze the knowledge in various biomedical networks. We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis. Through four case studies, we demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology category cross-talks, drug efficacy analysis and herb-drug interactions analysis.

Graph analysis of functional brain networks: practical issues in translational neuroscience

PubMed Central

De Vico Fallani, Fabrizio; Richiardi, Jonas; Chavez, Mario; Achard, Sophie

2014-01-01

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes. PMID:25180301

Learning In networks

NASA Technical Reports Server (NTRS)

Buntine, Wray L.

1995-01-01

Intelligent systems require software incorporating probabilistic reasoning, and often times learning. Networks provide a framework and methodology for creating this kind of software. This paper introduces network models based on chain graphs with deterministic nodes. Chain graphs are defined as a hierarchical combination of Bayesian and Markov networks. To model learning, plates on chain graphs are introduced to model independent samples. The paper concludes by discussing various operations that can be performed on chain graphs with plates as a simplification process or to generate learning algorithms.

A Visual Evaluation Study of Graph Sampling Techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Fangyan; Zhang, Song; Wong, Pak C.

2017-01-29

We evaluate a dozen prevailing graph-sampling techniques with an ultimate goal to better visualize and understand big and complex graphs that exhibit different properties and structures. The evaluation uses eight benchmark datasets with four different graph types collected from Stanford Network Analysis Platform and NetworkX to give a comprehensive comparison of various types of graphs. The study provides a practical guideline for visualizing big graphs of different sizes and structures. The paper discusses results and important observations from the study.

Experimental demonstration of graph-state quantum secret sharing.

PubMed

Bell, B A; Markham, D; Herrera-Martí, D A; Marin, A; Wadsworth, W J; Rarity, J G; Tame, M S

2014-11-21

Quantum communication and computing offer many new opportunities for information processing in a connected world. Networks using quantum resources with tailor-made entanglement structures have been proposed for a variety of tasks, including distributing, sharing and processing information. Recently, a class of states known as graph states has emerged, providing versatile quantum resources for such networking tasks. Here we report an experimental demonstration of graph state-based quantum secret sharing--an important primitive for a quantum network with applications ranging from secure money transfer to multiparty quantum computation. We use an all-optical setup, encoding quantum information into photons representing a five-qubit graph state. We find that one can reliably encode, distribute and share quantum information amongst four parties, with various access structures based on the complex connectivity of the graph. Our results show that graph states are a promising approach for realising sophisticated multi-layered communication protocols in quantum networks.

Deep graphs—A general framework to represent and analyze heterogeneous complex systems across scales

NASA Astrophysics Data System (ADS)

Traxl, Dominik; Boers, Niklas; Kurths, Jürgen

2016-06-01

Network theory has proven to be a powerful tool in describing and analyzing systems by modelling the relations between their constituent objects. Particularly in recent years, a great progress has been made by augmenting "traditional" network theory in order to account for the multiplex nature of many networks, multiple types of connections between objects, the time-evolution of networks, networks of networks and other intricacies. However, existing network representations still lack crucial features in order to serve as a general data analysis tool. These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales. In this paper, we introduce a collection of definitions resulting in a framework that, on the one hand, entails and unifies existing network representations (e.g., network of networks and multilayer networks), and on the other hand, generalizes and extends them by incorporating the above features. To implement these features, we first specify the nodes and edges of a finite graph as sets of properties (which are permitted to be arbitrary mathematical objects). Second, the mathematical concept of partition lattices is transferred to the network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges allows us to aggregate, compute, and allocate information on and between arbitrary groups of nodes. The derived partition lattice of a graph, which we denote by deep graph, constitutes a concise, yet comprehensive representation that enables the expression and analysis of heterogeneous properties, relations, and interactions on all scales of a complex system in a self-contained manner. Furthermore, to be able to utilize existing network-based methods and models, we derive different representations of multilayer networks from our framework and demonstrate the advantages of our representation. On the basis of the formal framework described here, we provide a rich, fully scalable (and self-explanatory) software package that integrates into the PyData ecosystem and offers interfaces to popular network packages, making it a powerful, general-purpose data analysis toolkit. We exemplify an application of deep graphs using a real world dataset, comprising 16 years of satellite-derived global precipitation measurements. We deduce a deep graph representation of these measurements in order to track and investigate local formations of spatio-temporal clusters of extreme precipitation events.

Risk assessment by dynamic representation of vulnerability, exploitation, and impact

NASA Astrophysics Data System (ADS)

Cam, Hasan

2015-05-01

Assessing and quantifying cyber risk accurately in real-time is essential to providing security and mission assurance in any system and network. This paper presents a modeling and dynamic analysis approach to assessing cyber risk of a network in real-time by representing dynamically its vulnerabilities, exploitations, and impact using integrated Bayesian network and Markov models. Given the set of vulnerabilities detected by a vulnerability scanner in a network, this paper addresses how its risk can be assessed by estimating in real-time the exploit likelihood and impact of vulnerability exploitation on the network, based on real-time observations and measurements over the network. The dynamic representation of the network in terms of its vulnerabilities, sensor measurements, and observations is constructed dynamically using the integrated Bayesian network and Markov models. The transition rates of outgoing and incoming links of states in hidden Markov models are used in determining exploit likelihood and impact of attacks, whereas emission rates help quantify the attack states of vulnerabilities. Simulation results show the quantification and evolving risk scores over time for individual and aggregated vulnerabilities of a network.

Image/video understanding systems based on network-symbolic models

NASA Astrophysics Data System (ADS)

Kuvich, Gary

2004-03-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolve ambiguity and uncertainty via feedback projections, and provide image understanding that is an interpretation of visual information in terms of such knowledge models. Computer simulation models are built on the basis of graphs/networks. The ability of human brain to emulate similar graph/network models is found. Symbols, predicates and grammars naturally emerge in such networks, and logic is simply a way of restructuring such models. Brain analyzes an image as a graph-type relational structure created via multilevel hierarchical compression of visual information. Primary areas provide active fusion of image features on a spatial grid-like structure, where nodes are cortical columns. Spatial logic and topology naturally present in such structures. Mid-level vision processes like perceptual grouping, separation of figure from ground, are special kinds of network transformations. They convert primary image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena are results of such analysis. Composition of network-symbolic models combines learning, classification, and analogy together with higher-level model-based reasoning into a single framework, and it works similar to frames and agents. Computational intelligence methods transform images into model-based knowledge representation. Based on such principles, an Image/Video Understanding system can convert images into the knowledge models, and resolve uncertainty and ambiguity. This allows creating intelligent computer vision systems for design and manufacturing.

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

PubMed

Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

2006-06-06

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence information. This method may yield further information about biological evolution, such as the history of horizontal transfer of each gene, by studying the detailed structure of the phylogenetic tree constructed by the kernel-based method.

Searching social networks for subgraph patterns

NASA Astrophysics Data System (ADS)

Ogaard, Kirk; Kase, Sue; Roy, Heather; Nagi, Rakesh; Sambhoos, Kedar; Sudit, Moises

2013-06-01

Software tools for Social Network Analysis (SNA) are being developed which support various types of analysis of social networks extracted from social media websites (e.g., Twitter). Once extracted and stored in a database such social networks are amenable to analysis by SNA software. This data analysis often involves searching for occurrences of various subgraph patterns (i.e., graphical representations of entities and relationships). The authors have developed the Graph Matching Toolkit (GMT) which provides an intuitive Graphical User Interface (GUI) for a heuristic graph matching algorithm called the Truncated Search Tree (TruST) algorithm. GMT is a visual interface for graph matching algorithms processing large social networks. GMT enables an analyst to draw a subgraph pattern by using a mouse to select categories and labels for nodes and links from drop-down menus. GMT then executes the TruST algorithm to find the top five occurrences of the subgraph pattern within the social network stored in the database. GMT was tested using a simulated counter-insurgency dataset consisting of cellular phone communications within a populated area of operations in Iraq. The results indicated GMT (when executing the TruST graph matching algorithm) is a time-efficient approach to searching large social networks. GMT's visual interface to a graph matching algorithm enables intelligence analysts to quickly analyze and summarize the large amounts of data necessary to produce actionable intelligence.

SpectralNET – an application for spectral graph analysis and visualization

PubMed Central

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-01-01

Background Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Results Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). Conclusion SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from . Source code is available upon request. PMID:16236170

SpectralNET--an application for spectral graph analysis and visualization.

PubMed

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-10-19

Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.

Multi-parametric centrality method for graph network models

NASA Astrophysics Data System (ADS)

Ivanov, Sergei Evgenievich; Gorlushkina, Natalia Nikolaevna; Ivanova, Lubov Nikolaevna

2018-04-01

The graph model networks are investigated to determine centrality, weights and the significance of vertices. For centrality analysis appliesa typical method that includesany one of the properties of graph vertices. In graph theory, methods of analyzing centrality are used: in terms by degree, closeness, betweenness, radiality, eccentricity, page-rank, status, Katz and eigenvector. We have proposed a new method of multi-parametric centrality, which includes a number of basic properties of the network member. The mathematical model of multi-parametric centrality method is developed. Comparison of results for the presented method with the centrality methods is carried out. For evaluate the results for the multi-parametric centrality methodthe graph model with hundreds of vertices is analyzed. The comparative analysis showed the accuracy of presented method, includes simultaneously a number of basic properties of vertices.

Childhood maltreatment is associated with a sex-dependent functional reorganization of a brain inhibitory control network.

PubMed

Elton, Amanda; Tripathi, Shanti P; Mletzko, Tanja; Young, Jonathan; Cisler, Josh M; James, G Andrew; Kilts, Clinton D

2014-04-01

Childhood adversity represents a major risk factor for drug addiction and other mental disorders. However, the specific mechanisms by which childhood adversity impacts human brain organization to confer greater vulnerability for negative outcomes in adulthood is largely unknown. As an impaired process in drug addiction, inhibitory control of behavior was investigated as a target of childhood maltreatment (abuse and neglect). Forty adults without Axis-I psychiatric disorders (21 females) completed a Childhood Trauma Questionnaire (CTQ) and underwent functional MRI (fMRI) while performing a stop-signal task. A group independent component analysis identified a putative brain inhibitory control network. Graph theoretical analyses and structural equation modeling investigated the impact of childhood maltreatment on the functional organization of this neural processing network. Graph theory outcomes revealed sex differences in the relationship between network functional connectivity and inhibitory control which were dependent on the severity of childhood maltreatment exposure. A network effective connectivity analysis indicated that a maltreatment dose-related negative modulation of dorsal anterior cingulate (dACC) activity by the left inferior frontal cortex (IFC) predicted better response inhibition and lesser attention deficit hyperactivity disorder (ADHD) symptoms in females, but poorer response inhibition and greater ADHD symptoms in males. Less inhibition of the right IFC by dACC in males with higher CTQ scores improved inhibitory control ability. The childhood maltreatment-related reorganization of a brain inhibitory control network provides sex-dependent mechanisms by which childhood adversity may confer greater risk for drug use and related disorders and by which adaptive brain responses protect individuals from this risk factor. Copyright © 2013 Wiley Periodicals, Inc.

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks.

PubMed

Petrovskaya, Olga V; Petrovskiy, Evgeny D; Lavrik, Inna N; Ivanisenko, Vladimir A

2017-04-01

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

Vulnerability detection using data-flow graphs and SMT solvers

DTIC Science & Technology

2016-10-31

concerns. The framework is modular and pipelined to allow scalable analysis on distributed systems. Our vulnerability detection framework employs machine...Design We designed the framework to be modular to enable flexible reuse and extendibility. In its current form, our framework performs the following

Identifying compromised systems through correlation of suspicious traffic from malware behavioral analysis

NASA Astrophysics Data System (ADS)

Camilo, Ana E. F.; Grégio, André; Santos, Rafael D. C.

2016-05-01

Malware detection may be accomplished through the analysis of their infection behavior. To do so, dynamic analysis systems run malware samples and extract their operating system activities and network traffic. This traffic may represent malware accessing external systems, either to steal sensitive data from victims or to fetch other malicious artifacts (configuration files, additional modules, commands). In this work, we propose the use of visualization as a tool to identify compromised systems based on correlating malware communications in the form of graphs and finding isomorphisms between them. We produced graphs from over 6 thousand distinct network traffic files captured during malware execution and analyzed the existing relationships among malware samples and IP addresses.

Graph distance for complex networks

NASA Astrophysics Data System (ADS)

Shimada, Yutaka; Hirata, Yoshito; Ikeguchi, Tohru; Aihara, Kazuyuki

2016-10-01

Networks are widely used as a tool for describing diverse real complex systems and have been successfully applied to many fields. The distance between networks is one of the most fundamental concepts for properly classifying real networks, detecting temporal changes in network structures, and effectively predicting their temporal evolution. However, this distance has rarely been discussed in the theory of complex networks. Here, we propose a graph distance between networks based on a Laplacian matrix that reflects the structural and dynamical properties of networked dynamical systems. Our results indicate that the Laplacian-based graph distance effectively quantifies the structural difference between complex networks. We further show that our approach successfully elucidates the temporal properties underlying temporal networks observed in the context of face-to-face human interactions.

Graph theory analysis of cortical thickness networks in adolescents with d-transposition of the great arteries.

PubMed

Watson, Christopher G; Stopp, Christian; Newburger, Jane W; Rivkin, Michael J

2018-02-01

Adolescents with d-transposition of the great arteries (d-TGA) who had the arterial switch operation in infancy have been found to have structural brain differences compared to healthy controls. We used cortical thickness measurements obtained from structural brain MRI to determine group differences in global brain organization using a graph theoretical approach. Ninety-two d-TGA subjects and 49 controls were scanned using one of two identical 1.5-Tesla MRI systems. Mean cortical thickness was obtained from 34 regions per hemisphere using Freesurfer. A linear model was used for each brain region to adjust for subject age, sex, and scanning location. Structural connectivity for each group was inferred based on the presence of high inter-regional correlations of the linear model residuals, and binary connectivity matrices were created by thresholding over a range of correlation values for each group. Graph theory analysis was performed using packages in R. Permutation tests were performed to determine significance of between-group differences in global network measures. Within-group connectivity patterns were qualitatively different between groups. At lower network densities, controls had significantly more long-range connections. The location and number of hub regions differed between groups: controls had a greater number of hubs at most network densities. The control network had a significant rightward asymmetry compared to the d-TGA group at all network densities. Using graph theory analysis of cortical thickness correlations, we found differences in brain structural network organization among d-TGA adolescents compared to controls. These may be related to the white matter and gray matter differences previously found in this cohort, and in turn may be related to the cognitive deficits this cohort presents.

Educational network comparative analysis of small groups: Short- and long-term communications

NASA Astrophysics Data System (ADS)

Berg, D. B.; Zvereva, O. M.; Nazarova, Yu. Yu.; Chepurov, E. G.; Kokovin, A. V.; Ranyuk, S. V.

2017-11-01

The present study is devoted to the discussion of small group communication network structures. These communications were observed in student groups, where actors were united with a regular educational activity. The comparative analysis was carried out for networks of short-term (1 hour) and long-term (4 weeks) communications, it was based on seven structural parameters, and consisted of two stages. At the first stage, differences between the network graphs were examined, and the random corresponding Bernoulli graphs were built. At the second stage, revealed differences were compared. Calculations were performed using UCINET software framework. It was found out that networks of long-term and short-term communications are quite different: the structure of a short-term communication network is close to a random one, whereas the most of long-term communication network parameters differ from the corresponding random ones by more than 30%. This difference can be explained by strong "noisiness" of a short-term communication network, and the lack of social in it.

Complexity of the international agro-food trade network and its impact on food safety.

PubMed

Ercsey-Ravasz, Mária; Toroczkai, Zoltán; Lakner, Zoltán; Baranyi, József

2012-01-01

With the world's population now in excess of 7 billion, it is vital to ensure the chemical and microbiological safety of our food, while maintaining the sustainability of its production, distribution and trade. Using UN databases, here we show that the international agro-food trade network (IFTN), with nodes and edges representing countries and import-export fluxes, respectively, has evolved into a highly heterogeneous, complex supply-chain network. Seven countries form the core of the IFTN, with high values of betweenness centrality and each trading with over 77% of all the countries in the world. Graph theoretical analysis and a dynamic food flux model show that the IFTN provides a vehicle suitable for the fast distribution of potential contaminants but unsuitable for tracing their origin. In particular, we show that high values of node betweenness and vulnerability correlate well with recorded large food poisoning outbreaks.

Massive Social Network Analysis: Mining Twitter for Social Good

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ediger, David; Jiang, Karl; Riedy, Edward J.

Social networks produce an enormous quantity of data. Facebook consists of over 400 million active users sharing over 5 billion pieces of information each month. Analyzing this vast quantity of unstructured data presents challenges for software and hardware. We present GraphCT, a Graph Characterization Tooklit for massive graphs representing social network data. On a 128-processor Cray XMT, GraphCT estimates the betweenness centrality of an artificially generated (R-MAT) 537 million vertex, 8.6 billion edge graph in 55 minutes. We use GraphCT to analyze public data from Twitter, a microblogging network. Twitter's message connections appear primarily tree-structured as a news dissemination system.more » Within the public data, however, are clusters of conversations. Using GraphCT, we can rank actors within these conversations and help analysts focus attention on a much smaller data subset.« less

Advanced Cyber Attack Modeling Analysis and Visualization

DTIC Science & Technology

2010-03-01

Graph Analysis Network Web Logs Netflow Data TCP Dump Data System Logs Detect Protect Security Management What-If Figure 8. TVA attack graphs for...Clustered Graphs,” in Proceedings of the Symposium on Graph Drawing, September 1996. [25] K. Lakkaraju, W. Yurcik, A. Lee, “NVisionIP: NetFlow

Topological robustness analysis of protein interaction networks reveals key targets for overcoming chemotherapy resistance in glioma

NASA Astrophysics Data System (ADS)

Azevedo, Hátylas; Moreira-Filho, Carlos Alberto

2015-11-01

Biological networks display high robustness against random failures but are vulnerable to targeted attacks on central nodes. Thus, network topology analysis represents a powerful tool for investigating network susceptibility against targeted node removal. Here, we built protein interaction networks associated with chemoresistance to temozolomide, an alkylating agent used in glioma therapy, and analyzed their modular structure and robustness against intentional attack. These networks showed functional modules related to DNA repair, immunity, apoptosis, cell stress, proliferation and migration. Subsequently, network vulnerability was assessed by means of centrality-based attacks based on the removal of node fractions in descending orders of degree, betweenness, or the product of degree and betweenness. This analysis revealed that removing nodes with high degree and high betweenness was more effective in altering networks’ robustness parameters, suggesting that their corresponding proteins may be particularly relevant to target temozolomide resistance. In silico data was used for validation and confirmed that central nodes are more relevant for altering proliferation rates in temozolomide-resistant glioma cell lines and for predicting survival in glioma patients. Altogether, these results demonstrate how the analysis of network vulnerability to topological attack facilitates target prioritization for overcoming cancer chemoresistance.

Speech graphs provide a quantitative measure of thought disorder in psychosis.

PubMed

Mota, Natalia B; Vasconcelos, Nivaldo A P; Lemos, Nathalia; Pieretti, Ana C; Kinouchi, Osame; Cecchi, Guillermo A; Copelli, Mauro; Ribeiro, Sidarta

2012-01-01

Psychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be achieved by the analysis of speech graphs. A graph represents a network with nodes connected by edges; in speech graphs, nodes correspond to words and edges correspond to semantic and grammatical relationships. To quantify speech differences related to psychosis, interviews with schizophrenics, manics and normal subjects were recorded and represented as graphs. Manics scored significantly higher than schizophrenics in ten graph measures. Psychopathological symptoms such as logorrhea, poor speech, and flight of thoughts were grasped by the analysis even when verbosity differences were discounted. Binary classifiers based on speech graph measures sorted schizophrenics from manics with up to 93.8% of sensitivity and 93.7% of specificity. In contrast, sorting based on the scores of two standard psychiatric scales (BPRS and PANSS) reached only 62.5% of sensitivity and specificity. The results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence. The quantitative analysis of speech graphs is not redundant with standard psychometric scales but rather complementary, as it yields a very accurate sorting of schizophrenics and manics. Overall, the results point to automated psychiatric diagnosis based not on what is said, but on how it is said.

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

NASA Astrophysics Data System (ADS)

Hyman, J.; Hagberg, A.; Srinivasan, G.; Mohd-Yusof, J.; Viswanathan, H. S.

2017-12-01

We present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths. First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. Accurate estimates of first passage times are obtained with an order of magnitude reduction of CPU time and mesh size using the proposed method.

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

NASA Astrophysics Data System (ADS)

Hyman, Jeffrey D.; Hagberg, Aric; Srinivasan, Gowri; Mohd-Yusof, Jamaludin; Viswanathan, Hari

2017-07-01

We present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths. First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. Accurate estimates of first passage times are obtained with an order of magnitude reduction of CPU time and mesh size using the proposed method.

Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

PubMed Central

Wang, Jin-Hui; Zuo, Xi-Nian; Gohel, Suril; Milham, Michael P.; Biswal, Bharat B.; He, Yong

2011-01-01

Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest. PMID:21818285

Local Higher-Order Graph Clustering

PubMed Central

Yin, Hao; Benson, Austin R.; Leskovec, Jure; Gleich, David F.

2018-01-01

Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph. These methods are attractive because they enable targeted clustering around a given seed node and are faster than traditional global graph clustering methods because their runtime does not depend on the size of the input graph. However, current local graph partitioning methods are not designed to account for the higher-order structures crucial to the network, nor can they effectively handle directed networks. Here we introduce a new class of local graph clustering methods that address these issues by incorporating higher-order network information captured by small subgraphs, also called network motifs. We develop the Motif-based Approximate Personalized PageRank (MAPPR) algorithm that finds clusters containing a seed node with minimal motif conductance, a generalization of the conductance metric for network motifs. We generalize existing theory to prove the fast running time (independent of the size of the graph) and obtain theoretical guarantees on the cluster quality (in terms of motif conductance). We also develop a theory of node neighborhoods for finding sets that have small motif conductance, and apply these results to the case of finding good seed nodes to use as input to the MAPPR algorithm. Experimental validation on community detection tasks in both synthetic and real-world networks, shows that our new framework MAPPR outperforms the current edge-based personalized PageRank methodology. PMID:29770258

Predicting and Detecting Emerging Cyberattack Patterns Using StreamWorks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chin, George; Choudhury, Sutanay; Feo, John T.

2014-06-30

The number and sophistication of cyberattacks on industries and governments have dramatically grown in recent years. To counter this movement, new advanced tools and techniques are needed to detect cyberattacks in their early stages such that defensive actions may be taken to avert or mitigate potential damage. From a cybersecurity analysis perspective, detecting cyberattacks may be cast as a problem of identifying patterns in computer network traffic. Logically and intuitively, these patterns may take on the form of a directed graph that conveys how an attack or intrusion propagates through the computers of a network. Such cyberattack graphs could providemore » cybersecurity analysts with powerful conceptual representations that are natural to express and analyze. We have been researching and developing graph-centric approaches and algorithms for dynamic cyberattack detection. The advanced dynamic graph algorithms we are developing will be packaged into a streaming network analysis framework known as StreamWorks. With StreamWorks, a scientist or analyst may detect and identify precursor events and patterns as they emerge in complex networks. This analysis framework is intended to be used in a dynamic environment where network data is streamed in and is appended to a large-scale dynamic graph. Specific graphical query patterns are decomposed and collected into a graph query library. The individual decomposed subpatterns in the library are continuously and efficiently matched against the dynamic graph as it evolves to identify and detect early, partial subgraph patterns. The scalable emerging subgraph pattern algorithms will match on both structural and semantic network properties.« less

Insights into Intrinsic Brain Networks based on Graph Theory and PET in right- compared to left-sided Temporal Lobe Epilepsy.

PubMed

Vanicek, Thomas; Hahn, Andreas; Traub-Weidinger, Tatjana; Hilger, Eva; Spies, Marie; Wadsak, Wolfgang; Lanzenberger, Rupert; Pataraia, Ekaterina; Asenbaum-Nan, Susanne

2016-06-28

The human brain exhibits marked hemispheric differences, though it is not fully understood to what extent lateralization of the epileptic focus is relevant. Preoperative [(18)F]FDG-PET depicts lateralization of seizure focus in patients with temporal lobe epilepsy and reveals dysfunctional metabolic brain connectivity. The aim of the present study was to compare metabolic connectivity, inferred from inter-regional [(18)F]FDG PET uptake correlations, in right-sided (RTLE; n = 30) and left-sided TLE (LTLE; n = 32) with healthy controls (HC; n = 31) using graph theory based network analysis. Comparing LTLE and RTLE and patient groups separately to HC, we observed higher lobar connectivity weights in RTLE compared to LTLE for connections of the temporal and the parietal lobe of the contralateral hemisphere (CH). Moreover, especially in RTLE compared to LTLE higher local efficiency were found in the temporal cortices and other brain regions of the CH. The results of this investigation implicate altered metabolic networks in patients with TLE specific to the lateralization of seizure focus, and describe compensatory mechanisms especially in the CH of patients with RTLE. We propose that graph theoretical analysis of metabolic connectivity using [(18)F]FDG-PET offers an important additional modality to explore brain networks.

Graph Based Models for Unsupervised High Dimensional Data Clustering and Network Analysis

DTIC Science & Technology

2015-01-01

ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for...algorithms we proposed improve the time e ciency signi cantly for large scale datasets. In the last chapter, we also propose an incremental reseeding...plume detection in hyper-spectral video data. These graph based clustering algorithms we proposed improve the time efficiency significantly for large

Ontology Design of Influential People Identification Using Centrality

NASA Astrophysics Data System (ADS)

Maulana Awangga, Rolly; Yusril, Muhammad; Setyawan, Helmi

2018-04-01

Identifying influential people as a node in a graph theory commonly calculated by social network analysis. The social network data has the user as node and edge as relation forming a friend relation graph. This research is conducting different meaning of every nodes relation in the social network. Ontology was perfect match science to describe the social network data as conceptual and domain. Ontology gives essential relationship in a social network more than a current graph. Ontology proposed as a standard for knowledge representation for the semantic web by World Wide Web Consortium. The formal data representation use Resource Description Framework (RDF) and Web Ontology Language (OWL) which is strategic for Open Knowledge-Based website data. Ontology used in the semantic description for a relationship in the social network, it is open to developing semantic based relationship ontology by adding and modifying various and different relationship to have influential people as a conclusion. This research proposes a model using OWL and RDF for influential people identification in the social network. The study use degree centrality, between ness centrality, and closeness centrality measurement for data validation. As a conclusion, influential people identification in Facebook can use proposed Ontology model in the Group, Photos, Photo Tag, Friends, Events and Works data.

Analysis of the enzyme network involved in cattle milk production using graph theory.

PubMed

Ghorbani, Sholeh; Tahmoorespur, Mojtaba; Masoudi Nejad, Ali; Nasiri, Mohammad; Asgari, Yazdan

2015-06-01

Understanding cattle metabolism and its relationship with milk products is important in bovine breeding. A systemic view could lead to consequences that will result in a better understanding of existing concepts. Topological indices and quantitative characterizations mostly result from the application of graph theory on biological data. In the present work, the enzyme network involved in cattle milk production was reconstructed and analyzed based on available bovine genome information using several public datasets (NCBI, Uniprot, KEGG, and Brenda). The reconstructed network consisted of 3605 reactions named by KEGG compound numbers and 646 enzymes that catalyzed the corresponding reactions. The characteristics of the directed and undirected network were analyzed using Graph Theory. The mean path length was calculated to be4.39 and 5.41 for directed and undirected networks, respectively. The top 11 hub enzymes whose abnormality could harm bovine health and reduce milk production were determined. Therefore, the aim of constructing the enzyme centric network was twofold; first to find out whether such network followed the same properties of other biological networks, and second, to find the key enzymes. The results of the present study can improve our understanding of milk production in cattle. Also, analysis of the enzyme network can help improve the modeling and simulation of biological systems and help design desired phenotypes to increase milk production quality or quantity.

Power laws and fragility in flow networks.

PubMed

Shore, Jesse; Chu, Catherine J; Bianchi, Matt T

2013-01-01

What makes economic and ecological networks so unlike other highly skewed networks in their tendency toward turbulence and collapse? Here, we explore the consequences of a defining feature of these networks: their nodes are tied together by flow. We show that flow networks tend to the power law degree distribution (PLDD) due to a self-reinforcing process involving position within the global network structure, and thus present the first random graph model for PLDDs that does not depend on a rich-get-richer function of nodal degree. We also show that in contrast to non-flow networks, PLDD flow networks are dramatically more vulnerable to catastrophic failure than non-PLDD flow networks, a finding with potential explanatory power in our age of resource- and financial-interdependence and turbulence.

Safety models incorporating graph theory based transit indicators.

PubMed

Quintero, Liliana; Sayed, Tarek; Wahba, Mohamed M

2013-01-01

There is a considerable need for tools to enable the evaluation of the safety of transit networks at the planning stage. One interesting approach for the planning of public transportation systems is the study of networks. Network techniques involve the analysis of systems by viewing them as a graph composed of a set of vertices (nodes) and edges (links). Once the transport system is visualized as a graph, various network properties can be evaluated based on the relationships between the network elements. Several indicators can be calculated including connectivity, coverage, directness and complexity, among others. The main objective of this study is to investigate the relationship between network-based transit indicators and safety. The study develops macro-level collision prediction models that explicitly incorporate transit physical and operational elements and transit network indicators as explanatory variables. Several macro-level (zonal) collision prediction models were developed using a generalized linear regression technique, assuming a negative binomial error structure. The models were grouped into four main themes: transit infrastructure, transit network topology, transit route design, and transit performance and operations. The safety models showed that collisions were significantly associated with transit network properties such as: connectivity, coverage, overlapping degree and the Local Index of Transit Availability. As well, the models showed a significant relationship between collisions and some transit physical and operational attributes such as the number of routes, frequency of routes, bus density, length of bus and 3+ priority lanes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Social Sensor Analytics: Making Sense of Network Models in Social Media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dowling, Chase P.; Harrison, Joshua J.; Sathanur, Arun V.

Social networks can be thought of as noisy sensor networks mapping real world information to the web. Owing to the extensive body of literature in sensor network analysis, this work sought to apply several novel and traditional methods in sensor network analysis for the purposes of efficiently interrogating social media data streams from raw data. We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the months of November 2013more » and June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify forms of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We hope to sufficiently characterize global behavior in a medium such as Twitter as a means of learning global model parameters one may use to predict or simulate behavior on a large scale. We have made our time series and dynamic graph analytical code available via a GitHub repository https://github.com/cpatdowling/salsa and our data are available upon request.« less

Overlapping community detection based on link graph using distance dynamics

NASA Astrophysics Data System (ADS)

Chen, Lei; Zhang, Jing; Cai, Li-Jun

2018-01-01

The distance dynamics model was recently proposed to detect the disjoint community of a complex network. To identify the overlapping structure of a network using the distance dynamics model, an overlapping community detection algorithm, called L-Attractor, is proposed in this paper. The process of L-Attractor mainly consists of three phases. In the first phase, L-Attractor transforms the original graph to a link graph (a new edge graph) to assure that one node has multiple distances. In the second phase, using the improved distance dynamics model, a dynamic interaction process is introduced to simulate the distance dynamics (shrink or stretch). Through the dynamic interaction process, all distances converge, and the disjoint community structure of the link graph naturally manifests itself. In the third phase, a recovery method is designed to convert the disjoint community structure of the link graph to the overlapping community structure of the original graph. Extensive experiments are conducted on the LFR benchmark networks as well as real-world networks. Based on the results, our algorithm demonstrates higher accuracy and quality than other state-of-the-art algorithms.

PeerShield: determining control and resilience criticality of collaborative cyber assets in networks

NASA Astrophysics Data System (ADS)

Cam, Hasan

2012-06-01

As attackers get more coordinated and advanced in cyber attacks, cyber assets are required to have much more resilience, control effectiveness, and collaboration in networks. Such a requirement makes it essential to take a comprehensive and objective approach for measuring the individual and relative performances of cyber security assets in network nodes. To this end, this paper presents four techniques as to how the relative importance of cyber assets can be measured more comprehensively and objectively by considering together the main variables of risk assessment (e.g., threats, vulnerabilities), multiple attributes (e.g., resilience, control, and influence), network connectivity and controllability among collaborative cyber assets in networks. In the first technique, a Bayesian network is used to include the random variables for control, recovery, and resilience attributes of nodes, in addition to the random variables of threats, vulnerabilities, and risk. The second technique shows how graph matching and coloring can be utilized to form collaborative pairs of nodes to shield together against threats and vulnerabilities. The third technique ranks the security assets of nodes by incorporating multiple weights and thresholds of attributes into a decision-making algorithm. In the fourth technique, the hierarchically well-separated tree is enhanced to first identify critical nodes of a network with respect to their attributes and network connectivity, and then selecting some nodes as driver nodes for network controllability.

Advanced Fault Diagnosis Methods in Molecular Networks

PubMed Central

Habibi, Iman; Emamian, Effat S.; Abdi, Ali

2014-01-01

Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally. PMID:25290670

Stability and dynamical properties of material flow systems on random networks

NASA Astrophysics Data System (ADS)

Anand, K.; Galla, T.

2009-04-01

The theory of complex networks and of disordered systems is used to study the stability and dynamical properties of a simple model of material flow networks defined on random graphs. In particular we address instabilities that are characteristic of flow networks in economic, ecological and biological systems. Based on results from random matrix theory, we work out the phase diagram of such systems defined on extensively connected random graphs, and study in detail how the choice of control policies and the network structure affects stability. We also present results for more complex topologies of the underlying graph, focussing on finitely connected Erdös-Réyni graphs, Small-World Networks and Barabási-Albert scale-free networks. Results indicate that variability of input-output matrix elements, and random structures of the underlying graph tend to make the system less stable, while fast price dynamics or strong responsiveness to stock accumulation promote stability.

A Clustering Graph Generator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Winlaw, Manda; De Sterck, Hans; Sanders, Geoffrey

In very simple terms a network can be de ned as a collection of points joined together by lines. Thus, networks can be used to represent connections between entities in a wide variety of elds including engi- neering, science, medicine, and sociology. Many large real-world networks share a surprising number of properties, leading to a strong interest in model development research and techniques for building synthetic networks have been developed, that capture these similarities and replicate real-world graphs. Modeling these real-world networks serves two purposes. First, building models that mimic the patterns and prop- erties of real networks helps tomore » understand the implications of these patterns and helps determine which patterns are important. If we develop a generative process to synthesize real networks we can also examine which growth processes are plausible and which are not. Secondly, high-quality, large-scale network data is often not available, because of economic, legal, technological, or other obstacles [7]. Thus, there are many instances where the systems of interest cannot be represented by a single exemplar network. As one example, consider the eld of cybersecurity, where systems require testing across diverse threat scenarios and validation across diverse network structures. In these cases, where there is no single exemplar network, the systems must instead be modeled as a collection of networks in which the variation among them may be just as important as their common features. By developing processes to build synthetic models, so-called graph generators, we can build synthetic networks that capture both the essential features of a system and realistic variability. Then we can use such synthetic graphs to perform tasks such as simulations, analysis, and decision making. We can also use synthetic graphs to performance test graph analysis algorithms, including clustering algorithms and anomaly detection algorithms.« less

Random graph models of social networks.

PubMed

Newman, M E J; Watts, D J; Strogatz, S H

2002-02-19

We describe some new exactly solvable models of the structure of social networks, based on random graphs with arbitrary degree distributions. We give models both for simple unipartite networks, such as acquaintance networks, and bipartite networks, such as affiliation networks. We compare the predictions of our models to data for a number of real-world social networks and find that in some cases, the models are in remarkable agreement with the data, whereas in others the agreement is poorer, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph.

TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data.

PubMed

Huang, Xiaoke; Zhao, Ye; Yang, Jing; Zhang, Chong; Ma, Chao; Ye, Xinyue

2016-01-01

We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis.

Tensor Spectral Clustering for Partitioning Higher-order Network Structures.

PubMed

Benson, Austin R; Gleich, David F; Leskovec, Jure

2015-01-01

Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms.

Tensor Spectral Clustering for Partitioning Higher-order Network Structures

PubMed Central

Benson, Austin R.; Gleich, David F.; Leskovec, Jure

2016-01-01

Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms. PMID:27812399

Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation

PubMed Central

Li, Wenyuan; Liu, Chun-Chi; Zhang, Tong; Li, Haifeng; Waterman, Michael S.; Zhou, Xianghong Jasmine

2011-01-01

The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks. PMID:21698123

A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks.

PubMed

Luo, Shibo; Dong, Mianxiong; Ota, Kaoru; Wu, Jun; Li, Jianhua

2015-12-17

Software-Defined Networking-based Mobile Networks (SDN-MNs) are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic nature and complexity, traditional network security assessment methodologies cannot be applied directly to SDN-MNs, and a novel security assessment methodology is needed. In this paper, an effective security assessment mechanism based on attack graphs and an Analytic Hierarchy Process (AHP) is proposed for SDN-MNs. Firstly, this paper discusses the security assessment problem of SDN-MNs and proposes a methodology using attack graphs and AHP. Secondly, to address the diversity and complexity of SDN-MNs, a novel attack graph definition and attack graph generation algorithm are proposed. In order to quantify security levels, the Node Minimal Effort (NME) is defined to quantify attack cost and derive system security levels based on NME. Thirdly, to calculate the NME of an attack graph that takes the dynamic factors of SDN-MN into consideration, we use AHP integrated with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) as the methodology. Finally, we offer a case study to validate the proposed methodology. The case study and evaluation show the advantages of the proposed security assessment mechanism.

A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks

PubMed Central

Luo, Shibo; Dong, Mianxiong; Ota, Kaoru; Wu, Jun; Li, Jianhua

2015-01-01

Software-Defined Networking-based Mobile Networks (SDN-MNs) are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic nature and complexity, traditional network security assessment methodologies cannot be applied directly to SDN-MNs, and a novel security assessment methodology is needed. In this paper, an effective security assessment mechanism based on attack graphs and an Analytic Hierarchy Process (AHP) is proposed for SDN-MNs. Firstly, this paper discusses the security assessment problem of SDN-MNs and proposes a methodology using attack graphs and AHP. Secondly, to address the diversity and complexity of SDN-MNs, a novel attack graph definition and attack graph generation algorithm are proposed. In order to quantify security levels, the Node Minimal Effort (NME) is defined to quantify attack cost and derive system security levels based on NME. Thirdly, to calculate the NME of an attack graph that takes the dynamic factors of SDN-MN into consideration, we use AHP integrated with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) as the methodology. Finally, we offer a case study to validate the proposed methodology. The case study and evaluation show the advantages of the proposed security assessment mechanism. PMID:26694409

On Quantifying Diffusion of Health Information on Twitter.

PubMed

Bakal, Gokhan; Kavuluru, Ramakanth

2017-02-01

With the increasing use of digital technologies, online social networks are emerging as major means of communication. Recently, social networks such as Facebook and Twitter are also being used by consumers, care providers (physicians, hospitals), and government agencies to share health related information. The asymmetric user network and the short message size have made Twitter particularly popular for propagating health related content on the Web. Besides tweeting on their own, users can choose to retweet particular tweets from other users (even if they do not follow them on Twitter.) Thus, a tweet can diffuse through the Twitter network via the follower-friend connections. In this paper, we report results of a pilot study we conducted to quantitatively assess how health related tweets diffuse in the directed follower-friend Twitter graph through the retweeting activity. Our effort includes (1). development of a retweet collection and Twitter retweet graph formation framework and (2). a preliminary analysis of retweet graphs and associated diffusion metrics for health tweets. Given the ambiguous nature (due to polysemy and sarcasm) of health relatedness of tweets collected with keyword based matches, our initial study is limited to ≈ 200 health related tweets (which were manually verified to be on health topics) each with at least 25 retweets. To our knowledge, this is first attempt to study health information diffusion on Twitter through retweet graph analysis.

On Learning Cluster Coefficient of Private Networks

PubMed Central

Wang, Yue; Wu, Xintao; Zhu, Jun; Xiang, Yang

2013-01-01

Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as clustering coefficient or modularity often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. In this paper, we treat a graph statistics as a function f and develop a divide and conquer approach to enforce differential privacy. The basic procedure of this approach is to first decompose the target computation f into several less complex unit computations f1, …, fm connected by basic mathematical operations (e.g., addition, subtraction, multiplication, division), then perturb the output of each fi with Laplace noise derived from its own sensitivity value and the distributed privacy threshold εi, and finally combine those perturbed fi as the perturbed output of computation f. We examine how various operations affect the accuracy of complex computations. When unit computations have large global sensitivity values, we enforce the differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We illustrate our approach by using clustering coefficient, which is a popular statistics used in social network analysis. Empirical evaluations on five real social networks and various synthetic graphs generated from three random graph models show the developed divide and conquer approach outperforms the direct approach. PMID:24429843

A new intrusion prevention model using planning knowledge graph

NASA Astrophysics Data System (ADS)

Cai, Zengyu; Feng, Yuan; Liu, Shuru; Gan, Yong

2013-03-01

Intelligent plan is a very important research in artificial intelligence, which has applied in network security. This paper proposes a new intrusion prevention model base on planning knowledge graph and discuses the system architecture and characteristics of this model. The Intrusion Prevention based on plan knowledge graph is completed by plan recognition based on planning knowledge graph, and the Intrusion response strategies and actions are completed by the hierarchical task network (HTN) planner in this paper. Intrusion prevention system has the advantages of intelligent planning, which has the advantage of the knowledge-sharing, the response focused, learning autonomy and protective ability.

PyBoolNet: a python package for the generation, analysis and visualization of boolean networks.

PubMed

Klarner, Hannes; Streck, Adam; Siebert, Heike

2017-03-01

The goal of this project is to provide a simple interface to working with Boolean networks. Emphasis is put on easy access to a large number of common tasks including the generation and manipulation of networks, attractor and basin computation, model checking and trap space computation, execution of established graph algorithms as well as graph drawing and layouts. P y B ool N et is a Python package for working with Boolean networks that supports simple access to model checking via N u SMV, standard graph algorithms via N etwork X and visualization via dot . In addition, state of the art attractor computation exploiting P otassco ASP is implemented. The package is function-based and uses only native Python and N etwork X data types. https://github.com/hklarner/PyBoolNet. hannes.klarner@fu-berlin.de. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Assessing the Robustness of Graph Statistics for Network Analysis Under Incomplete Information

DTIC Science & Technology

strategy for dismantling these networks based on their network structure. However, these strategies typically assume complete information about the...combat them with missing information . This thesis analyzes the performance of a variety of network statistics in the context of incomplete information by...leveraging simulation to remove nodes and edges from networks and evaluating the effect this missing information has on our ability to accurately

Generalised power graph compression reveals dominant relationship patterns in complex networks

PubMed Central

Ahnert, Sebastian E.

2014-01-01

We introduce a framework for the discovery of dominant relationship patterns in complex networks, by compressing the networks into power graphs with overlapping power nodes. When paired with enrichment analysis of node classification terms, the most compressible sets of edges provide a highly informative sketch of the dominant relationship patterns that define the network. In addition, this procedure also gives rise to a novel, link-based definition of overlapping node communities in which nodes are defined by their relationships with sets of other nodes, rather than through connections within the community. We show that this completely general approach can be applied to undirected, directed, and bipartite networks, yielding valuable insights into the large-scale structure of real-world networks, including social networks and food webs. Our approach therefore provides a novel way in which network architecture can be studied, defined and classified. PMID:24663099

Information Retrieval and Graph Analysis Approaches for Book Recommendation.

PubMed

Benkoussas, Chahinez; Bellot, Patrice

2015-01-01

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments.

Information Retrieval and Graph Analysis Approaches for Book Recommendation

PubMed Central

Benkoussas, Chahinez; Bellot, Patrice

2015-01-01

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments. PMID:26504899

Personal and Impersonal Stimuli Differentially Engage Brain Networks during Moral Reasoning

ERIC Educational Resources Information Center

Xue, Shao-Wei; Wang, Yan; Tang, Yi-Yuan

2013-01-01

Moral decision making has recently attracted considerable attention as a core feature of all human endeavors. Previous functional magnetic resonance imaging studies about moral judgment have identified brain areas associated with cognitive or emotional engagement. Here, we applied graph theory-based network analysis of event-related potentials…

Combining the Finite Element Method with Structural Connectome-based Analysis for Modeling Neurotrauma: Connectome Neurotrauma Mechanics

PubMed Central

Kraft, Reuben H.; Mckee, Phillip Justin; Dagro, Amy M.; Grafton, Scott T.

2012-01-01

This article presents the integration of brain injury biomechanics and graph theoretical analysis of neuronal connections, or connectomics, to form a neurocomputational model that captures spatiotemporal characteristics of trauma. We relate localized mechanical brain damage predicted from biofidelic finite element simulations of the human head subjected to impact with degradation in the structural connectome for a single individual. The finite element model incorporates various length scales into the full head simulations by including anisotropic constitutive laws informed by diffusion tensor imaging. Coupling between the finite element analysis and network-based tools is established through experimentally-based cellular injury thresholds for white matter regions. Once edges are degraded, graph theoretical measures are computed on the “damaged” network. For a frontal impact, the simulations predict that the temporal and occipital regions undergo the most axonal strain and strain rate at short times (less than 24 hrs), which leads to cellular death initiation, which results in damage that shows dependence on angle of impact and underlying microstructure of brain tissue. The monotonic cellular death relationships predict a spatiotemporal change of structural damage. Interestingly, at 96 hrs post-impact, computations predict no network nodes were completely disconnected from the network, despite significant damage to network edges. At early times () network measures of global and local efficiency were degraded little; however, as time increased to 96 hrs the network properties were significantly reduced. In the future, this computational framework could help inform functional networks from physics-based structural brain biomechanics to obtain not only a biomechanics-based understanding of injury, but also neurophysiological insight. PMID:22915997

Exploring the evolution of London's street network in the information space: A dual approach

NASA Astrophysics Data System (ADS)

Masucci, A. Paolo; Stanilov, Kiril; Batty, Michael

2014-01-01

We study the growth of London's street network in its dual representation, as the city has evolved over the past 224 years. The dual representation of a planar graph is a content-based network, where each node is a set of edges of the planar graph and represents a transportation unit in the so-called information space, i.e., the space where information is handled in order to navigate through the city. First, we discuss a novel hybrid technique to extract dual graphs from planar graphs, called the hierarchical intersection continuity negotiation principle. Then we show that the growth of the network can be analytically described by logistic laws and that the topological properties of the network are governed by robust log-normal distributions characterizing the network's connectivity and small-world properties that are consistent over time. Moreover, we find that the double-Pareto-like distributions for the connectivity emerge for major roads and can be modeled via a stochastic content-based network model using simple space-filling principles.

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hyman, Jeffrey De'Haven; Hagberg, Aric Arild; Mohd-Yusof, Jamaludin

Here, we present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We also derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths.more » First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. We obtain accurate estimates of first passage times with an order of magnitude reduction of CPU time and mesh size using the proposed method.« less

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

DOE PAGES

Hyman, Jeffrey De'Haven; Hagberg, Aric Arild; Mohd-Yusof, Jamaludin; ...

2017-07-10

Here, we present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We also derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths.more » First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. We obtain accurate estimates of first passage times with an order of magnitude reduction of CPU time and mesh size using the proposed method.« less

GLO-STIX: Graph-Level Operations for Specifying Techniques and Interactive eXploration

PubMed Central

Stolper, Charles D.; Kahng, Minsuk; Lin, Zhiyuan; Foerster, Florian; Goel, Aakash; Stasko, John; Chau, Duen Horng

2015-01-01

The field of graph visualization has produced a wealth of visualization techniques for accomplishing a variety of analysis tasks. Therefore analysts often rely on a suite of different techniques, and visual graph analysis application builders strive to provide this breadth of techniques. To provide a holistic model for specifying network visualization techniques (as opposed to considering each technique in isolation) we present the Graph-Level Operations (GLO) model. We describe a method for identifying GLOs and apply it to identify five classes of GLOs, which can be flexibly combined to re-create six canonical graph visualization techniques. We discuss advantages of the GLO model, including potentially discovering new, effective network visualization techniques and easing the engineering challenges of building multi-technique graph visualization applications. Finally, we implement the GLOs that we identified into the GLO-STIX prototype system that enables an analyst to interactively explore a graph by applying GLOs. PMID:26005315

Dynamical modeling and analysis of large cellular regulatory networks

NASA Astrophysics Data System (ADS)

Bérenguier, D.; Chaouiya, C.; Monteiro, P. T.; Naldi, A.; Remy, E.; Thieffry, D.; Tichit, L.

2013-06-01

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Computing the shape of brain networks using graph filtration and Gromov-Hausdorff metric.

PubMed

Lee, Hyekyoung; Chung, Moo K; Kang, Hyejin; Kim, Boong-Nyun; Lee, Dong Soo

2011-01-01

The difference between networks has been often assessed by the difference of global topological measures such as the clustering coefficient, degree distribution and modularity. In this paper, we introduce a new framework for measuring the network difference using the Gromov-Hausdorff (GH) distance, which is often used in shape analysis. In order to apply the GH distance, we define the shape of the brain network by piecing together the patches of locally connected nearest neighbors using the graph filtration. The shape of the network is then transformed to an algebraic form called the single linkage matrix. The single linkage matrix is subsequently used in measuring network differences using the GH distance. As an illustration, we apply the proposed framework to compare the FDG-PET based functional brain networks out of 24 attention deficit hyperactivity disorder (ADHD) children, 26 autism spectrum disorder (ASD) children and 11 pediatric control subjects.

Comparing multilayer brain networks between groups: Introducing graph metrics and recommendations.

PubMed

Mandke, Kanad; Meier, Jil; Brookes, Matthew J; O'Dea, Reuben D; Van Mieghem, Piet; Stam, Cornelis J; Hillebrand, Arjan; Tewarie, Prejaas

2018-02-01

There is an increasing awareness of the advantages of multi-modal neuroimaging. Networks obtained from different modalities are usually treated in isolation, which is however contradictory to accumulating evidence that these networks show non-trivial interdependencies. Even networks obtained from a single modality, such as frequency-band specific functional networks measured from magnetoencephalography (MEG) are often treated independently. Here, we discuss how a multilayer network framework allows for integration of multiple networks into a single network description and how graph metrics can be applied to quantify multilayer network organisation for group comparison. We analyse how well-known biases for single layer networks, such as effects of group differences in link density and/or average connectivity, influence multilayer networks, and we compare four schemes that aim to correct for such biases: the minimum spanning tree (MST), effective graph resistance cost minimisation, efficiency cost optimisation (ECO) and a normalisation scheme based on singular value decomposition (SVD). These schemes can be applied to the layers independently or to the multilayer network as a whole. For correction applied to whole multilayer networks, only the SVD showed sufficient bias correction. For correction applied to individual layers, three schemes (ECO, MST, SVD) could correct for biases. By using generative models as well as empirical MEG and functional magnetic resonance imaging (fMRI) data, we further demonstrated that all schemes were sensitive to identify network topology when the original networks were perturbed. In conclusion, uncorrected multilayer network analysis leads to biases. These biases may differ between centres and studies and could consequently lead to unreproducible results in a similar manner as for single layer networks. We therefore recommend using correction schemes prior to multilayer network analysis for group comparisons. Copyright © 2017 Elsevier Inc. All rights reserved.

What does the structure of its visibility graph tell us about the nature of the time series?

NASA Astrophysics Data System (ADS)

Franke, Jasper G.; Donner, Reik V.

2017-04-01

Visibility graphs are a recently introduced method to construct complex network representations based upon univariate time series in order to study their dynamical characteristics [1]. In the last years, this approach has been successfully applied to studying a considerable variety of geoscientific research questions and data sets, including non-trivial temporal patterns in complex earthquake catalogs [2] or time-reversibility in climate time series [3]. It has been shown that several characteristic features of the thus constructed networks differ between stochastic and deterministic (possibly chaotic) processes, which is, however, relatively hard to exploit in the case of real-world applications. In this study, we propose studying two new measures related with the network complexity of visibility graphs constructed from time series, one being a special type of network entropy [4] and the other a recently introduced measure of the heterogeneity of the network's degree distribution [5]. For paradigmatic model systems exhibiting bifurcation sequences between regular and chaotic dynamics, both properties clearly trace the transitions between both types of regimes and exhibit marked quantitative differences for regular and chaotic dynamics. Moreover, for dynamical systems with a small amount of additive noise, the considered properties demonstrate gradual changes prior to the bifurcation point. This finding appears closely related to the subsequent loss of stability of the current state known to lead to a critical slowing down as the transition point is approaches. In this spirit, both considered visibility graph characteristics provide alternative tracers of dynamical early warning signals consistent with classical indicators. Our results demonstrate that measures of visibility graph complexity (i) provide a potentially useful means to tracing changes in the dynamical patterns encoded in a univariate time series that originate from increasing autocorrelation and (ii) allow to systematically distinguish regular from deterministic-chaotic dynamics. We demonstrate the application of our method for different model systems as well as selected paleoclimate time series from the North Atlantic region. Notably, visibility graph based methods are particularly suited for studying the latter type of geoscientific data, since they do not impose intrinsic restrictions or assumptions on the nature of the time series under investigation in terms of noise process, linearity and sampling homogeneity. [1] Lacasa, Lucas, et al. "From time series to complex networks: The visibility graph." Proceedings of the National Academy of Sciences 105.13 (2008): 4972-4975. [2] Telesca, Luciano, and Michele Lovallo. "Analysis of seismic sequences by using the method of visibility graph." EPL (Europhysics Letters) 97.5 (2012): 50002. [3] Donges, Jonathan F., Reik V. Donner, and Jürgen Kurths. "Testing time series irreversibility using complex network methods." EPL (Europhysics Letters) 102.1 (2013): 10004. [4] Small, Michael. "Complex networks from time series: capturing dynamics." 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013), Beijing (2013): 2509-2512. [5] Jacob, Rinku, K.P. Harikrishnan, Ranjeev Misra, and G. Ambika. "Measure for degree heterogeneity in complex networks and its application to recurrence network analysis." arXiv preprint 1605.06607 (2016).

PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data.

PubMed

Du, Haixiao; Xia, Mingrui; Zhao, Kang; Liao, Xuhong; Yang, Huazhong; Wang, Yu; He, Yong

2018-05-01

The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph-theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit-graphics processing unit (CPU-GPU) framework with a graphical user interface to facilitate the mapping and characterization of high-resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel-based brain networks with ∼200,000 nodes that were derived from a resting-state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ∼27 h for one subject, which is markedly less than the 118 h required with a single-thread implementation. The voxel-based functional brain networks exhibited prominent small-world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto-parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto-parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high-resolution connectomics research in health and disease. © 2018 Wiley Periodicals, Inc.

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia

PubMed Central

Yu, Qingbao; Erhardt, Erik B.; Sui, Jing; Du, Yuhui; He, Hao; Hjelm, Devon; Cetin, Mustafa S.; Rachakonda, Srinivas; Miller, Robyn L.; Pearlson, Godfrey; Calhoun, Vince D.

2014-01-01

Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia which may underscore the abnormal brain performance in this mental illness. PMID:25514514

Graph theory network function in Parkinson's disease assessed with electroencephalography.

PubMed

Utianski, Rene L; Caviness, John N; van Straaten, Elisabeth C W; Beach, Thomas G; Dugger, Brittany N; Shill, Holly A; Driver-Dunckley, Erika D; Sabbagh, Marwan N; Mehta, Shyamal; Adler, Charles H; Hentz, Joseph G

2016-05-01

To determine what differences exist in graph theory network measures derived from electroencephalography (EEG), between Parkinson's disease (PD) patients who are cognitively normal (PD-CN) and matched healthy controls; and between PD-CN and PD dementia (PD-D). EEG recordings were analyzed via graph theory network analysis to quantify changes in global efficiency and local integration. This included minimal spanning tree analysis. T-tests and correlations were used to assess differences between groups and assess the relationship with cognitive performance. Network measures showed increased local integration across all frequency bands between control and PD-CN; in contrast, decreased local integration occurred in PD-D when compared to PD-CN in the alpha1 frequency band. Differences found in PD-MCI mirrored PD-D. Correlations were found between network measures and assessments of global cognitive performance in PD. Our results reveal distinct patterns of band and network measure type alteration and breakdown for PD, as well as with cognitive decline in PD. These patterns suggest specific ways that interaction between cortical areas becomes abnormal and contributes to PD symptoms at various stages. Graph theory analysis by EEG suggests that network alteration and breakdown are robust attributes of PD cortical dysfunction pathophysiology. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

The Laplacian spectrum of neural networks

PubMed Central

de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.

2014-01-01

The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286

Fisher metric, geometric entanglement, and spin networks

NASA Astrophysics Data System (ADS)

Chirco, Goffredo; Mele, Fabio M.; Oriti, Daniele; Vitale, Patrizia

2018-02-01

Starting from recent results on the geometric formulation of quantum mechanics, we propose a new information geometric characterization of entanglement for spin network states in the context of quantum gravity. For the simple case of a single-link fixed graph (Wilson line), we detail the construction of a Riemannian Fisher metric tensor and a symplectic structure on the graph Hilbert space, showing how these encode the whole information about separability and entanglement. In particular, the Fisher metric defines an entanglement monotone which provides a notion of distance among states in the Hilbert space. In the maximally entangled gauge-invariant case, the entanglement monotone is proportional to a power of the area of the surface dual to the link thus supporting a connection between entanglement and the (simplicial) geometric properties of spin network states. We further extend such analysis to the study of nonlocal correlations between two nonadjacent regions of a generic spin network graph characterized by the bipartite unfolding of an intertwiner state. Our analysis confirms the interpretation of spin network bonds as a result of entanglement and to regard the same spin network graph as an information graph, whose connectivity encodes, both at the local and nonlocal level, the quantum correlations among its parts. This gives a further connection between entanglement and geometry.

Analysis of Network Vulnerability Under Joint Node and Link Attacks

NASA Astrophysics Data System (ADS)

Li, Yongcheng; Liu, Shumei; Yu, Yao; Cao, Ting

2018-03-01

The security problem of computer network system is becoming more and more serious. The fundamental reason is that there are security vulnerabilities in the network system. Therefore, it’s very important to identify and reduce or eliminate these vulnerabilities before they are attacked. In this paper, we are interested in joint node and link attacks and propose a vulnerability evaluation method based on the overall connectivity of the network to defense this attack. Especially, we analyze the attack cost problem from the attackers’ perspective. The purpose is to find the set of least costs for joint links and nodes, and their deletion will lead to serious network connection damage. The simulation results show that the vulnerable elements obtained from the proposed method are more suitable for the attacking idea of the malicious persons in joint node and link attack. It is easy to find that the proposed method has more realistic protection significance.

Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.

PubMed

Crichton, Gamal; Guo, Yufan; Pyysalo, Sampo; Korhonen, Anna

2018-05-21

Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results. However they did not use realistic settings like time-slicing, evaluate performances with comprehensive metrics or explain when or why neural network methods outperform. We investigated how inputs from four node representation algorithms affect performance of a neural link predictor on random- and time-sliced biomedical graphs of real-world sizes (∼ 6 million edges) containing information relevant to DTI, PPI and LBD. We compared the performance of the neural link predictor to those of established baselines and report performance across five metrics. In random- and time-sliced experiments when the neural network methods were able to learn good node representations and there was a negligible amount of disconnected nodes, those approaches outperformed the baselines. In the smallest graph (∼ 15,000 edges) and in larger graphs with approximately 14% disconnected nodes, baselines such as Common Neighbours proved a justifiable choice for link prediction. At low recall levels (∼ 0.3) the approaches were mostly equal, but at higher recall levels across all nodes and average performance at individual nodes, neural network approaches were superior. Analysis showed that neural network methods performed well on links between nodes with no previous common neighbours; potentially the most interesting links. Additionally, while neural network methods benefit from large amounts of data, they require considerable amounts of computational resources to utilise them. Our results indicate that when there is enough data for the neural network methods to use and there are a negligible amount of disconnected nodes, those approaches outperform the baselines. At low recall levels the approaches are mostly equal but at higher recall levels and average performance at individual nodes, neural network approaches are superior. Performance at nodes without common neighbours which indicate more unexpected and perhaps more useful links account for this.

Ontology- and graph-based similarity assessment in biological networks.

PubMed

Wang, Haiying; Zheng, Huiru; Azuaje, Francisco

2010-10-15

A standard systems-based approach to biomarker and drug target discovery consists of placing putative biomarkers in the context of a network of biological interactions, followed by different 'guilt-by-association' analyses. The latter is typically done based on network structural features. Here, an alternative analysis approach in which the networks are analyzed on a 'semantic similarity' space is reported. Such information is extracted from ontology-based functional annotations. We present SimTrek, a Cytoscape plugin for ontology-based similarity assessment in biological networks. http://rosalind.infj.ulst.ac.uk/SimTrek.html francisco.azuaje@crp-sante.lu Supplementary data are available at Bioinformatics online.

Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling.

PubMed

de Santos-Sierra, Daniel; Sendiña-Nadal, Irene; Leyva, Inmaculada; Almendral, Juan A; Ayali, Amir; Anava, Sarit; Sánchez-Ávila, Carmen; Boccaletti, Stefano

2015-06-01

Large scale phase-contrast images taken at high resolution through the life of a cultured neuronal network are analyzed by a graph-based unsupervised segmentation algorithm with a very low computational cost, scaling linearly with the image size. The processing automatically retrieves the whole network structure, an object whose mathematical representation is a matrix in which nodes are identified neurons or neurons' clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our non invasive measures entitle us to perform a longitudinal analysis during the maturation of a single culture. Such an analysis furnishes the way of individuating the main physical processes underlying the self-organization of the neurons' ensemble into a complex network, and drives the formulation of a phenomenological model yet able to describe qualitatively the overall scenario observed during the culture growth. © 2014 International Society for Advancement of Cytometry.

Bayesian Analysis for Exponential Random Graph Models Using the Adaptive Exchange Sampler.

PubMed

Jin, Ick Hoon; Yuan, Ying; Liang, Faming

2013-10-01

Exponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the intractable normalizing constant and model degeneracy. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the intractable normalizing constant and model degeneracy issues encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.

Image understanding systems based on the unifying representation of perceptual and conceptual information and the solution of mid-level and high-level vision problems

NASA Astrophysics Data System (ADS)

Kuvychko, Igor

2001-10-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, that is an interpretation of visual information in terms of such knowledge models. A computer vision system based on such principles requires unifying representation of perceptual and conceptual information. Computer simulation models are built on the basis of graphs/networks. The ability of human brain to emulate similar graph/networks models is found. That means a very important shift of paradigm in our knowledge about brain from neural networks to the cortical software. Starting from the primary visual areas, brain analyzes an image as a graph-type spatial structure. Primary areas provide active fusion of image features on a spatial grid-like structure, where nodes are cortical columns. The spatial combination of different neighbor features cannot be described as a statistical/integral characteristic of the analyzed region, but uniquely characterizes such region itself. Spatial logic and topology naturally present in such structures. Mid-level vision processes like clustering, perceptual grouping, multilevel hierarchical compression, separation of figure from ground, etc. are special kinds of graph/network transformations. They convert low-level image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena like shape from shading, occlusion, etc. are results of such analysis. Such approach gives opportunity not only to explain frequently unexplainable results of the cognitive science, but also to create intelligent computer vision systems that simulate perceptional processes in both what and where visual pathways. Such systems can open new horizons for robotic and computer vision industries.

A new algorithm to find fuzzy Hamilton cycle in a fuzzy network using adjacency matrix and minimum vertex degree.

PubMed

Nagoor Gani, A; Latha, S R

2016-01-01

A Hamiltonian cycle in a graph is a cycle that visits each node/vertex exactly once. A graph containing a Hamiltonian cycle is called a Hamiltonian graph. There have been several researches to find the number of Hamiltonian cycles of a Hamilton graph. As the number of vertices and edges grow, it becomes very difficult to keep track of all the different ways through which the vertices are connected. Hence, analysis of large graphs can be efficiently done with the assistance of a computer system that interprets graphs as matrices. And, of course, a good and well written algorithm will expedite the analysis even faster. The most convenient way to quickly test whether there is an edge between two vertices is to represent graphs using adjacent matrices. In this paper, a new algorithm is proposed to find fuzzy Hamiltonian cycle using adjacency matrix and the degree of the vertices of a fuzzy graph. A fuzzy graph structure is also modeled to illustrate the proposed algorithms with the selected air network of Indigo airlines.

Stock market network’s topological stability: Evidence from planar maximally filtered graph and minimal spanning tree

NASA Astrophysics Data System (ADS)

Yan, Xin-Guo; Xie, Chi; Wang, Gang-Jin

2015-08-01

We study the topological stability of stock market network by investigating the topological robustness, namely the ability of the network to resist structural or topological changes. The stock market network is extracted by minimal spanning tree (MST) and planar maximally filtered graph (PMFG). We find that the specific delisting thresholds of the listed companies exist in both MST and PMFG networks. In comparison with MST, PMFG provides more information and is better for the aim of exploring stock market network’s robustness. The PMFG before the US sub-prime crisis (i.e., from June 2005 to May 2007) has a stronger robustness against the intentional topological damage than the other two sub-periods (i.e., from June 2007 to May 2009 and from June 2009 to May 2011). We also find that the nonfractal property exists in MSTs of S&P 500, i.e., the highly connected nodes link with each other directly, which indicates that the MSTs are vulnerable to the removal of such important nodes. Moreover, the financial institutions and high technology companies are important in maintaining the stability of S&P 500 network.

Network Analysis in Disorders of Consciousness: Four Problems and One Proposed Solution (Exponential Random Graph Models)

PubMed Central

Dell'Italia, John; Johnson, Micah A.; Vespa, Paul M.; Monti, Martin M.

2018-01-01

In recent years, the study of the neural basis of consciousness, particularly in the context of patients recovering from severe brain injury, has greatly benefited from the application of sophisticated network analysis techniques to functional brain data. Yet, current graph theoretic approaches, as employed in the neuroimaging literature, suffer from four important shortcomings. First, they require arbitrary fixing of the number of connections (i.e., density) across networks which are likely to have different “natural” (i.e., stable) density (e.g., patients vs. controls, vegetative state vs. minimally conscious state patients). Second, when describing networks, they do not control for the fact that many characteristics are interrelated, particularly some of the most popular metrics employed (e.g., nodal degree, clustering coefficient)—which can lead to spurious results. Third, in the clinical domain of disorders of consciousness, there currently are no methods for incorporating structural connectivity in the characterization of functional networks which clouds the interpretation of functional differences across groups with different underlying pathology as well as in longitudinal approaches where structural reorganization processes might be operating. Finally, current methods do not allow assessing the dynamics of network change over time. We present a different framework for network analysis, based on Exponential Random Graph Models, which overcomes the above limitations and is thus particularly well suited for clinical populations with disorders of consciousness. We demonstrate this approach in the context of the longitudinal study of recovery from coma. First, our data show that throughout recovery from coma, brain graphs vary in their natural level of connectivity (from 10.4 to 14.5%), which conflicts with the standard approach of imposing arbitrary and equal density thresholds across networks (e.g., time-points, subjects, groups). Second, we show that failure to consider the interrelation between network measures does lead to spurious characterization of both inter- and intra-regional brain connectivity. Finally, we show that Separable Temporal ERGM can be employed to describe network dynamics over time revealing the specific pattern of formation and dissolution of connectivity that accompany recovery from coma. PMID:29946293

A Vulnerability Index and Analysis for the Road Network of Rural Chile

NASA Astrophysics Data System (ADS)

Braun, Andreas; Stötzer, Johanna; Kubisch, Susanne; Dittrich, Andre; Keller, Sina

2017-04-01

Natural hazards impose considerable threats to the physical and socio-economic wellbeing of people, a fact, which is well understood and investigated for many regions. However, not only people are vulnerable. During the last decades, a considerable amount of literature has focussed the particular vulnerability of the critical infrastructure: for example road networks. Considering critical infrastructure, far less reliable information exists for many regions worldwide - particularly, regions outside of the so called developed world. Critical infrastructure is destroyed in many disasters, causing cascade and follow up effects, for instance, impediments during evacuation, rescue and during the resilience phase. These circumstances, which are general enough to be applied to most regions, aggravate in regions characterized by high disparities between the urban and the rural sphere. Peripheral rural areas are especially prone to get isolated due to defects of the few roads which connect them to larger urban centres (where, frequently, disaster and emergency actors are situated). The rural area of Central Chile is a appropriate example for these circumstances. It is prone to destruction by several geo-hazards and furthermore, characterized by the aforementioned disparities. Past disasters, e.g. the 1991 Cerro Hudson eruption and the 2010 Maule earthquake have led to follow up effects (e.g. farmers, being unable to evacuate their animals due to road failures in the first case, and difficultires to evacuate people from places such as Caleta Tumbes or Dichato, which are connected by just a single road only in the second). The contribution develops a methodology to investigate into the critical infrastructure of such places. It develops a remoteness index for Chile, which identifies remote, peripheral rural areas, prone to get isolated due to road network failures during disasters. The approach is graph based. It offers particular advantages for regions like rural Chile since 1. it does not require traffic flow data which do not exist, 2. identifies peripheral areas particularly well, 3. identifies both nodes (places) prone to isolation and edges (roads) critical for the connectivity of rural areas, 4. based on a mathematical structure, it implies several possible planning solutions to reduce vulnerability of the critical infrastructure and people dependent on it. The methodology is presented and elaborated theoretically. Afterwards, it is demonstrated on an actual dataset from central Chile. It is demonstrated, how the methodology can be applied to derive planning solutions for peripheral rural areas.

Brain network dynamics characterization in epileptic seizures. Joint directed graph and pairwise synchronization measures

NASA Astrophysics Data System (ADS)

Rodrigues, A. C.; Machado, B. S.; Florence, G.; Hamad, A. P.; Sakamoto, A. C.; Fujita, A.; Baccalá, L. A.; Amaro, E.; Sameshima, K.

2014-12-01

Here we propose and evaluate a new approach to analyse multichannel mesial temporal lobe epilepsy EEG data from eight patients through complex network and synchronization theories. The method employs a Granger causality test to infer the directed connectivity graphs and a wavelet transform based phase synchronization measure whose characteristics allow studying dynamical transitions during epileptic seizures. We present a new combined graph measure that quantifies the level of network hub formation, called network hub out-degree, which closely reflects the level of synchronization observed during the ictus.

Unsupervised spatiotemporal analysis of fMRI data using graph-based visualizations of self-organizing maps.

PubMed

Katwal, Santosh B; Gore, John C; Marois, Rene; Rogers, Baxter P

2013-09-01

We present novel graph-based visualizations of self-organizing maps for unsupervised functional magnetic resonance imaging (fMRI) analysis. A self-organizing map is an artificial neural network model that transforms high-dimensional data into a low-dimensional (often a 2-D) map using unsupervised learning. However, a postprocessing scheme is necessary to correctly interpret similarity between neighboring node prototypes (feature vectors) on the output map and delineate clusters and features of interest in the data. In this paper, we used graph-based visualizations to capture fMRI data features based upon 1) the distribution of data across the receptive fields of the prototypes (density-based connectivity); and 2) temporal similarities (correlations) between the prototypes (correlation-based connectivity). We applied this approach to identify task-related brain areas in an fMRI reaction time experiment involving a visuo-manual response task, and we correlated the time-to-peak of the fMRI responses in these areas with reaction time. Visualization of self-organizing maps outperformed independent component analysis and voxelwise univariate linear regression analysis in identifying and classifying relevant brain regions. We conclude that the graph-based visualizations of self-organizing maps help in advanced visualization of cluster boundaries in fMRI data enabling the separation of regions with small differences in the timings of their brain responses.

Sampling from complex networks using distributed learning automata

NASA Astrophysics Data System (ADS)

Rezvanian, Alireza; Rahmati, Mohammad; Meybodi, Mohammad Reza

2014-02-01

A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due to a growing trend in analysis of online social networks as dynamic complex large-scale graphs. Due to the large scale and limited access of real networks, the network model is characterized using an appropriate part of a network by sampling approaches. In this paper, a new sampling algorithm based on distributed learning automata has been proposed for sampling from complex networks. In the proposed algorithm, a set of distributed learning automata cooperate with each other in order to take appropriate samples from the given network. To investigate the performance of the proposed algorithm, several simulation experiments are conducted on well-known complex networks. Experimental results are compared with several sampling methods in terms of different measures. The experimental results demonstrate the superiority of the proposed algorithm over the others.

Robustness and percolation of holes in complex networks

NASA Astrophysics Data System (ADS)

Zhou, Andu; Maletić, Slobodan; Zhao, Yi

2018-07-01

Efficient robustness and fault tolerance of complex network is significantly influenced by its connectivity, commonly modeled by the structure of pairwise relations between network elements, i.e., nodes. Nevertheless, aggregations of nodes build higher-order structures embedded in complex network, which may be more vulnerable when the fraction of nodes is removed. The structure of higher-order aggregations of nodes can be naturally modeled by simplicial complexes, whereas the removal of nodes affects the values of topological invariants, like the number of higher-dimensional holes quantified with Betti numbers. Following the methodology of percolation theory, as the fraction of nodes is removed, new holes appear, which have the role of merger between already present holes. In the present article, relationship between the robustness and homological properties of complex network is studied, through relating the graph-theoretical signatures of robustness and the quantities derived from topological invariants. The simulation results of random failures and intentional attacks on networks suggest that the changes of graph-theoretical signatures of robustness are followed by differences in the distribution of number of holes per cluster under different attack strategies. In the broader sense, the results indicate the importance of topological invariants research for obtaining further insights in understanding dynamics taking place over complex networks.

Industrial entrepreneurial network: Structural and functional analysis

NASA Astrophysics Data System (ADS)

Medvedeva, M. A.; Davletbaev, R. H.; Berg, D. B.; Nazarova, J. J.; Parusheva, S. S.

2016-12-01

Structure and functioning of two model industrial entrepreneurial networks are investigated in the present paper. One of these networks is forming when implementing an integrated project and consists of eight agents, which interact with each other and external environment. The other one is obtained from the municipal economy and is based on the set of the 12 real business entities. Analysis of the networks is carried out on the basis of the matrix of mutual payments aggregated over the certain time period. The matrix is created by the methods of experimental economics. Social Network Analysis (SNA) methods and instruments were used in the present research. The set of basic structural characteristics was investigated: set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. They were compared with the random Bernoulli graphs of the corresponding size and density. Discovered variations of random and entrepreneurial networks structure are explained by the peculiarities of agents functioning in production network. Separately, were identified the closed exchange circuits (cyclically closed contours of graph) forming an autopoietic (self-replicating) network pattern. The purpose of the functional analysis was to identify the contribution of the autopoietic network pattern in its gross product. It was found that the magnitude of this contribution is more than 20%. Such value allows using of the complementary currency in order to stimulate economic activity of network agents.

Laplacian Estrada and normalized Laplacian Estrada indices of evolving graphs.

PubMed

Shang, Yilun

2015-01-01

Large-scale time-evolving networks have been generated by many natural and technological applications, posing challenges for computation and modeling. Thus, it is of theoretical and practical significance to probe mathematical tools tailored for evolving networks. In this paper, on top of the dynamic Estrada index, we study the dynamic Laplacian Estrada index and the dynamic normalized Laplacian Estrada index of evolving graphs. Using linear algebra techniques, we established general upper and lower bounds for these graph-spectrum-based invariants through a couple of intuitive graph-theoretic measures, including the number of vertices or edges. Synthetic random evolving small-world networks are employed to show the relevance of the proposed dynamic Estrada indices. It is found that neither the static snapshot graphs nor the aggregated graph can approximate the evolving graph itself, indicating the fundamental difference between the static and dynamic Estrada indices.

Abnormal brain white matter network in young smokers: a graph theory analysis study.

PubMed

Zhang, Yajuan; Li, Min; Wang, Ruonan; Bi, Yanzhi; Li, Yangding; Yi, Zhang; Liu, Jixin; Yu, Dahua; Yuan, Kai

2018-04-01

Previous diffusion tensor imaging (DTI) studies had investigated the white matter (WM) integrity abnormalities in some specific fiber bundles in smokers. However, little is known about the changes in topological organization of WM structural network in young smokers. In current study, we acquired DTI datasets from 58 male young smokers and 51 matched nonsmokers and constructed the WM networks by the deterministic fiber tracking approach. Graph theoretical analysis was used to compare the topological parameters of WM network (global and nodal) and the inter-regional fractional anisotropy (FA) weighted WM connections between groups. The results demonstrated that both young smokers and nonsmokers had small-world topology in WM network. Further analysis revealed that the young smokers exhibited the abnormal topological organization, i.e., increased network strength, global efficiency, and decreased shortest path length. In addition, the increased nodal efficiency predominately was located in frontal cortex, striatum and anterior cingulate gyrus (ACG) in smokers. Moreover, based on network-based statistic (NBS) approach, the significant increased FA-weighted WM connections were mainly found in the PFC, ACG and supplementary motor area (SMA) regions. Meanwhile, the network parameters were correlated with the nicotine dependence severity (FTND) scores, and the nodal efficiency of orbitofrontal cortex was positive correlation with the cigarette per day (CPD) in young smokers. We revealed the abnormal topological organization of WM network in young smokers, which may improve our understanding of the neural mechanism of young smokers form WM topological organization level.

On Wiener polarity index of bicyclic networks.

PubMed

Ma, Jing; Shi, Yongtang; Wang, Zhen; Yue, Jun

2016-01-11

Complex networks are ubiquitous in biological, physical and social sciences. Network robustness research aims at finding a measure to quantify network robustness. A number of Wiener type indices have recently been incorporated as distance-based descriptors of complex networks. Wiener type indices are known to depend both on the network's number of nodes and topology. The Wiener polarity index is also related to the cluster coefficient of networks. In this paper, based on some graph transformations, we determine the sharp upper bound of the Wiener polarity index among all bicyclic networks. These bounds help to understand the underlying quantitative graph measures in depth.

Network-based Arbitrated Quantum Signature Scheme with Graph State

NASA Astrophysics Data System (ADS)

Ma, Hongling; Li, Fei; Mao, Ningyi; Wang, Yijun; Guo, Ying

2017-08-01

Implementing an arbitrated quantum signature(QAS) through complex networks is an interesting cryptography technology in the literature. In this paper, we propose an arbitrated quantum signature for the multi-user-involved networks, whose topological structures are established by the encoded graph state. The determinative transmission of the shared keys, is enabled by the appropriate stabilizers performed on the graph state. The implementation of this scheme depends on the deterministic distribution of the multi-user-shared graph state on which the encoded message can be processed in signing and verifying phases. There are four parties involved, the signatory Alice, the verifier Bob, the arbitrator Trent and Dealer who assists the legal participants in the signature generation and verification. The security is guaranteed by the entanglement of the encoded graph state which is cooperatively prepared by legal participants in complex quantum networks.

Visual traffic jam analysis based on trajectory data.

PubMed

Wang, Zuchao; Lu, Min; Yuan, Xiaoru; Zhang, Junping; van de Wetering, Huub

2013-12-01

In this work, we present an interactive system for visual analysis of urban traffic congestion based on GPS trajectories. For these trajectories we develop strategies to extract and derive traffic jam information. After cleaning the trajectories, they are matched to a road network. Subsequently, traffic speed on each road segment is computed and traffic jam events are automatically detected. Spatially and temporally related events are concatenated in, so-called, traffic jam propagation graphs. These graphs form a high-level description of a traffic jam and its propagation in time and space. Our system provides multiple views for visually exploring and analyzing the traffic condition of a large city as a whole, on the level of propagation graphs, and on road segment level. Case studies with 24 days of taxi GPS trajectories collected in Beijing demonstrate the effectiveness of our system.

Quasirandom geometric networks from low-discrepancy sequences

NASA Astrophysics Data System (ADS)

Estrada, Ernesto

2017-08-01

We define quasirandom geometric networks using low-discrepancy sequences, such as Halton, Sobol, and Niederreiter. The networks are built in d dimensions by considering the d -tuples of digits generated by these sequences as the coordinates of the vertices of the networks in a d -dimensional Id unit hypercube. Then, two vertices are connected by an edge if they are at a distance smaller than a connection radius. We investigate computationally 11 network-theoretic properties of two-dimensional quasirandom networks and compare them with analogous random geometric networks. We also study their degree distribution and their spectral density distributions. We conclude from this intensive computational study that in terms of the uniformity of the distribution of the vertices in the unit square, the quasirandom networks look more random than the random geometric networks. We include an analysis of potential strategies for generating higher-dimensional quasirandom networks, where it is know that some of the low-discrepancy sequences are highly correlated. In this respect, we conclude that up to dimension 20, the use of scrambling, skipping and leaping strategies generate quasirandom networks with the desired properties of uniformity. Finally, we consider a diffusive process taking place on the nodes and edges of the quasirandom and random geometric graphs. We show that the diffusion time is shorter in the quasirandom graphs as a consequence of their larger structural homogeneity. In the random geometric graphs the diffusion produces clusters of concentration that make the process more slow. Such clusters are a direct consequence of the heterogeneous and irregular distribution of the nodes in the unit square in which the generation of random geometric graphs is based on.

Construction of road network vulnerability evaluation index based on general travel cost

NASA Astrophysics Data System (ADS)

Leng, Jun-qiang; Zhai, Jing; Li, Qian-wen; Zhao, Lin

2018-03-01

With the development of China's economy and the continuous improvement of her urban road network, the vulnerability of the urban road network has attracted increasing attention. Based on general travel cost, this work constructs the vulnerability evaluation index for the urban road network, and evaluates the vulnerability of the urban road network from the perspective of user generalised travel cost. Firstly, the generalised travel cost model is constructed based on vehicle cost, travel time, and traveller comfort. Then, the network efficiency index is selected as an evaluation index of vulnerability: the network efficiency index is composed of the traffic volume and the generalised travel cost, which are obtained from the equilibrium state of the network. In addition, the research analyses the influence of traffic capacity decrease, road section attribute value, and location of road section, on vulnerability. Finally, the vulnerability index is used to analyse the local area network of Harbin and verify its applicability.

From brain topography to brain topology: relevance of graph theory to functional neuroscience.

PubMed

Minati, Ludovico; Varotto, Giulia; D'Incerti, Ludovico; Panzica, Ferruccio; Chan, Dennis

2013-07-10

Although several brain regions show significant specialization, higher functions such as cross-modal information integration, abstract reasoning and conscious awareness are viewed as emerging from interactions across distributed functional networks. Analytical approaches capable of capturing the properties of such networks can therefore enhance our ability to make inferences from functional MRI, electroencephalography and magnetoencephalography data. Graph theory is a branch of mathematics that focuses on the formal modelling of networks and offers a wide range of theoretical tools to quantify specific features of network architecture (topology) that can provide information complementing the anatomical localization of areas responding to given stimuli or tasks (topography). Explicit modelling of the architecture of axonal connections and interactions among areas can furthermore reveal peculiar topological properties that are conserved across diverse biological networks, and highly sensitive to disease states. The field is evolving rapidly, partly fuelled by computational developments that enable the study of connectivity at fine anatomical detail and the simultaneous interactions among multiple regions. Recent publications in this area have shown that graph-based modelling can enhance our ability to draw causal inferences from functional MRI experiments, and support the early detection of disconnection and the modelling of pathology spread in neurodegenerative disease, particularly Alzheimer's disease. Furthermore, neurophysiological studies have shown that network topology has a profound link to epileptogenesis and that connectivity indices derived from graph models aid in modelling the onset and spread of seizures. Graph-based analyses may therefore significantly help understand the bases of a range of neurological conditions. This review is designed to provide an overview of graph-based analyses of brain connectivity and their relevance to disease aimed principally at general neuroscientists and clinicians.

Community structure and scale-free collections of Erdős-Rényi graphs.

PubMed

Seshadhri, C; Kolda, Tamara G; Pinar, Ali

2012-05-01

Community structure plays a significant role in the analysis of social networks and similar graphs, yet this structure is little understood and not well captured by most models. We formally define a community to be a subgraph that is internally highly connected and has no deeper substructure. We use tools of combinatorics to show that any such community must contain a dense Erdős-Rényi (ER) subgraph. Based on mathematical arguments, we hypothesize that any graph with a heavy-tailed degree distribution and community structure must contain a scale-free collection of dense ER subgraphs. These theoretical observations corroborate well with empirical evidence. From this, we propose the Block Two-Level Erdős-Rényi (BTER) model, and demonstrate that it accurately captures the observable properties of many real-world social networks.

Generic patterns in the evolution of urban water networks: Evidence from a large Asian city

NASA Astrophysics Data System (ADS)

Krueger, Elisabeth; Klinkhamer, Christopher; Urich, Christian; Zhan, Xianyuan; Rao, P. Suresh C.

2017-03-01

We examine high-resolution urban infrastructure data using every pipe for the water distribution network (WDN) and sanitary sewer network (SSN) in a large Asian city (≈4 million residents) to explore the structure as well as the spatial and temporal evolution of these infrastructure networks. Network data were spatially disaggregated into multiple subnets to examine intracity topological differences for functional zones of the WDN and SSN, and time-stamped SSN data were examined to understand network evolution over several decades as the city expanded. Graphs were generated using a dual-mapping technique (Hierarchical Intersection Continuity Negotiation), which emphasizes the functional attributes of these networks. Network graphs for WDNs and SSNs are characterized by several network topological metrics, and a double Pareto (power-law) model approximates the node-degree distributions of both water infrastructure networks (WDN and SSN), across spatial and hierarchical scales relevant to urban settings, and throughout their temporal evolution over several decades. These results indicate that generic mechanisms govern the networks' evolution, similar to those of scale-free networks found in nature. Deviations from the general topological patterns are indicative of (1) incomplete establishment of network hierarchies and functional network evolution, (2) capacity for growth (expansion) or densification (e.g., in-fill), and (3) likely network vulnerabilities. We discuss the implications of our findings for the (re-)design of urban infrastructure networks to enhance their resilience to external and internal threats.

Network Analysis of Intrinsic Functional Brain Connectivity in Male and Female Adult Smokers: A Preliminary Study.

PubMed

Moran-Santa Maria, Megan M; Vanderweyen, Davy C; Camp, Christopher C; Zhu, Xun; McKee, Sherry A; Cosgrove, Kelly P; Hartwell, Karen J; Brady, Kathleen T; Joseph, Jane E

2018-06-07

The goal of this study was to conduct a preliminary network analysis (using graph-theory measures) of intrinsic functional connectivity in adult smokers, with an exploration of sex differences in smokers. Twenty-seven adult smokers (13 males; mean age = 35) and 17 sex and age-matched controls (11 males; mean age = 35) completed a blood oxygen level-dependent resting state functional magnetic resonance imaging experiment. Data analysis involved preprocessing, creation of connectivity matrices using partial correlation, and computation of graph-theory measures using the Brain Connectivity Toolbox. Connector hubs and additional graph-theory measures were examined for differences between smokers and controls and correlations with nicotine dependence. Sex differences were examined in a priori regions of interest based on prior literature. Compared to nonsmokers, connector hubs in smokers emerged primarily in limbic (parahippocampus) and salience network (cingulate cortex) regions. In addition, global influence of the right insula and left nucleus accumbens was associated with higher nicotine dependence. These trends were present in male but not female smokers. Network communication was altered in smokers, primarily in limbic and salience network regions. Network topology was associated with nicotine dependence in male but not female smokers in regions associated with reinforcement (nucleus accumbens) and craving (insula), consistent with the idea that male smokers are more sensitive to the reinforcing aspects of nicotine than female smokers. Identifying alterations in brain network communication in male and female smokers can help tailor future behavioral and pharmacological smoking interventions. Male smokers showed alterations in brain networks associated with the reinforcing effects of nicotine more so than females, suggesting that pharmacotherapies targeting reinforcement and craving may be more efficacious in male smokers.

An extensive assessment of network alignment algorithms for comparison of brain connectomes.

PubMed

Milano, Marianna; Guzzi, Pietro Hiram; Tymofieva, Olga; Xu, Duan; Hess, Christofer; Veltri, Pierangelo; Cannataro, Mario

2017-06-06

Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem. In this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset. The results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.

Virtual terrain: a security-based representation of a computer network

NASA Astrophysics Data System (ADS)

Holsopple, Jared; Yang, Shanchieh; Argauer, Brian

2008-03-01

Much research has been put forth towards detection, correlating, and prediction of cyber attacks in recent years. As this set of research progresses, there is an increasing need for contextual information of a computer network to provide an accurate situational assessment. Typical approaches adopt contextual information as needed; yet such ad hoc effort may lead to unnecessary or even conflicting features. The concept of virtual terrain is, therefore, developed and investigated in this work. Virtual terrain is a common representation of crucial information about network vulnerabilities, accessibilities, and criticalities. A virtual terrain model encompasses operating systems, firewall rules, running services, missions, user accounts, and network connectivity. It is defined as connected graphs with arc attributes defining dynamic relationships among vertices modeling network entities, such as services, users, and machines. The virtual terrain representation is designed to allow feasible development and maintenance of the model, as well as efficacy in terms of the use of the model. This paper will describe the considerations in developing the virtual terrain schema, exemplary virtual terrain models, and algorithms utilizing the virtual terrain model for situation and threat assessment.

A Single Session of rTMS Enhances Small-Worldness in Writer's Cramp: Evidence from Simultaneous EEG-fMRI Multi-Modal Brain Graph.

PubMed

Bharath, Rose D; Panda, Rajanikant; Reddam, Venkateswara Reddy; Bhaskar, M V; Gohel, Suril; Bhardwaj, Sujas; Prajapati, Arvind; Pal, Pramod Kumar

2017-01-01

Background and Purpose : Repetitive transcranial magnetic stimulation (rTMS) induces widespread changes in brain connectivity. As the network topology differences induced by a single session of rTMS are less known we undertook this study to ascertain whether the network alterations had a small-world morphology using multi-modal graph theory analysis of simultaneous EEG-fMRI. Method : Simultaneous EEG-fMRI was acquired in duplicate before (R1) and after (R2) a single session of rTMS in 14 patients with Writer's Cramp (WC). Whole brain neuronal and hemodynamic network connectivity were explored using the graph theory measures and clustering coefficient, path length and small-world index were calculated for EEG and resting state fMRI (rsfMRI). Multi-modal graph theory analysis was used to evaluate the correlation of EEG and fMRI clustering coefficients. Result : A single session of rTMS was found to increase the clustering coefficient and small-worldness significantly in both EEG and fMRI ( p < 0.05). Multi-modal graph theory analysis revealed significant modulations in the fronto-parietal regions immediately after rTMS. The rsfMRI revealed additional modulations in several deep brain regions including cerebellum, insula and medial frontal lobe. Conclusion : Multi-modal graph theory analysis of simultaneous EEG-fMRI can supplement motor physiology methods in understanding the neurobiology of rTMS in vivo . Coinciding evidence from EEG and rsfMRI reports small-world morphology for the acute phase network hyper-connectivity indicating changes ensuing low-frequency rTMS is probably not "noise".

An industrial robot singular trajectories planning based on graphs and neural networks

NASA Astrophysics Data System (ADS)

Łęgowski, Adrian; Niezabitowski, Michał

2016-06-01

Singular trajectories are rarely used because of issues during realization. A method of planning trajectories for given set of points in task space with use of graphs and neural networks is presented. In every desired point the inverse kinematics problem is solved in order to derive all possible solutions. A graph of solutions is made. The shortest path is determined to define required nodes in joint space. Neural networks are used to define the path between these nodes.

Grid Transmission Expansion Planning Model Based on Grid Vulnerability

NASA Astrophysics Data System (ADS)

Tang, Quan; Wang, Xi; Li, Ting; Zhang, Quanming; Zhang, Hongli; Li, Huaqiang

2018-03-01

Based on grid vulnerability and uniformity theory, proposed global network structure and state vulnerability factor model used to measure different grid models. established a multi-objective power grid planning model which considering the global power network vulnerability, economy and grid security constraint. Using improved chaos crossover and mutation genetic algorithm to optimize the optimal plan. For the problem of multi-objective optimization, dimension is not uniform, the weight is not easy given. Using principal component analysis (PCA) method to comprehensive assessment of the population every generation, make the results more objective and credible assessment. the feasibility and effectiveness of the proposed model are validated by simulation results of Garver-6 bus system and Garver-18 bus.

A Survey of Visualization Tools Assessed for Anomaly-Based Intrusion Detection Analysis

DTIC Science & Technology

2014-04-01

objective? • What vulnerabilities exist in the target system? • What damage or other consequences are likely? • What exploit scripts or other attack...languages C, R, and Python; no response capabilities. JUNG https://blogs.reucon.com/asterisk- java /tag/visualization/ Create custom layouts and can...annotate graphs, links, nodes with any Java data type. Must be familiar with coding in Java to call the routines; no monitoring or response

Reproducibility of graph metrics of human brain structural networks.

PubMed

Duda, Jeffrey T; Cook, Philip A; Gee, James C

2014-01-01

Recent interest in human brain connectivity has led to the application of graph theoretical analysis to human brain structural networks, in particular white matter connectivity inferred from diffusion imaging and fiber tractography. While these methods have been used to study a variety of patient populations, there has been less examination of the reproducibility of these methods. A number of tractography algorithms exist and many of these are known to be sensitive to user-selected parameters. The methods used to derive a connectivity matrix from fiber tractography output may also influence the resulting graph metrics. Here we examine how these algorithm and parameter choices influence the reproducibility of proposed graph metrics on a publicly available test-retest dataset consisting of 21 healthy adults. The dice coefficient is used to examine topological similarity of constant density subgraphs both within and between subjects. Seven graph metrics are examined here: mean clustering coefficient, characteristic path length, largest connected component size, assortativity, global efficiency, local efficiency, and rich club coefficient. The reproducibility of these network summary measures is examined using the intraclass correlation coefficient (ICC). Graph curves are created by treating the graph metrics as functions of a parameter such as graph density. Functional data analysis techniques are used to examine differences in graph measures that result from the choice of fiber tracking algorithm. The graph metrics consistently showed good levels of reproducibility as measured with ICC, with the exception of some instability at low graph density levels. The global and local efficiency measures were the most robust to the choice of fiber tracking algorithm.

Network analysis reveals disrupted functional brain circuitry in drug-naive social anxiety disorder.

PubMed

Yang, Xun; Liu, Jin; Meng, Yajing; Xia, Mingrui; Cui, Zaixu; Wu, Xi; Hu, Xinyu; Zhang, Wei; Gong, Gaolang; Gong, Qiyong; Sweeney, John A; He, Yong

2017-12-07

Social anxiety disorder (SAD) is a common and disabling condition characterized by excessive fear and avoidance of public scrutiny. Psychoradiology studies have suggested that the emotional and behavior deficits in SAD are associated with abnormalities in regional brain function and functional connectivity. However, little is known about whether intrinsic functional brain networks in patients with SAD are topologically disrupted. Here, we collected resting-state fMRI data from 33 drug-naive patients with SAD and 32 healthy controls (HC), constructed functional networks with 34 predefined regions based on previous meta-analytic research with task-based fMRI in SAD, and performed network-based statistic and graph-theory analyses. The network-based statistic analysis revealed a single connected abnormal circuitry including the frontolimbic circuit (termed the "fear circuit", including the dorsolateral prefrontal cortex, ventral medial prefrontal cortex and insula) and posterior cingulate/occipital areas supporting perceptual processing. In this single altered network, patients with SAD had higher functional connectivity than HC. At the global level, graph-theory analysis revealed that the patients exhibited a lower normalized characteristic path length than HC, which suggests a disorder-related shift of network topology toward randomized configurations. SAD-related deficits in nodal degree, efficiency and participation coefficient were detected in the parahippocampal gyrus, posterior cingulate cortex, dorsolateral prefrontal cortex, insula and the calcarine sulcus. Aspects of abnormal connectivity were associated with anxiety symptoms. These findings highlight the aberrant topological organization of functional brain network organization in SAD, which provides insights into the neural mechanisms underlying excessive fear and avoidance of social interactions in patients with debilitating social anxiety. Copyright © 2017. Published by Elsevier Inc.

Novel presentational approaches were developed for reporting network meta-analysis.

PubMed

Tan, Sze Huey; Cooper, Nicola J; Bujkiewicz, Sylwia; Welton, Nicky J; Caldwell, Deborah M; Sutton, Alexander J

2014-06-01

To present graphical tools for reporting network meta-analysis (NMA) results aiming to increase the accessibility, transparency, interpretability, and acceptability of NMA analyses. The key components of NMA results were identified based on recommendations by agencies such as the National Institute for Health and Care Excellence (United Kingdom). Three novel graphs were designed to amalgamate the identified components using familiar graphical tools such as the bar, line, or pie charts and adhering to good graphical design principles. Three key components for presentation of NMA results were identified, namely relative effects and their uncertainty, probability of an intervention being best, and between-study heterogeneity. Two of the three graphs developed present results (for each pairwise comparison of interventions in the network) obtained from both NMA and standard pairwise meta-analysis for easy comparison. They also include options to display the probability best, ranking statistics, heterogeneity, and prediction intervals. The third graph presents rankings of interventions in terms of their effectiveness to enable clinicians to easily identify "top-ranking" interventions. The graphical tools presented can display results tailored to the research question of interest, and targeted at a whole spectrum of users from the technical analyst to the nontechnical clinician. Copyright © 2014 Elsevier Inc. All rights reserved.

Abnormal functional global and local brain connectivity in female patients with anorexia nervosa

PubMed Central

Geisler, Daniel; Borchardt, Viola; Lord, Anton R.; Boehm, Ilka; Ritschel, Franziska; Zwipp, Johannes; Clas, Sabine; King, Joseph A.; Wolff-Stephan, Silvia; Roessner, Veit; Walter, Martin; Ehrlich, Stefan

2016-01-01

Background Previous resting-state functional connectivity studies in patients with anorexia nervosa used independent component analysis or seed-based connectivity analysis to probe specific brain networks. Instead, modelling the entire brain as a complex network allows determination of graph-theoretical metrics, which describe global and local properties of how brain networks are organized and how they interact. Methods To determine differences in network properties between female patients with acute anorexia nervosa and pairwise matched healthy controls, we used resting-state fMRI and computed well-established global and local graph metrics across a range of network densities. Results Our analyses included 35 patients and 35 controls. We found that the global functional network structure in patients with anorexia nervosa is characterized by increases in both characteristic path length (longer average routes between nodes) and assortativity (more nodes with a similar connectedness link together). Accordingly, we found locally decreased connectivity strength and increased path length in the posterior insula and thalamus. Limitations The present results may be limited to the methods applied during preprocessing and network construction. Conclusion We demonstrated anorexia nervosa–related changes in the network configuration for, to our knowledge, the first time using resting-state fMRI and graph-theoretical measures. Our findings revealed an altered global brain network architecture accompanied by local degradations indicating wide-scale disturbance in information flow across brain networks in patients with acute anorexia nervosa. Reduced local network efficiency in the thalamus and posterior insula may reflect a mechanism that helps explain the impaired integration of visuospatial and homeostatic signals in patients with this disorder, which is thought to be linked to abnormal representations of body size and hunger. PMID:26252451

Abnormal functional global and local brain connectivity in female patients with anorexia nervosa.

PubMed

Geisler, Daniel; Borchardt, Viola; Lord, Anton R; Boehm, Ilka; Ritschel, Franziska; Zwipp, Johannes; Clas, Sabine; King, Joseph A; Wolff-Stephan, Silvia; Roessner, Veit; Walter, Martin; Ehrlich, Stefan

2016-01-01

Previous resting-state functional connectivity studies in patients with anorexia nervosa used independent component analysis or seed-based connectivity analysis to probe specific brain networks. Instead, modelling the entire brain as a complex network allows determination of graph-theoretical metrics, which describe global and local properties of how brain networks are organized and how they interact. To determine differences in network properties between female patients with acute anorexia nervosa and pairwise matched healthy controls, we used resting-state fMRI and computed well-established global and local graph metrics across a range of network densities. Our analyses included 35 patients and 35 controls. We found that the global functional network structure in patients with anorexia nervosa is characterized by increases in both characteristic path length (longer average routes between nodes) and assortativity (more nodes with a similar connectedness link together). Accordingly, we found locally decreased connectivity strength and increased path length in the posterior insula and thalamus. The present results may be limited to the methods applied during preprocessing and network construction. We demonstrated anorexia nervosa-related changes in the network configuration for, to our knowledge, the first time using resting-state fMRI and graph-theoretical measures. Our findings revealed an altered global brain network architecture accompanied by local degradations indicating wide-scale disturbance in information flow across brain networks in patients with acute anorexia nervosa. Reduced local network efficiency in the thalamus and posterior insula may reflect a mechanism that helps explain the impaired integration of visuospatial and homeostatic signals in patients with this disorder, which is thought to be linked to abnormal representations of body size and hunger.

Time-dependence of graph theory metrics in functional connectivity analysis

PubMed Central

Chiang, Sharon; Cassese, Alberto; Guindani, Michele; Vannucci, Marina; Yeh, Hsiang J.; Haneef, Zulfi; Stern, John M.

2016-01-01

Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations. PMID:26518632

Time-dependence of graph theory metrics in functional connectivity analysis.

PubMed

Chiang, Sharon; Cassese, Alberto; Guindani, Michele; Vannucci, Marina; Yeh, Hsiang J; Haneef, Zulfi; Stern, John M

2016-01-15

Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations. Copyright © 2015 Elsevier Inc. All rights reserved.

Overlapping communities detection based on spectral analysis of line graphs

NASA Astrophysics Data System (ADS)

Gui, Chun; Zhang, Ruisheng; Hu, Rongjing; Huang, Guoming; Wei, Jiaxuan

2018-05-01

Community in networks are often overlapping where one vertex belongs to several clusters. Meanwhile, many networks show hierarchical structure such that community is recursively grouped into hierarchical organization. In order to obtain overlapping communities from a global hierarchy of vertices, a new algorithm (named SAoLG) is proposed to build the hierarchical organization along with detecting the overlap of community structure. SAoLG applies the spectral analysis into line graphs to unify the overlap and hierarchical structure of the communities. In order to avoid the limitation of absolute distance such as Euclidean distance, SAoLG employs Angular distance to compute the similarity between vertices. Furthermore, we make a micro-improvement partition density to evaluate the quality of community structure and use it to obtain the more reasonable and sensible community numbers. The proposed SAoLG algorithm achieves a balance between overlap and hierarchy by applying spectral analysis to edge community detection. The experimental results on one standard network and six real-world networks show that the SAoLG algorithm achieves higher modularity and reasonable community number values than those generated by Ahn's algorithm, the classical CPM and GN ones.

Empirical Reference Distributions for Networks of Different Size

PubMed Central

Smith, Anna; Calder, Catherine A.; Browning, Christopher R.

2016-01-01

Network analysis has become an increasingly prevalent research tool across a vast range of scientific fields. Here, we focus on the particular issue of comparing network statistics, i.e. graph-level measures of network structural features, across multiple networks that differ in size. Although “normalized” versions of some network statistics exist, we demonstrate via simulation why direct comparison is often inappropriate. We consider normalizing network statistics relative to a simple fully parameterized reference distribution and demonstrate via simulation how this is an improvement over direct comparison, but still sometimes problematic. We propose a new adjustment method based on a reference distribution constructed as a mixture model of random graphs which reflect the dependence structure exhibited in the observed networks. We show that using simple Bernoulli models as mixture components in this reference distribution can provide adjusted network statistics that are relatively comparable across different network sizes but still describe interesting features of networks, and that this can be accomplished at relatively low computational expense. Finally, we apply this methodology to a collection of ecological networks derived from the Los Angeles Family and Neighborhood Survey activity location data. PMID:27721556

Analysis of Community Detection Algorithms for Large Scale Cyber Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mane, Prachita; Shanbhag, Sunanda; Kamath, Tanmayee

The aim of this project is to use existing community detection algorithms on an IP network dataset to create supernodes within the network. This study compares the performance of different algorithms on the network in terms of running time. The paper begins with an introduction to the concept of clustering and community detection followed by the research question that the team aimed to address. Further the paper describes the graph metrics that were considered in order to shortlist algorithms followed by a brief explanation of each algorithm with respect to the graph metric on which it is based. The nextmore » section in the paper describes the methodology used by the team in order to run the algorithms and determine which algorithm is most efficient with respect to running time. Finally, the last section of the paper includes the results obtained by the team and a conclusion based on those results as well as future work.« less

Community detection enhancement using non-negative matrix factorization with graph regularization

NASA Astrophysics Data System (ADS)

Liu, Xiao; Wei, Yi-Ming; Wang, Jian; Wang, Wen-Jun; He, Dong-Xiao; Song, Zhan-Jie

2016-06-01

Community detection is a meaningful task in the analysis of complex networks, which has received great concern in various domains. A plethora of exhaustive studies has made great effort and proposed many methods on community detection. Particularly, a kind of attractive one is the two-step method which first makes a preprocessing for the network and then identifies its communities. However, not all types of methods can achieve satisfactory results by using such preprocessing strategy, such as the non-negative matrix factorization (NMF) methods. In this paper, rather than using the above two-step method as most works did, we propose a graph regularized-based model to improve, specialized, the NMF-based methods for the detection of communities, namely NMFGR. In NMFGR, we introduce the similarity metric which contains both the global and local information of networks, to reflect the relationships between two nodes, so as to improve the accuracy of community detection. Experimental results on both artificial and real-world networks demonstrate the superior performance of NMFGR to some competing methods.

X-Graphs: Language and Algorithms for Heterogeneous Graph Streams

DTIC Science & Technology

2017-09-01

INTRODUCTION 1 3 METHODS , ASUMPTIONS, AND PROCEDURES 2 Software Abstractions for Graph Analytic Applications 2 High performance Platforms for Graph Processing...data is stored in a distributed file system. 3 METHODS , ASUMPTIONS, AND PROCEDURES Software Abstractions for Graph Analytic Applications To...implementations of novel methods for networks analysis: several methods for detection of overlapping communities, personalized PageRank, node embeddings into a d

Resilience of developing brain networks to interictal epileptiform discharges is associated with cognitive outcome.

PubMed

Ibrahim, George M; Cassel, Daniel; Morgan, Benjamin R; Smith, Mary Lou; Otsubo, Hiroshi; Ochi, Ayako; Taylor, Margot; Rutka, James T; Snead, O Carter; Doesburg, Sam

2014-10-01

The effects of interictal epileptiform discharges on neurocognitive development in children with medically-intractable epilepsy are poorly understood. Such discharges may have a deleterious effect on the brain's intrinsic connectivity networks, which reflect the organization of functional networks at rest, and in turn on neurocognitive development. Using a combined functional magnetic resonance imaging-magnetoencephalography approach, we examine the effects of interictal epileptiform discharges on intrinsic connectivity networks and neurocognitive outcome. Functional magnetic resonance imaging was used to determine the location of regions comprising various intrinsic connectivity networks in 26 children (7-17 years), and magnetoencephalography data were reconstructed from these locations. Inter-regional phase synchronization was then calculated across interictal epileptiform discharges and graph theoretical analysis was applied to measure event-related changes in network topology in the peri-discharge period. The magnitude of change in network topology (network resilience/vulnerability) to interictal epileptiform discharges was associated with neurocognitive outcomes and functional magnetic resonance imaging networks using dual regression. Three main findings are reported: (i) large-scale network changes precede and follow interictal epileptiform discharges; (ii) the resilience of network topologies to interictal discharges is associated with stronger resting-state network connectivity; and (iii) vulnerability to interictal discharges is associated with worse neurocognitive outcomes. By combining the spatial resolution of functional magnetic resonance imaging with the temporal resolution of magnetoencephalography, we describe the effects of interictal epileptiform discharges on neurophysiological synchrony in intrinsic connectivity networks and establish the impact of interictal disruption of functional networks on cognitive outcome in children with epilepsy. The association between interictal discharges, network changes and neurocognitive outcomes suggests that it is of clinical importance to suppress discharges to foster more typical brain network development in children with focal epilepsy. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Scale-space measures for graph topology link protein network architecture to function.

PubMed

Hulsman, Marc; Dimitrakopoulos, Christos; de Ridder, Jeroen

2014-06-15

The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale). In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels. We apply these to characterize the topology of networks across all scales simultaneously, generating a so-called graph topological scale-space. The comprehensive physical interaction network in yeast is used to show that scale-space based measures consistently give superior performance when distinguishing protein functional categories and three major types of functional interactions-genetic interaction, co-expression and perturbation interactions. Moreover, we demonstrate that graph topological scale spaces capture biologically meaningful features that provide new insights into the link between function and protein network architecture. Matlab(TM) code to calculate the scale-aware topological measures (STMs) is available at http://bioinformatics.tudelft.nl/TSSA © The Author 2014. Published by Oxford University Press.

On the design of a hierarchical SS7 network: A graph theoretical approach

NASA Astrophysics Data System (ADS)

Krauss, Lutz; Rufa, Gerhard

1994-04-01

This contribution is concerned with the design of Signaling System No. 7 networks based on graph theoretical methods. A hierarchical network topology is derived by combining the advantage of the hierarchical network structure with the realization of node disjoint routes between nodes of the network. By using specific features of this topology, we develop an algorithm to construct circle-free routing data and to assure bidirectionality also in case of failure situations. The methods described are based on the requirements that the network topology, as well as the routing data, may be easily changed.

Diffusion tractography and graph theory analysis reveal the disrupted rich-club organization of white matter structural networks in early Tourette Syndrome children

NASA Astrophysics Data System (ADS)

Wen, Hongwei; Liu, Yue; Wang, Shengpei; Zhang, Jishui; Peng, Yun; He, Huiguang

2017-03-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. At present, the topological disruptions of the whole brain white matter (WM) structural networks remain poorly understood in TS children. Considering the unique position of the topologically central role of densely interconnected brain hubs, namely the rich club regions, therefore, we aimed to investigate whether the rich club regions and their related connections would be particularly vulnerable in early TS children. In our study, we used diffusion tractography and graph theoretical analyses to explore the rich club structures in 44 TS children and 48 healthy children. The structural networks of TS children exhibited significantly increased normalized rich club coefficient, suggesting that TS is characterized by increased structural integrity of this centrally embedded rich club backbone, potentially resulting in increased global communication capacity. In addition, TS children showed a reorganization of rich club regions, as well as significantly increased density and decreased number in feeder connections. Furthermore, the increased rich club coefficients and feeder connections density of TS children were significantly positively correlated to tic severity, indicating that TS may be characterized by a selective alteration of the structural connectivity of the rich club regions, tending to have higher bridging with non-rich club regions, which may increase the integration among tic-related brain circuits with more excitability but less inhibition for information exchanges between highly centered brain regions and peripheral areas. In all, our results suggest the disrupted rich club organization in early TS children and provide structural insights into the brain networks.

Connectome analysis for pre-operative brain mapping in neurosurgery

PubMed Central

Hart, Michael G.; Price, Stephen J.; Suckling, John

2016-01-01

Abstract Object: Brain mapping has entered a new era focusing on complex network connectivity. Central to this is the search for the connectome or the brains ‘wiring diagram’. Graph theory analysis of the connectome allows understanding of the importance of regions to network function, and the consequences of their impairment or excision. Our goal was to apply connectome analysis in patients with brain tumours to characterise overall network topology and individual patterns of connectivity alterations. Methods: Resting-state functional MRI data were acquired using multi-echo, echo planar imaging pre-operatively from five participants each with a right temporal–parietal–occipital glioblastoma. Complex networks analysis was initiated by parcellating the brain into anatomically regions amongst which connections were identified by retaining the most significant correlations between the respective wavelet decomposed time-series. Results: Key characteristics of complex networks described in healthy controls were preserved in these patients, including ubiquitous small world organization. An exponentially truncated power law fit to the degree distribution predicted findings of general network robustness to injury but with a core of hubs exhibiting disproportionate vulnerability. Tumours produced a consistent reduction in local and long-range connectivity with distinct patterns of connection loss depending on lesion location. Conclusions: Connectome analysis is a feasible and novel approach to brain mapping in individual patients with brain tumours. Applications to pre-surgical planning include identifying regions critical to network function that should be preserved and visualising connections at risk from tumour resection. In the future one could use such data to model functional plasticity and recovery of cognitive deficits. PMID:27447756

Alterations of brain network hubs in reflex syncope: Evidence from a graph theoretical analysis based on DTI.

PubMed

Park, Bong Soo; Lee, Yoo Jin; Park, Jin-Han; Kim, Il Hwan; Park, Si Hyung; Lee, Ho-Joon; Park, Kang Min

2018-06-01

We evaluated global topology and organization of regional hubs in the brain networks and microstructural abnormalities in the white matter of patients with reflex syncope. Twenty patients with reflex syncope and thirty healthy subjects were recruited, and they underwent diffusion tensor imaging (DTI) scans. Graph theory was applied to obtain network measures based on extracted DTI data, using DSI Studio. We then investigated differences in the network measures between the patients with reflex syncope and the healthy subjects. We also analyzed microstructural abnormalities of white matter using tract-based spatial statistics analysis (TBSS). Measures of global topology were not different between patients with reflex syncope and healthy subjects. However, in reflex syncope patients, the strength measures of the right angular, left inferior frontal, left middle orbitofrontal, left superior medial frontal, and left middle temporal gyrus were lower than in healthy subjects. The betweenness centrality measures of the left middle orbitofrontal, left fusiform, and left lingual gyrus in patients were lower than those in healthy subjects. The PageRank centrality measures of the right angular, left middle orbitofrontal, and left superior medial frontal gyrus in patients were lower than those in healthy subjects. Regarding the analysis of the white matter microstructure, there were no differences in the fractional anisotropy and mean diffusivity values between the two groups. We have identified a reorganization of network hubs in the brain network of patients with reflex syncope. These alterations in brain network may play a role in the pathophysiologic mechanism underlying reflex syncope. © 2018 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.

Gene function prediction with gene interaction networks: a context graph kernel approach.

PubMed

Li, Xin; Chen, Hsinchun; Li, Jiexun; Zhang, Zhu

2010-01-01

Predicting gene functions is a challenge for biologists in the postgenomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.

Modeling flow and transport in fracture networks using graphs

NASA Astrophysics Data System (ADS)

Karra, S.; O'Malley, D.; Hyman, J. D.; Viswanathan, H. S.; Srinivasan, G.

2018-03-01

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizations of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. The good accuracy and the low computational cost, with O (104) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.

Modeling flow and transport in fracture networks using graphs.

PubMed

Karra, S; O'Malley, D; Hyman, J D; Viswanathan, H S; Srinivasan, G

2018-03-01

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizations of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. The good accuracy and the low computational cost, with O(10^{4}) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.

Modeling flow and transport in fracture networks using graphs

DOE PAGES

Karra, S.; O'Malley, D.; Hyman, J. D.; ...

2018-03-09

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizationsmore » of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. In conclusion, the good accuracy and the low computational cost, with O(10 4) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.« less

Modeling flow and transport in fracture networks using graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karra, S.; O'Malley, D.; Hyman, J. D.

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizationsmore » of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. In conclusion, the good accuracy and the low computational cost, with O(10 4) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.« less

SBEToolbox: A Matlab Toolbox for Biological Network Analysis

PubMed Central

Konganti, Kranti; Wang, Gang; Yang, Ence; Cai, James J.

2013-01-01

We present SBEToolbox (Systems Biology and Evolution Toolbox), an open-source Matlab toolbox for biological network analysis. It takes a network file as input, calculates a variety of centralities and topological metrics, clusters nodes into modules, and displays the network using different graph layout algorithms. Straightforward implementation and the inclusion of high-level functions allow the functionality to be easily extended or tailored through developing custom plugins. SBEGUI, a menu-driven graphical user interface (GUI) of SBEToolbox, enables easy access to various network and graph algorithms for programmers and non-programmers alike. All source code and sample data are freely available at https://github.com/biocoder/SBEToolbox/releases. PMID:24027418

SBEToolbox: A Matlab Toolbox for Biological Network Analysis.

PubMed

Konganti, Kranti; Wang, Gang; Yang, Ence; Cai, James J

2013-01-01

We present SBEToolbox (Systems Biology and Evolution Toolbox), an open-source Matlab toolbox for biological network analysis. It takes a network file as input, calculates a variety of centralities and topological metrics, clusters nodes into modules, and displays the network using different graph layout algorithms. Straightforward implementation and the inclusion of high-level functions allow the functionality to be easily extended or tailored through developing custom plugins. SBEGUI, a menu-driven graphical user interface (GUI) of SBEToolbox, enables easy access to various network and graph algorithms for programmers and non-programmers alike. All source code and sample data are freely available at https://github.com/biocoder/SBEToolbox/releases.

Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs)

PubMed Central

Dimitriadis, Stavros I.; Salis, Christos; Tarnanas, Ioannis; Linden, David E.

2017-01-01

The human brain is a large-scale system of functionally connected brain regions. This system can be modeled as a network, or graph, by dividing the brain into a set of regions, or “nodes,” and quantifying the strength of the connections between nodes, or “edges,” as the temporal correlation in their patterns of activity. Network analysis, a part of graph theory, provides a set of summary statistics that can be used to describe complex brain networks in a meaningful way. The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. The adaptation of both bivariate (mutual information) and multivariate (Granger causality) connectivity estimators to quantify the synchronization between multichannel recordings yields a fully connected, weighted, (a)symmetric functional connectivity graph (FCG), representing the associations among all brain areas. The aforementioned procedure leads to an extremely dense network of tens up to a few hundreds of weights. Therefore, this FCG must be filtered out so that the “true” connectivity pattern can emerge. Here, we compared a large number of well-known topological thresholding techniques with the novel proposed data-driven scheme based on orthogonal minimal spanning trees (OMSTs). OMSTs filter brain connectivity networks based on the optimization between the global efficiency of the network and the cost preserving its wiring. We demonstrated the proposed method in a large EEG database (N = 101 subjects) with eyes-open (EO) and eyes-closed (EC) tasks by adopting a time-varying approach with the main goal to extract features that can totally distinguish each subject from the rest of the set. Additionally, the reliability of the proposed scheme was estimated in a second case study of fMRI resting-state activity with multiple scans. Our results demonstrated clearly that the proposed thresholding scheme outperformed a large list of thresholding schemes based on the recognition accuracy of each subject compared to the rest of the cohort (EEG). Additionally, the reliability of the network metrics based on the fMRI static networks was improved based on the proposed topological filtering scheme. Overall, the proposed algorithm could be used across neuroimaging and multimodal studies as a common computationally efficient standardized tool for a great number of neuroscientists and physicists working on numerous of projects. PMID:28491032

STARNET 2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data

PubMed Central

Jupiter, Daniel; Chen, Hailin; VanBuren, Vincent

2009-01-01

Background Although expression microarrays have become a standard tool used by biologists, analysis of data produced by microarray experiments may still present challenges. Comparison of data from different platforms, organisms, and labs may involve complicated data processing, and inferring relationships between genes remains difficult. Results STARNET 2 is a new web-based tool that allows post hoc visual analysis of correlations that are derived from expression microarray data. STARNET 2 facilitates user discovery of putative gene regulatory networks in a variety of species (human, rat, mouse, chicken, zebrafish, Drosophila, C. elegans, S. cerevisiae, Arabidopsis and rice) by graphing networks of genes that are closely co-expressed across a large heterogeneous set of preselected microarray experiments. For each of the represented organisms, raw microarray data were retrieved from NCBI's Gene Expression Omnibus for a selected Affymetrix platform. All pairwise Pearson correlation coefficients were computed for expression profiles measured on each platform, respectively. These precompiled results were stored in a MySQL database, and supplemented by additional data retrieved from NCBI. A web-based tool allows user-specified queries of the database, centered at a gene of interest. The result of a query includes graphs of correlation networks, graphs of known interactions involving genes and gene products that are present in the correlation networks, and initial statistical analyses. Two analyses may be performed in parallel to compare networks, which is facilitated by the new HEATSEEKER module. Conclusion STARNET 2 is a useful tool for developing new hypotheses about regulatory relationships between genes and gene products, and has coverage for 10 species. Interpretation of the correlation networks is supported with a database of previously documented interactions, a test for enrichment of Gene Ontology terms, and heat maps of correlation distances that may be used to compare two networks. The list of genes in a STARNET network may be useful in developing a list of candidate genes to use for the inference of causal networks. The tool is freely available at , and does not require user registration. PMID:19828039

Graph-Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory

ERIC Educational Resources Information Center

Gruenenfelder, Thomas M.; Recchia, Gabriel; Rubin, Tim; Jones, Michael N.

2016-01-01

We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network…

Enhanced Contact Graph Routing (ECGR) MACHETE Simulation Model

NASA Technical Reports Server (NTRS)

Segui, John S.; Jennings, Esther H.; Clare, Loren P.

2013-01-01

Contact Graph Routing (CGR) for Delay/Disruption Tolerant Networking (DTN) space-based networks makes use of the predictable nature of node contacts to make real-time routing decisions given unpredictable traffic patterns. The contact graph will have been disseminated to all nodes before the start of route computation. CGR was designed for space-based networking environments where future contact plans are known or are independently computable (e.g., using known orbital dynamics). For each data item (known as a bundle in DTN), a node independently performs route selection by examining possible paths to the destination. Route computation could conceivably run thousands of times a second, so computational load is important. This work refers to the simulation software model of Enhanced Contact Graph Routing (ECGR) for DTN Bundle Protocol in JPL's MACHETE simulation tool. The simulation model was used for performance analysis of CGR and led to several performance enhancements. The simulation model was used to demonstrate the improvements of ECGR over CGR as well as other routing methods in space network scenarios. ECGR moved to using earliest arrival time because it is a global monotonically increasing metric that guarantees the safety properties needed for the solution's correctness since route re-computation occurs at each node to accommodate unpredicted changes (e.g., traffic pattern, link quality). Furthermore, using earliest arrival time enabled the use of the standard Dijkstra algorithm for path selection. The Dijkstra algorithm for path selection has a well-known inexpensive computational cost. These enhancements have been integrated into the open source CGR implementation. The ECGR model is also useful for route metric experimentation and comparisons with other DTN routing protocols particularly when combined with MACHETE's space networking models and Delay Tolerant Link State Routing (DTLSR) model.

Vulnerability Analysis and Passenger Source Prediction in Urban Rail Transit Networks

PubMed Central

Wang, Junjie; Li, Yishuai; Liu, Jingyu; He, Kun; Wang, Pu

2013-01-01

Based on large-scale human mobility data collected in San Francisco and Boston, the morning peak urban rail transit (URT) ODs (origin-destination matrix) were estimated and the most vulnerable URT segments, those capable of causing the largest service interruptions, were identified. In both URT networks, a few highly vulnerable segments were observed. For this small group of vital segments, the impact of failure must be carefully evaluated. A bipartite URT usage network was developed and used to determine the inherent connections between urban rail transits and their passengers' travel demands. Although passengers' origins and destinations were easy to locate for a large number of URT segments, a few show very complicated spatial distributions. Based on the bipartite URT usage network, a new layer of the understanding of a URT segment's vulnerability can be achieved by taking the difficulty of addressing the failure of a given segment into account. Two proof-of-concept cases are described here: Possible transfer of passenger flow to the road network is here predicted in the cases of failures of two representative URT segments in San Francisco. PMID:24260355

Graph processing platforms at scale: practices and experiences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lim, Seung-Hwan; Lee, Sangkeun; Brown, Tyler C

2015-01-01

Graph analysis unveils hidden associations of data in many phenomena and artifacts, such as road network, social networks, genomic information, and scientific collaboration. Unfortunately, a wide diversity in the characteristics of graphs and graph operations make it challenging to find a right combination of tools and implementation of algorithms to discover desired knowledge from the target data set. This study presents an extensive empirical study of three representative graph processing platforms: Pegasus, GraphX, and Urika. Each system represents a combination of options in data model, processing paradigm, and infrastructure. We benchmarked each platform using three popular graph operations, degree distribution,more » connected components, and PageRank over a variety of real-world graphs. Our experiments show that each graph processing platform shows different strength, depending the type of graph operations. While Urika performs the best in non-iterative operations like degree distribution, GraphX outputforms iterative operations like connected components and PageRank. In addition, we discuss challenges to optimize the performance of each platform over large scale real world graphs.« less

Graph pyramids for protein function prediction

PubMed Central

2015-01-01

Background Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Methods Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Results Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data. PMID:26044522

Graph pyramids for protein function prediction.

PubMed

Sandhan, Tushar; Yoo, Youngjun; Choi, Jin; Kim, Sun

2015-01-01

Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data.

Unwinding the hairball graph: Pruning algorithms for weighted complex networks

NASA Astrophysics Data System (ADS)

Dianati, Navid

2016-01-01

Empirical networks of weighted dyadic relations often contain "noisy" edges that alter the global characteristics of the network and obfuscate the most important structures therein. Graph pruning is the process of identifying the most significant edges according to a generative null model and extracting the subgraph consisting of those edges. Here, we focus on integer-weighted graphs commonly arising when weights count the occurrences of an "event" relating the nodes. We introduce a simple and intuitive null model related to the configuration model of network generation and derive two significance filters from it: the marginal likelihood filter (MLF) and the global likelihood filter (GLF). The former is a fast algorithm assigning a significance score to each edge based on the marginal distribution of edge weights, whereas the latter is an ensemble approach which takes into account the correlations among edges. We apply these filters to the network of air traffic volume between US airports and recover a geographically faithful representation of the graph. Furthermore, compared with thresholding based on edge weight, we show that our filters extract a larger and significantly sparser giant component.

Mining and Modeling Real-World Networks: Patterns, Anomalies, and Tools

ERIC Educational Resources Information Center

Akoglu, Leman

2012-01-01

Large real-world graph (a.k.a network, relational) data are omnipresent, in online media, businesses, science, and the government. Analysis of these massive graphs is crucial, in order to extract descriptive and predictive knowledge with many commercial, medical, and environmental applications. In addition to its general structure, knowing what…

Fast generation of sparse random kernel graphs

DOE PAGES

Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

2015-09-10

The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

An Optimal CDS Construction Algorithm with Activity Scheduling in Ad Hoc Networks

PubMed Central

Penumalli, Chakradhar; Palanichamy, Yogesh

2015-01-01

A new energy efficient optimal Connected Dominating Set (CDS) algorithm with activity scheduling for mobile ad hoc networks (MANETs) is proposed. This algorithm achieves energy efficiency by minimizing the Broadcast Storm Problem [BSP] and at the same time considering the node's remaining energy. The Connected Dominating Set is widely used as a virtual backbone or spine in mobile ad hoc networks [MANETs] or Wireless Sensor Networks [WSN]. The CDS of a graph representing a network has a significant impact on an efficient design of routing protocol in wireless networks. Here the CDS is a distributed algorithm with activity scheduling based on unit disk graph [UDG]. The node's mobility and residual energy (RE) are considered as parameters in the construction of stable optimal energy efficient CDS. The performance is evaluated at various node densities, various transmission ranges, and mobility rates. The theoretical analysis and simulation results of this algorithm are also presented which yield better results. PMID:26221627

Visibility graph analysis of very short-term heart rate variability during sleep

NASA Astrophysics Data System (ADS)

Hou, F. Z.; Li, F. W.; Wang, J.; Yan, F. R.

2016-09-01

Based on a visibility-graph algorithm, complex networks were constructed from very short-term heart rate variability (HRV) during different sleep stages. Network measurements progressively changed from rapid eye movement (REM) sleep to light sleep and then deep sleep, exhibiting promising ability for sleep assessment. Abnormal activation of the cardiovascular controls with enhanced 'small-world' couplings and altered fractal organization during REM sleep indicates that REM could be a potential risk factor for adverse cardiovascular event, especially in males, older individuals, and people who are overweight. Additionally, an apparent influence of gender, aging, and obesity on sleep was demonstrated in healthy adults, which may be helpful for establishing expected sleep-HRV patterns in different populations.

Analysis Tools for Interconnected Boolean Networks With Biological Applications.

PubMed

Chaves, Madalena; Tournier, Laurent

2018-01-01

Boolean networks with asynchronous updates are a class of logical models particularly well adapted to describe the dynamics of biological networks with uncertain measures. The state space of these models can be described by an asynchronous state transition graph, which represents all the possible exits from every single state, and gives a global image of all the possible trajectories of the system. In addition, the asynchronous state transition graph can be associated with an absorbing Markov chain, further providing a semi-quantitative framework where it becomes possible to compute probabilities for the different trajectories. For large networks, however, such direct analyses become computationally untractable, given the exponential dimension of the graph. Exploiting the general modularity of biological systems, we have introduced the novel concept of asymptotic graph , computed as an interconnection of several asynchronous transition graphs and recovering all asymptotic behaviors of a large interconnected system from the behavior of its smaller modules. From a modeling point of view, the interconnection of networks is very useful to address for instance the interplay between known biological modules and to test different hypotheses on the nature of their mutual regulatory links. This paper develops two new features of this general methodology: a quantitative dimension is added to the asymptotic graph, through the computation of relative probabilities for each final attractor and a companion cross-graph is introduced to complement the method on a theoretical point of view.

Path finding methods accounting for stoichiometry in metabolic networks

PubMed Central

2011-01-01

Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks. PMID:21619601

Large-scale quantum networks based on graphs

NASA Astrophysics Data System (ADS)

Epping, Michael; Kampermann, Hermann; Bruß, Dagmar

2016-05-01

Society relies and depends increasingly on information exchange and communication. In the quantum world, security and privacy is a built-in feature for information processing. The essential ingredient for exploiting these quantum advantages is the resource of entanglement, which can be shared between two or more parties. The distribution of entanglement over large distances constitutes a key challenge for current research and development. Due to losses of the transmitted quantum particles, which typically scale exponentially with the distance, intermediate quantum repeater stations are needed. Here we show how to generalise the quantum repeater concept to the multipartite case, by describing large-scale quantum networks, i.e. network nodes and their long-distance links, consistently in the language of graphs and graph states. This unifying approach comprises both the distribution of multipartite entanglement across the network, and the protection against errors via encoding. The correspondence to graph states also provides a tool for optimising the architecture of quantum networks.

A prediction model for cognitive performance in health ageing using diffusion tensor imaging with graph theory.

PubMed

Yun, Ruijuan; Lin, Chung-Chih; Wu, Shuicai; Huang, Chu-Chung; Lin, Ching-Po; Chao, Yi-Ping

2013-01-01

In this study, we employed diffusion tensor imaging (DTI) to construct brain structural network and then derive the connection matrices from 96 healthy elderly subjects. The correlation analysis between these topological properties of network based on graph theory and the Cognitive Abilities Screening Instrument (CASI) index were processed to extract the significant network characteristics. These characteristics were then integrated to estimate the models by various machine-learning algorithms to predict user's cognitive performance. From the results, linear regression model and Gaussian processes model showed presented better abilities with lower mean absolute errors of 5.8120 and 6.25 to predict the cognitive performance respectively. Moreover, these extracted topological properties of brain structural network derived from DTI also could be regarded as the bio-signatures for further evaluation of brain degeneration in healthy aged and early diagnosis of mild cognitive impairment (MCI).

Failure Analysis of Network Based Accessible Pedestrian Signals in Closed-Loop Operation

DOT National Transportation Integrated Search

2011-03-01

The potential failure modes of a network based accessible pedestrian system were analyzed to determine the limitations and benefits of closed-loop operation. The vulnerabilities of the system are accessed using the industry standard process known as ...

Classification of user interfaces for graph-based online analytical processing

NASA Astrophysics Data System (ADS)

Michaelis, James R.

2016-05-01

In the domain of business intelligence, user-oriented software for conducting multidimensional analysis via Online- Analytical Processing (OLAP) is now commonplace. In this setting, datasets commonly have well-defined sets of dimensions and measures around which analysis tasks can be conducted. However, many forms of data used in intelligence operations - deriving from social networks, online communications, and text corpora - will consist of graphs with varying forms of potential dimensional structure. Hence, enabling OLAP over such data collections requires explicit definition and extraction of supporting dimensions and measures. Further, as Graph OLAP remains an emerging technique, limited research has been done on its user interface requirements. Namely, on effective pairing of interface designs to different types of graph-derived dimensions and measures. This paper presents a novel technique for pairing of user interface designs to Graph OLAP datasets, rooted in Analytic Hierarchy Process (AHP) driven comparisons. Attributes of the classification strategy are encoded through an AHP ontology, developed in our alternate work and extended to support pairwise comparison of interfaces. Specifically, according to their ability, as perceived by Subject Matter Experts, to support dimensions and measures corresponding to Graph OLAP dataset attributes. To frame this discussion, a survey is provided both on existing variations of Graph OLAP, as well as existing interface designs previously applied in multidimensional analysis settings. Following this, a review of our AHP ontology is provided, along with a listing of corresponding dataset and interface attributes applicable toward SME recommendation structuring. A walkthrough of AHP-based recommendation encoding via the ontology-based approach is then provided. The paper concludes with a short summary of proposed future directions seen as essential for this research area.

Static, Dynamic and Semantic Dimensions: Towards a Multidisciplinary Approach of Social Networks Analysis

NASA Astrophysics Data System (ADS)

Thovex, Christophe; Trichet, Francky

The objective of our work is to extend static and dynamic models of Social Networks Analysis (SNA), by taking conceptual aspects of enterprises and institutions social graph into account. The originality of our multidisciplinary work is to introduce abstract notions of electro-physic to define new measures in SNA, for new decision-making functions dedicated to Human Resource Management (HRM). This paper introduces a multidimensional system and new measures: (1) a tension measure for social network analysis, (2) an electrodynamic, predictive and semantic system for recommendations on social graphs evolutions and (3) a reactance measure used to evaluate the individual stress at work of the members of a social network.

CoryneRegNet 4.0 – A reference database for corynebacterial gene regulatory networks

PubMed Central

Baumbach, Jan

2007-01-01

Background Detailed information on DNA-binding transcription factors (the key players in the regulation of gene expression) and on transcriptional regulatory interactions of microorganisms deduced from literature-derived knowledge, computer predictions and global DNA microarray hybridization experiments, has opened the way for the genome-wide analysis of transcriptional regulatory networks. The large-scale reconstruction of these networks allows the in silico analysis of cell behavior in response to changing environmental conditions. We previously published CoryneRegNet, an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum. Results Now we introduce CoryneRegNet release 4.0, which integrates data on the gene regulatory networks of 4 corynebacteria, 2 mycobacteria and the model organism Escherichia coli K12. As the previous versions, CoryneRegNet provides a web-based user interface to access the database content, to allow various queries, and to support the reconstruction, analysis and visualization of regulatory networks at different hierarchical levels. In this article, we present the further improved database content of CoryneRegNet along with novel analysis features. The network visualization feature GraphVis now allows the inter-species comparisons of reconstructed gene regulatory networks and the projection of gene expression levels onto that networks. Therefore, we added stimulon data directly into the database, but also provide Web Service access to the DNA microarray analysis platform EMMA. Additionally, CoryneRegNet now provides a SOAP based Web Service server, which can easily be consumed by other bioinformatics software systems. Stimulons (imported from the database, or uploaded by the user) can be analyzed in the context of known transcriptional regulatory networks to predict putative contradictions or further gene regulatory interactions. Furthermore, it integrates protein clusters by means of heuristically solving the weighted graph cluster editing problem. In addition, it provides Web Service based access to up to date gene annotation data from GenDB. Conclusion The release 4.0 of CoryneRegNet is a comprehensive system for the integrated analysis of procaryotic gene regulatory networks. It is a versatile systems biology platform to support the efficient and large-scale analysis of transcriptional regulation of gene expression in microorganisms. It is publicly available at . PMID:17986320

Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications

PubMed Central

Tadić, Bosiljka; Andjelković, Miroslav; Boshkoska, Biljana Mileva; Levnajić, Zoran

2016-01-01

Human behaviour in various circumstances mirrors the corresponding brain connectivity patterns, which are suitably represented by functional brain networks. While the objective analysis of these networks by graph theory tools deepened our understanding of brain functions, the multi-brain structures and connections underlying human social behaviour remain largely unexplored. In this study, we analyse the aggregate graph that maps coordination of EEG signals previously recorded during spoken communications in two groups of six listeners and two speakers. Applying an innovative approach based on the algebraic topology of graphs, we analyse higher-order topological complexes consisting of mutually interwoven cliques of a high order to which the identified functional connections organise. Our results reveal that the topological quantifiers provide new suitable measures for differences in the brain activity patterns and inter-brain synchronisation between speakers and listeners. Moreover, the higher topological complexity correlates with the listener’s concentration to the story, confirmed by self-rating, and closeness to the speaker’s brain activity pattern, which is measured by network-to-network distance. The connectivity structures of the frontal and parietal lobe consistently constitute distinct clusters, which extend across the listener’s group. Formally, the topology quantifiers of the multi-brain communities exceed the sum of those of the participating individuals and also reflect the listener’s rated attributes of the speaker and the narrated subject. In the broader context, the presented study exposes the relevance of higher topological structures (besides standard graph measures) for characterising functional brain networks under different stimuli. PMID:27880802

Roads at risk - the impact of debris flows on road network reliability and vulnerability in southern Norway

NASA Astrophysics Data System (ADS)

Meyer, Nele Kristin; Schwanghart, Wolfgang; Korup, Oliver

2014-05-01

Norwegian's road network is frequently affected by debris flows. Both damage repair and traffic interruption generate high economic losses and necessitate a rigorous assessment of where losses are expected to be high and where preventive measures should be focused on. In recent studies, we have developed susceptibility and trigger probability maps that serve as input into a hazard calculation at the scale of first-order watersheds. Here we combine these results with graph theory to assess the impact of debris flows on the road network of southern Norway. Susceptibility and trigger probability are aggregated for individual road sections to form a reliability index that relates to the failure probability of a link that connects two network vertices, e.g., road junctions. We define link vulnerability as a function of traffic volume and additional link failure distance. Additional link failure distance is the extra length of the alternative path connecting the two associated link vertices in case the network link fails and is calculated by a shortest-path algorithm. The product of network reliability and vulnerability indices represent the risk index. High risk indices identify critical links for the Norwegian road network and are investigated in more detail. Scenarios demonstrating the impact of single or multiple debris flow events are run for the most important routes between seven large cities in southern Norway. First results show that the reliability of the road network is lowest in the central and north-western part of the study area. Road network vulnerability is highest in the mountainous regions in central southern Norway where the road density is low and in the vicinity of cities where the traffic volume is large. The scenarios indicate that city connections that have their shortest path via routes crossing the central part of the study area have the highest risk of route failure.

Artistic image analysis using graph-based learning approaches.

PubMed

Carneiro, Gustavo

2013-08-01

We introduce a new methodology for the problem of artistic image analysis, which among other tasks, involves the automatic identification of visual classes present in an art work. In this paper, we advocate the idea that artistic image analysis must explore a graph that captures the network of artistic influences by computing the similarities in terms of appearance and manual annotation. One of the novelties of our methodology is the proposed formulation that is a principled way of combining these two similarities in a single graph. Using this graph, we show that an efficient random walk algorithm based on an inverted label propagation formulation produces more accurate annotation and retrieval results compared with the following baseline algorithms: bag of visual words, label propagation, matrix completion, and structural learning. We also show that the proposed approach leads to a more efficient inference and training procedures. This experiment is run on a database containing 988 artistic images (with 49 visual classification problems divided into a multiclass problem with 27 classes and 48 binary problems), where we show the inference and training running times, and quantitative comparisons with respect to several retrieval and annotation performance measures.

Framework for Querying and Analysis of Evolving Graphs

ERIC Educational Resources Information Center

Moffitt, Vera Zaychik

2017-01-01

Graph representations underlie many modern computer applications, capturing the structure of such diverse networks as the Internet, personal associations, roads, sensors, and metabolic pathways. While the static structure of graphs is a well-explored field, a new emphasis is being placed on understanding and representing the way these networks…

Network connectivity value.

PubMed

Dragicevic, Arnaud; Boulanger, Vincent; Bruciamacchie, Max; Chauchard, Sandrine; Dupouey, Jean-Luc; Stenger, Anne

2017-04-21

In order to unveil the value of network connectivity, we formalize the construction of ecological networks in forest environments as an optimal control dynamic graph-theoretic problem. The network is based on a set of bioreserves and patches linked by ecological corridors. The node dynamics, built upon the consensus protocol, form a time evolutive Mahalanobis distance weighted by the opportunity costs of timber production. We consider a case of complete graph, where the ecological network is fully connected, and a case of incomplete graph, where the ecological network is partially connected. The results show that the network equilibrium depends on the size of the reception zone, while the network connectivity depends on the environmental compatibility between the ecological areas. Through shadow prices, we find that securing connectivity in partially connected networks is more expensive than in fully connected networks, but should be undertaken when the opportunity costs are significant. Copyright © 2017 Elsevier Ltd. All rights reserved.

Exploratory Visualization of Graphs Based on Community Structure

ERIC Educational Resources Information Center

Liu, Yujie

2013-01-01

Communities, also called clusters or modules, are groups of nodes which probably share common properties and/or play similar roles within a graph. They widely exist in real networks such as biological, social, and information networks. Allowing users to interactively browse and explore the community structure, which is essential for understanding…

Discovery of Boolean metabolic networks: integer linear programming based approach.

PubMed

Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

2018-04-11

Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

[Not Available].

PubMed

Yanashima, Ryoji; Kitagawa, Noriyuki; Matsubara, Yoshiya; Weatheritt, Robert; Oka, Kotaro; Kikuchi, Shinichi; Tomita, Masaru; Ishizaki, Shun

2009-01-01

The scale-free and small-world network models reflect the functional units of networks. However, when we investigated the network properties of a signaling pathway using these models, no significant differences were found between the original undirected graphs and the graphs in which inactive proteins were eliminated from the gene expression data. We analyzed signaling networks by focusing on those pathways that best reflected cellular function. Therefore, our analysis of pathways started from the ligands and progressed to transcription factors and cytoskeletal proteins. We employed the Python module to assess the target network. This involved comparing the original and restricted signaling cascades as a directed graph using microarray gene expression profiles of late onset Alzheimer's disease. The most commonly used method of shortest-path analysis neglects to consider the influences of alternative pathways that can affect the activation of transcription factors or cytoskeletal proteins. We therefore introduced included k-shortest paths and k-cycles in our network analysis using the Python modules, which allowed us to attain a reasonable computational time and identify k-shortest paths. This technique reflected results found in vivo and identified pathways not found when shortest path or degree analysis was applied. Our module enabled us to comprehensively analyse the characteristics of biomolecular networks and also enabled analysis of the effects of diseases considering the feedback loop and feedforward loop control structures as an alternative path.

On the Topologic Properties of River Networks

NASA Astrophysics Data System (ADS)

Sarker, S.; Singh, A.

2017-12-01

River network is an important landscape feature and has been studied extensively from a range of geomorphological and hydrological perspective. However, quantifying topologic dynamics and reorganization of river networks is becoming more and more challenging under changing natural and anthropogenic forcings. Here, we use a graph-theoretical approach to study topologic properties of natural and simulated river networks for a range of climatic and tectonic conditions. Among other metrics, we use betweeness and eigenvector centrality distributions computed using adjacency matrix of river networks and show their dependence on energy exponent γ that characterizes mechanism of erosional processes on a landscape. We further compare these topologic characteristics of landscape to geomorphic features such as slope-area curve and drainage density. Furthermore, we identify locations of critical nodes and links on a network as a function of energy exponent γ to understand network robustness and vulnerability under external attacks.

The Mathematics of Networks Science: Scale-Free, Power-Law Graphs and Continuum Theoretical Analysis

ERIC Educational Resources Information Center

Padula, Janice

2012-01-01

When hoping to initiate or sustain students' interest in mathematics teachers should always consider relevance, relevance to students' lives and in the middle and later years of instruction in high school and university, accessibility. A topic such as the mathematics behind networks science, more specifically scale-free graphs, is up-to-date,…

Heuristic urban transportation network design method, a multilayer coevolution approach

NASA Astrophysics Data System (ADS)

Ding, Rui; Ujang, Norsidah; Hamid, Hussain bin; Manan, Mohd Shahrudin Abd; Li, Rong; Wu, Jianjun

2017-08-01

The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users' route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends.

Topics on data transmission problem in software definition network

NASA Astrophysics Data System (ADS)

Gao, Wei; Liang, Li; Xu, Tianwei; Gan, Jianhou

2017-08-01

In normal computer networks, the data transmission between two sites go through the shortest path between two corresponding vertices. However, in the setting of software definition network (SDN), it should monitor the network traffic flow in each site and channel timely, and the data transmission path between two sites in SDN should consider the congestion in current networks. Hence, the difference of available data transmission theory between normal computer network and software definition network is that we should consider the prohibit graph structures in SDN, and these forbidden subgraphs represent the sites and channels in which data can't be passed by the serious congestion. Inspired by theoretical analysis of an available data transmission in SDN, we consider some computational problems from the perspective of the graph theory. Several results determined in the paper imply the sufficient conditions of data transmission in SDN in the various graph settings.

Pattern recognition tool based on complex network-based approach

NASA Astrophysics Data System (ADS)

Casanova, Dalcimar; Backes, André Ricardo; Martinez Bruno, Odemir

2013-02-01

This work proposed a generalization of the method proposed by the authors: 'A complex network-based approach for boundary shape analysis'. Instead of modelling a contour into a graph and use complex networks rules to characterize it, here, we generalize the technique. This way, the work proposes a mathematical tool for characterization signals, curves and set of points. To evaluate the pattern description power of the proposal, an experiment of plat identification based on leaf veins image are conducted. Leaf vein is a taxon characteristic used to plant identification proposes, and one of its characteristics is that these structures are complex, and difficult to be represented as a signal or curves and this way to be analyzed in a classical pattern recognition approach. Here, we model the veins as a set of points and model as graphs. As features, we use the degree and joint degree measurements in a dynamic evolution. The results demonstrates that the technique has a good power of discrimination and can be used for plant identification, as well as other complex pattern recognition tasks.

Quantification of Graph Complexity Based on the Edge Weight Distribution Balance: Application to Brain Networks.

PubMed

Gomez-Pilar, Javier; Poza, Jesús; Bachiller, Alejandro; Gómez, Carlos; Núñez, Pablo; Lubeiro, Alba; Molina, Vicente; Hornero, Roberto

2018-02-01

The aim of this study was to introduce a novel global measure of graph complexity: Shannon graph complexity (SGC). This measure was specifically developed for weighted graphs, but it can also be applied to binary graphs. The proposed complexity measure was designed to capture the interplay between two properties of a system: the 'information' (calculated by means of Shannon entropy) and the 'order' of the system (estimated by means of a disequilibrium measure). SGC is based on the concept that complex graphs should maintain an equilibrium between the aforementioned two properties, which can be measured by means of the edge weight distribution. In this study, SGC was assessed using four synthetic graph datasets and a real dataset, formed by electroencephalographic (EEG) recordings from controls and schizophrenia patients. SGC was compared with graph density (GD), a classical measure used to evaluate graph complexity. Our results showed that SGC is invariant with respect to GD and independent of node degree distribution. Furthermore, its variation with graph size [Formula: see text] is close to zero for [Formula: see text]. Results from the real dataset showed an increment in the weight distribution balance during the cognitive processing for both controls and schizophrenia patients, although these changes are more relevant for controls. Our findings revealed that SGC does not need a comparison with null-hypothesis networks constructed by a surrogate process. In addition, SGC results on the real dataset suggest that schizophrenia is associated with a deficit in the brain dynamic reorganization related to secondary pathways of the brain network.

Graph theory findings in the pathophysiology of temporal lobe epilepsy

PubMed Central

Chiang, Sharon; Haneef, Zulfi

2014-01-01

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Accumulating evidence has shown that TLE is a disorder of abnormal epileptogenic networks, rather than focal sources. Graph theory allows for a network-based representation of TLE brain networks, and has potential to illuminate characteristics of brain topology conducive to TLE pathophysiology, including seizure initiation and spread. We review basic concepts which we believe will prove helpful in interpreting results rapidly emerging from graph theory research in TLE. In addition, we summarize the current state of graph theory findings in TLE as they pertain its pathophysiology. Several common findings have emerged from the many modalities which have been used to study TLE using graph theory, including structural MRI, diffusion tensor imaging, surface EEG, intracranial EEG, magnetoencephalography, functional MRI, cell cultures, simulated models, and mouse models, involving increased regularity of the interictal network configuration, altered local segregation and global integration of the TLE network, and network reorganization of temporal lobe and limbic structures. As different modalities provide different views of the same phenomenon, future studies integrating data from multiple modalities are needed to clarify findings and contribute to the formation of a coherent theory on the pathophysiology of TLE. PMID:24831083

Investigating changes in brain network properties in HIV-associated neurocognitive disease (HAND) using mutual connectivity analysis (MCA)

NASA Astrophysics Data System (ADS)

Abidin, Anas Zainul; D'Souza, Adora M.; Nagarajan, Mahesh B.; Wismüller, Axel

2016-03-01

About 50% of subjects infected with HIV present deficits in cognitive domains, which are known collectively as HIV associated neurocognitive disorder (HAND). The underlying synaptodendritic damage can be captured using resting state functional MRI, as has been demonstrated by a few earlier studies. Such damage may induce topological changes of brain connectivity networks. We test this hypothesis by capturing the functional interdependence of 90 brain network nodes using a Mutual Connectivity Analysis (MCA) framework with non-linear time series modeling based on Generalized Radial Basis function (GRBF) neural networks. The network nodes are selected based on the regions defined in the Automated Anatomic Labeling (AAL) atlas. Each node is represented by the average time series of the voxels of that region. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We tested for differences in these properties in network graphs obtained for 10 subjects (6 male and 4 female, 5 HIV+ and 5 HIV-). Global network properties captured some differences between these subject cohorts, though significant differences were seen only with the clustering coefficient measure. Local network properties, such as local efficiency and the degree of connections, captured significant differences in regions of the frontal lobe, precentral and cingulate cortex amongst a few others. These results suggest that our method can be used to effectively capture differences occurring in brain network connectivity properties revealed by resting-state functional MRI in neurological disease states, such as HAND.

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

PubMed Central

Ghanbari, Yasser; Smith, Alex R.; Schultz, Robert T.; Verma, Ragini

2014-01-01

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain’s traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations. PMID:25037933

A Statistical Graphical Model of the California Reservoir System

NASA Astrophysics Data System (ADS)

Taeb, A.; Reager, J. T.; Turmon, M.; Chandrasekaran, V.

2017-11-01

The recent California drought has highlighted the potential vulnerability of the state's water management infrastructure to multiyear dry intervals. Due to the high complexity of the network, dynamic storage changes in California reservoirs on a state-wide scale have previously been difficult to model using either traditional statistical or physical approaches. Indeed, although there is a significant line of research on exploring models for single (or a small number of) reservoirs, these approaches are not amenable to a system-wide modeling of the California reservoir network due to the spatial and hydrological heterogeneities of the system. In this work, we develop a state-wide statistical graphical model to characterize the dependencies among a collection of 55 major California reservoirs across the state; this model is defined with respect to a graph in which the nodes index reservoirs and the edges specify the relationships or dependencies between reservoirs. We obtain and validate this model in a data-driven manner based on reservoir volumes over the period 2003-2016. A key feature of our framework is a quantification of the effects of external phenomena that influence the entire reservoir network. We further characterize the degree to which physical factors (e.g., state-wide Palmer Drought Severity Index (PDSI), average temperature, snow pack) and economic factors (e.g., consumer price index, number of agricultural workers) explain these external influences. As a consequence of this analysis, we obtain a system-wide health diagnosis of the reservoir network as a function of PDSI.

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

PubMed Central

Xu, Tingting; Cullen, Kathryn R.; Mueller, Bryon; Schreiner, Mindy W.; Lim, Kelvin O.; Schulz, S. Charles; Parhi, Keshab K.

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03–0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03–0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works. PMID:26977400

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI.

PubMed

Xu, Tingting; Cullen, Kathryn R; Mueller, Bryon; Schreiner, Mindy W; Lim, Kelvin O; Schulz, S Charles; Parhi, Keshab K

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03-0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03-0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works.

NAP: The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks.

PubMed

Theodosiou, Theodosios; Efstathiou, Georgios; Papanikolaou, Nikolas; Kyrpides, Nikos C; Bagos, Pantelis G; Iliopoulos, Ioannis; Pavlopoulos, Georgios A

2017-07-14

Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network's size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks. Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .

Privacy Breach Analysis in Social Networks

NASA Astrophysics Data System (ADS)

Nagle, Frank

This chapter addresses various aspects of analyzing privacy breaches in social networks. We first review literature that defines three types of privacy breaches in social networks: interactive, active, and passive. We then survey the various network anonymization schemes that have been constructed to address these privacy breaches. After exploring these breaches and anonymization schemes, we evaluate a measure for determining the level of anonymity inherent in a network graph based on its topological structure. Finally, we close by emphasizing the difficulty of anonymizing social network data while maintaining usability for research purposes and offering areas for future work.

Weighted graph cuts without eigenvectors a multilevel approach.

PubMed

Dhillon, Inderjit S; Guan, Yuqiang; Kulis, Brian

2007-11-01

A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods. In this paper, we discuss an equivalence between the objective functions used in these seemingly different methods--in particular, a general weighted kernel k-means objective is mathematically equivalent to a weighted graph clustering objective. We exploit this equivalence to develop a fast, high-quality multilevel algorithm that directly optimizes various weighted graph clustering objectives, such as the popular ratio cut, normalized cut, and ratio association criteria. This eliminates the need for any eigenvector computation for graph clustering problems, which can be prohibitive for very large graphs. Previous multilevel graph partitioning methods, such as Metis, have suffered from the restriction of equal-sized clusters; our multilevel algorithm removes this restriction by using kernel k-means to optimize weighted graph cuts. Experimental results show that our multilevel algorithm outperforms a state-of-the-art spectral clustering algorithm in terms of speed, memory usage, and quality. We demonstrate that our algorithm is applicable to large-scale clustering tasks such as image segmentation, social network analysis and gene network analysis.

Active vision and image/video understanding with decision structures based on the network-symbolic models

NASA Astrophysics Data System (ADS)

Kuvich, Gary

2003-08-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolve ambiguity and uncertainty via feedback projections, and provide image understanding that is an interpretation of visual information in terms of such knowledge models. The ability of human brain to emulate knowledge structures in the form of networks-symbolic models is found. And that means an important shift of paradigm in our knowledge about brain from neural networks to "cortical software". Symbols, predicates and grammars naturally emerge in such active multilevel hierarchical networks, and logic is simply a way of restructuring such models. Brain analyzes an image as a graph-type decision structure created via multilevel hierarchical compression of visual information. Mid-level vision processes like clustering, perceptual grouping, separation of figure from ground, are special kinds of graph/network transformations. They convert low-level image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena are results of such analysis. Composition of network-symbolic models works similar to frames and agents, combines learning, classification, analogy together with higher-level model-based reasoning into a single framework. Such models do not require supercomputers. Based on such principles, and using methods of Computational intelligence, an Image Understanding system can convert images into the network-symbolic knowledge models, and effectively resolve uncertainty and ambiguity, providing unifying representation for perception and cognition. That allows creating new intelligent computer vision systems for robotic and defense industries.

Altered brain functional networks in people with Internet gaming disorder: Evidence from resting-state fMRI.

PubMed

Wang, Lingxiao; Wu, Lingdan; Lin, Xiao; Zhang, Yifen; Zhou, Hongli; Du, Xiaoxia; Dong, Guangheng

2016-08-30

Although numerous neuroimaging studies have detected structural and functional abnormality in specific brain regions and connections in subjects with Internet gaming disorder (IGD), the topological organization of the whole-brain network in IGD remain unclear. In this study, we applied graph theoretical analysis to explore the intrinsic topological properties of brain networks in Internet gaming disorder (IGD). 37 IGD subjects and 35 matched healthy control (HC) subjects underwent a resting-state functional magnetic resonance imaging scan. The functional networks were constructed by thresholding partial correlation matrices of 90 brain regions. Then we applied graph-based approaches to analysis their topological attributes, including small-worldness, nodal metrics, and efficiency. Both IGD and HC subjects show efficient and economic brain network, and small-world topology. Although there was no significant group difference in global topology metrics, the IGD subjects showed reduced regional centralities in the prefrontal cortex, left posterior cingulate cortex, right amygdala, and bilateral lingual gyrus, and increased functional connectivity in sensory-motor-related brain networks compared to the HC subjects. These results imply that people with IGD may be associated with functional network dysfunction, including impaired executive control and emotional management, but enhanced coordination among visual, sensorimotor, auditory and visuospatial systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining.

PubMed

Huan, Tianxiao; Sivachenko, Andrey Y; Harrison, Scott H; Chen, Jake Y

2008-08-12

New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network. The architectural design of ProteoLens makes it suitable for bioinformatics expert data analysts who are experienced with relational database management to perform large-scale integrated network visual explorations. ProteoLens is a promising visual analytic platform that will facilitate knowledge discoveries in future network and systems biology studies.

Human connectome module pattern detection using a new multi-graph MinMax cut model.

PubMed

De, Wang; Wang, Yang; Nie, Feiping; Yan, Jingwen; Cai, Weidong; Saykin, Andrew J; Shen, Li; Huang, Heng

2014-01-01

Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.

Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease.

PubMed

Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas

2016-09-01

The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer's disease (AD), and 45 age-matched HC. The rs-fMRI connectivity matrix was constructed using a brain parcellation based on a 264 putative functional areas. Using the optimal features extracted from the graph measures, we were able to accurately classify three groups (i.e., HC, MCI, and AD) with accuracy of 88.4 %. We also investigated performance of our proposed method for a binary classification of a group (e.g., MCI) from two other groups (e.g., HC and AD). The classification accuracies for identifying HC from AD and MCI, AD from HC and MCI, and MCI from HC and AD, were 87.3, 97.5, and 72.0 %, respectively. In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.

Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory.

PubMed

Stavrakas, Vassilis; Melas, Ioannis N; Sakellaropoulos, Theodore; Alexopoulos, Leonidas G

2015-01-01

Modeling of signal transduction pathways is instrumental for understanding cells' function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells' biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways' logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein-protein interaction networks and to provide meaningful biological insights.

EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome.

PubMed

Hassan, Mahmoud; Shamas, Mohamad; Khalil, Mohamad; El Falou, Wassim; Wendling, Fabrice

2015-01-01

The brain is a large-scale complex network often referred to as the "connectome". Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage: https://sites.google.com/site/eegnetworks/.

EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome

PubMed Central

Hassan, Mahmoud; Shamas, Mohamad; Khalil, Mohamad; El Falou, Wassim; Wendling, Fabrice

2015-01-01

The brain is a large-scale complex network often referred to as the “connectome”. Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage: https://sites.google.com/site/eegnetworks/. PMID:26379232

Co-clustering directed graphs to discover asymmetries and directional communities

PubMed Central

Rohe, Karl; Qin, Tai; Yu, Bin

2016-01-01

In directed graphs, relationships are asymmetric and these asymmetries contain essential structural information about the graph. Directed relationships lead to a new type of clustering that is not feasible in undirected graphs. We propose a spectral co-clustering algorithm called di-sim for asymmetry discovery and directional clustering. A Stochastic co-Blockmodel is introduced to show favorable properties of di-sim. To account for the sparse and highly heterogeneous nature of directed networks, di-sim uses the regularized graph Laplacian and projects the rows of the eigenvector matrix onto the sphere. A nodewise asymmetry score and di-sim are used to analyze the clustering asymmetries in the networks of Enron emails, political blogs, and the Caenorhabditis elegans chemical connectome. In each example, a subset of nodes have clustering asymmetries; these nodes send edges to one cluster, but receive edges from another cluster. Such nodes yield insightful information (e.g., communication bottlenecks) about directed networks, but are missed if the analysis ignores edge direction. PMID:27791058

Co-clustering directed graphs to discover asymmetries and directional communities.

PubMed

Rohe, Karl; Qin, Tai; Yu, Bin

2016-10-21

In directed graphs, relationships are asymmetric and these asymmetries contain essential structural information about the graph. Directed relationships lead to a new type of clustering that is not feasible in undirected graphs. We propose a spectral co-clustering algorithm called di-sim for asymmetry discovery and directional clustering. A Stochastic co-Blockmodel is introduced to show favorable properties of di-sim To account for the sparse and highly heterogeneous nature of directed networks, di-sim uses the regularized graph Laplacian and projects the rows of the eigenvector matrix onto the sphere. A nodewise asymmetry score and di-sim are used to analyze the clustering asymmetries in the networks of Enron emails, political blogs, and the Caenorhabditis elegans chemical connectome. In each example, a subset of nodes have clustering asymmetries; these nodes send edges to one cluster, but receive edges from another cluster. Such nodes yield insightful information (e.g., communication bottlenecks) about directed networks, but are missed if the analysis ignores edge direction.

Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

PubMed

Schmidt, Helmut; Petkov, George; Richardson, Mark P; Terry, John R

2014-11-01

Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz) and low-alpha (6-9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.

Dynamic Graph Analytic Framework (DYGRAF): greater situation awareness through layered multi-modal network analysis

NASA Astrophysics Data System (ADS)

Margitus, Michael R.; Tagliaferri, William A., Jr.; Sudit, Moises; LaMonica, Peter M.

2012-06-01

Understanding the structure and dynamics of networks are of vital importance to winning the global war on terror. To fully comprehend the network environment, analysts must be able to investigate interconnected relationships of many diverse network types simultaneously as they evolve both spatially and temporally. To remove the burden from the analyst of making mental correlations of observations and conclusions from multiple domains, we introduce the Dynamic Graph Analytic Framework (DYGRAF). DYGRAF provides the infrastructure which facilitates a layered multi-modal network analysis (LMMNA) approach that enables analysts to assemble previously disconnected, yet related, networks in a common battle space picture. In doing so, DYGRAF provides the analyst with timely situation awareness, understanding and anticipation of threats, and support for effective decision-making in diverse environments.

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

PubMed Central

Lange, Stefan; Donges, Jonathan F.; Volkholz, Jan; Kurths, Jürgen

2015-01-01

A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation. Building on a recent study by Feldhoff et al. [1] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system. Three types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge- and node-weighted graphs are discussed. PMID:25856374

Local difference measures between complex networks for dynamical system model evaluation.

PubMed

Lange, Stefan; Donges, Jonathan F; Volkholz, Jan; Kurths, Jürgen

2015-01-01

A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation.Building on a recent study by Feldhoff et al. [8] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system [corrected]. types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge- and node-weighted graphs are discussed.

Using graph theory to analyze biological networks

PubMed Central

2011-01-01

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system. PMID:21527005

SPECTRAL GRAPH THEORY AND GRAPH ENERGY METRICS SHOW EVIDENCE FOR THE ALZHEIMER’S DISEASE DISCONNECTION SYNDROME IN APOE-4 RISK GENE CARRIERS

PubMed Central

Daianu, Madelaine; Mezher, Adam; Jahanshad, Neda; Hibar, Derrek P.; Nir, Talia M.; Jack, Clifford R.; Weiner, Michael W.; Bernstein, Matt A.; Thompson, Paul M.

2015-01-01

Our understanding of network breakdown in Alzheimer’s disease (AD) is likely to be enhanced through advanced mathematical descriptors. Here, we applied spectral graph theory to provide novel metrics of structural connectivity based on 3-Tesla diffusion weighted images in 42 AD patients and 50 healthy controls. We reconstructed connectivity networks using whole-brain tractography and examined, for the first time here, cortical disconnection based on the graph energy and spectrum. We further assessed supporting metrics - link density and nodal strength - to better interpret our results. Metrics were analyzed in relation to the well-known APOE-4 genetic risk factor for late-onset AD. The number of disconnected cortical regions increased with the number of copies of the APOE-4 risk gene in people with AD. Each additional copy of the APOE-4 risk gene may lead to more dysfunctional networks with weakened or abnormal connections, providing evidence for the previously hypothesized “disconnection syndrome”. PMID:26413205

Modelling disease outbreaks in realistic urban social networks

NASA Astrophysics Data System (ADS)

Eubank, Stephen; Guclu, Hasan; Anil Kumar, V. S.; Marathe, Madhav V.; Srinivasan, Aravind; Toroczkai, Zoltán; Wang, Nan

2004-05-01

Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions or ad hoc models for the contact process. Here we explore the use of dynamic bipartite graphs to model the physical contact patterns that result from movements of individuals between specific locations. The graphs are generated by large-scale individual-based urban traffic simulations built on actual census, land-use and population-mobility data. We find that the contact network among people is a strongly connected small-world-like graph with a well-defined scale for the degree distribution. However, the locations graph is scale-free, which allows highly efficient outbreak detection by placing sensors in the hubs of the locations network. Within this large-scale simulation framework, we then analyse the relative merits of several proposed mitigation strategies for smallpox spread. Our results suggest that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population.

SPECTRAL GRAPH THEORY AND GRAPH ENERGY METRICS SHOW EVIDENCE FOR THE ALZHEIMER'S DISEASE DISCONNECTION SYNDROME IN APOE-4 RISK GENE CARRIERS.

PubMed

Daianu, Madelaine; Mezher, Adam; Jahanshad, Neda; Hibar, Derrek P; Nir, Talia M; Jack, Clifford R; Weiner, Michael W; Bernstein, Matt A; Thompson, Paul M

2015-04-01

Our understanding of network breakdown in Alzheimer's disease (AD) is likely to be enhanced through advanced mathematical descriptors. Here, we applied spectral graph theory to provide novel metrics of structural connectivity based on 3-Tesla diffusion weighted images in 42 AD patients and 50 healthy controls. We reconstructed connectivity networks using whole-brain tractography and examined, for the first time here, cortical disconnection based on the graph energy and spectrum. We further assessed supporting metrics - link density and nodal strength - to better interpret our results. Metrics were analyzed in relation to the well-known APOE -4 genetic risk factor for late-onset AD. The number of disconnected cortical regions increased with the number of copies of the APOE -4 risk gene in people with AD. Each additional copy of the APOE -4 risk gene may lead to more dysfunctional networks with weakened or abnormal connections, providing evidence for the previously hypothesized "disconnection syndrome".

Neurobiological changes of schizotypy: evidence from both volume-based morphometric analysis and resting-state functional connectivity.

PubMed

Wang, Yi; Yan, Chao; Yin, Da-zhi; Fan, Ming-xia; Cheung, Eric F C; Pantelis, Christos; Chan, Raymond C K

2015-03-01

The current study sought to examine the underlying brain changes in individuals with high schizotypy by integrating networks derived from brain structural and functional imaging. Individuals with high schizotypy (n = 35) and low schizotypy (n = 34) controls were screened using the Schizotypal Personality Questionnaire and underwent brain structural and resting-state functional magnetic resonance imaging on a 3T scanner. Voxel-based morphometric analysis and graph theory-based functional network analysis were conducted. Individuals with high schizotypy showed reduced gray matter (GM) density in the insula and the dorsolateral prefrontal gyrus. The graph theoretical analysis showed that individuals with high schizotypy showed similar global properties in their functional networks as low schizotypy individuals. Several hubs of the functional network were identified in both groups, including the insula, the lingual gyrus, the postcentral gyrus, and the rolandic operculum. More hubs in the frontal lobe and fewer hubs in the occipital lobe were identified in individuals with high schizotypy. By comparing the functional connectivity between clusters with abnormal GM density and the whole brain, individuals with high schizotypy showed weaker functional connectivity between the left insula and the putamen, but stronger connectivity between the cerebellum and the medial frontal gyrus. Taken together, our findings suggest that individuals with high schizotypy present changes in terms of GM and resting-state functional connectivity, especially in the frontal lobe. © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Network topology and resilience analysis of South Korean power grid

NASA Astrophysics Data System (ADS)

Kim, Dong Hwan; Eisenberg, Daniel A.; Chun, Yeong Han; Park, Jeryang

2017-01-01

In this work, we present topological and resilience analyses of the South Korean power grid (KPG) with a broad voltage level. While topological analysis of KPG only with high-voltage infrastructure shows an exponential degree distribution, providing another empirical evidence of power grid topology, the inclusion of low voltage components generates a distribution with a larger variance and a smaller average degree. This result suggests that the topology of a power grid may converge to a highly skewed degree distribution if more low-voltage data is considered. Moreover, when compared to ER random and BA scale-free networks, the KPG has a lower efficiency and a higher clustering coefficient, implying that highly clustered structure does not necessarily guarantee a functional efficiency of a network. Error and attack tolerance analysis, evaluated with efficiency, indicate that the KPG is more vulnerable to random or degree-based attacks than betweenness-based intentional attack. Cascading failure analysis with recovery mechanism demonstrates that resilience of the network depends on both tolerance capacity and recovery initiation time. Also, when the two factors are fixed, the KPG is most vulnerable among the three networks. Based on our analysis, we propose that the topology of power grids should be designed so the loads are homogeneously distributed, or functional hubs and their neighbors have high tolerance capacity to enhance resilience.

A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.

PubMed

Yan, Kang K; Zhao, Hongyu; Pang, Herbert

2017-12-06

High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.

The Collaborative Coordination of Special Interest Groups on the Telemedicine University Network (RUTE) in Brazil.

PubMed

de Lima Verde Brito, Thiago Delevidove; Baptista, Roberto Silva; de Lima Lopes, Paulo Roberto; Haddad, Ana Estela; Messina, Luiz Ary; Torres Pisa, Ivan

2015-01-01

In Brazil the Telemedicine University Network (Rede Universitária de Telemedicina RUTE) is an initiative that among others promotes collaboration between university hospitals, universities, and health professionals through information technology infrastructure and special interest groups (SIGs) support. This paper presents results of analyses on collaboration during implementation and coordination activities of RUTE SIGs. This study is based on descriptive statistics and data visualization previously collected by RUTE national coordination relative to the status in July 2014. The analysis through collaboration graph identified the strongest collaboration RUTE units. The graph also highlights the collaborative relationship of RUTE units in form of communities, the most collaborative with each other in a communion in the same SIGs, and the less the collaborative units in the network. It should be stated that the most active units are also the oldest in the community.

Measuring Road Network Vulnerability with Sensitivity Analysis

PubMed Central

Jun-qiang, Leng; Long-hai, Yang; Liu, Wei-yi; Zhao, Lin

2017-01-01

This paper focuses on the development of a method for road network vulnerability analysis, from the perspective of capacity degradation, which seeks to identify the critical infrastructures in the road network and the operational performance of the whole traffic system. This research involves defining the traffic utility index and modeling vulnerability of road segment, route, OD (Origin Destination) pair and road network. Meanwhile, sensitivity analysis method is utilized to calculate the change of traffic utility index due to capacity degradation. This method, compared to traditional traffic assignment, can improve calculation efficiency and make the application of vulnerability analysis to large actual road network possible. Finally, all the above models and calculation method is applied to actual road network evaluation to verify its efficiency and utility. This approach can be used as a decision-supporting tool for evaluating the performance of road network and identifying critical infrastructures in transportation planning and management, especially in the resource allocation for mitigation and recovery. PMID:28125706

Graph Representations of Flow and Transport in Fracture Networks using Machine Learning

NASA Astrophysics Data System (ADS)

Srinivasan, G.; Viswanathan, H. S.; Karra, S.; O'Malley, D.; Godinez, H. C.; Hagberg, A.; Osthus, D.; Mohd-Yusof, J.

2017-12-01

Flow and transport of fluids through fractured systems is governed by the properties and interactions at the micro-scale. Retaining information about the micro-structure such as fracture length, orientation, aperture and connectivity in mesh-based computational models results in solving for millions to billions of degrees of freedom and quickly renders the problem computationally intractable. Our approach depicts fracture networks graphically, by mapping fractures to nodes and intersections to edges, thereby greatly reducing computational burden. Additionally, we use machine learning techniques to build simulators on the graph representation, trained on data from the mesh-based high fidelity simulations to speed up computation by orders of magnitude. We demonstrate our methodology on ensembles of discrete fracture networks, dividing up the data into training and validation sets. Our machine learned graph-based solvers result in over 3 orders of magnitude speedup without any significant sacrifice in accuracy.

A graph-based evolutionary algorithm: Genetic Network Programming (GNP) and its extension using reinforcement learning.

PubMed

Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu

2007-01-01

This paper proposes a graph-based evolutionary algorithm called Genetic Network Programming (GNP). Our goal is to develop GNP, which can deal with dynamic environments efficiently and effectively, based on the distinguished expression ability of the graph (network) structure. The characteristics of GNP are as follows. 1) GNP programs are composed of a number of nodes which execute simple judgment/processing, and these nodes are connected by directed links to each other. 2) The graph structure enables GNP to re-use nodes, thus the structure can be very compact. 3) The node transition of GNP is executed according to its node connections without any terminal nodes, thus the past history of the node transition affects the current node to be used and this characteristic works as an implicit memory function. These structural characteristics are useful for dealing with dynamic environments. Furthermore, we propose an extended algorithm, "GNP with Reinforcement Learning (GNPRL)" which combines evolution and reinforcement learning in order to create effective graph structures and obtain better results in dynamic environments. In this paper, we applied GNP to the problem of determining agents' behavior to evaluate its effectiveness. Tileworld was used as the simulation environment. The results show some advantages for GNP over conventional methods.

Graphs, matrices, and the GraphBLAS: Seven good reasons

DOE PAGES

Kepner, Jeremy; Bader, David; Buluç, Aydın; ...

2015-01-01

The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istcbigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implementmore » a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.« less

Graph theory findings in the pathophysiology of temporal lobe epilepsy.

PubMed

Chiang, Sharon; Haneef, Zulfi

2014-07-01

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Accumulating evidence has shown that TLE is a disorder of abnormal epileptogenic networks, rather than focal sources. Graph theory allows for a network-based representation of TLE brain networks, and has potential to illuminate characteristics of brain topology conducive to TLE pathophysiology, including seizure initiation and spread. We review basic concepts which we believe will prove helpful in interpreting results rapidly emerging from graph theory research in TLE. In addition, we summarize the current state of graph theory findings in TLE as they pertain its pathophysiology. Several common findings have emerged from the many modalities which have been used to study TLE using graph theory, including structural MRI, diffusion tensor imaging, surface EEG, intracranial EEG, magnetoencephalography, functional MRI, cell cultures, simulated models, and mouse models, involving increased regularity of the interictal network configuration, altered local segregation and global integration of the TLE network, and network reorganization of temporal lobe and limbic structures. As different modalities provide different views of the same phenomenon, future studies integrating data from multiple modalities are needed to clarify findings and contribute to the formation of a coherent theory on the pathophysiology of TLE. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Quantifying loopy network architectures.

PubMed

Katifori, Eleni; Magnasco, Marcelo O

2012-01-01

Biology presents many examples of planar distribution and structural networks having dense sets of closed loops. An archetype of this form of network organization is the vasculature of dicotyledonous leaves, which showcases a hierarchically-nested architecture containing closed loops at many different levels. Although a number of approaches have been proposed to measure aspects of the structure of such networks, a robust metric to quantify their hierarchical organization is still lacking. We present an algorithmic framework, the hierarchical loop decomposition, that allows mapping loopy networks to binary trees, preserving in the connectivity of the trees the architecture of the original graph. We apply this framework to investigate computer generated graphs, such as artificial models and optimal distribution networks, as well as natural graphs extracted from digitized images of dicotyledonous leaves and vasculature of rat cerebral neocortex. We calculate various metrics based on the asymmetry, the cumulative size distribution and the Strahler bifurcation ratios of the corresponding trees and discuss the relationship of these quantities to the architectural organization of the original graphs. This algorithmic framework decouples the geometric information (exact location of edges and nodes) from the metric topology (connectivity and edge weight) and it ultimately allows us to perform a quantitative statistical comparison between predictions of theoretical models and naturally occurring loopy graphs.

Parallel Algorithms for Switching Edges in Heterogeneous Graphs.

PubMed

Bhuiyan, Hasanuzzaman; Khan, Maleq; Chen, Jiangzhuo; Marathe, Madhav

2017-06-01

An edge switch is an operation on a graph (or network) where two edges are selected randomly and one of their end vertices are swapped with each other. Edge switch operations have important applications in graph theory and network analysis, such as in generating random networks with a given degree sequence, modeling and analyzing dynamic networks, and in studying various dynamic phenomena over a network. The recent growth of real-world networks motivates the need for efficient parallel algorithms. The dependencies among successive edge switch operations and the requirement to keep the graph simple (i.e., no self-loops or parallel edges) as the edges are switched lead to significant challenges in designing a parallel algorithm. Addressing these challenges requires complex synchronization and communication among the processors leading to difficulties in achieving a good speedup by parallelization. In this paper, we present distributed memory parallel algorithms for switching edges in massive networks. These algorithms provide good speedup and scale well to a large number of processors. A harmonic mean speedup of 73.25 is achieved on eight different networks with 1024 processors. One of the steps in our edge switch algorithms requires the computation of multinomial random variables in parallel. This paper presents the first non-trivial parallel algorithm for the problem, achieving a speedup of 925 using 1024 processors.

Complete graph model for community detection

NASA Astrophysics Data System (ADS)

Sun, Peng Gang; Sun, Xiya

2017-04-01

Community detection brings plenty of considerable problems, which has attracted more attention for many years. This paper develops a new framework, which tries to measure the interior and the exterior of a community based on a same metric, complete graph model. In particular, the exterior is modeled as a complete bipartite. We partition a network into subnetworks by maximizing the difference between the interior and the exterior of the subnetworks. In addition, we compare our approach with some state of the art methods on computer-generated networks based on the LFR benchmark as well as real-world networks. The experimental results indicate that our approach obtains better results for community detection, is capable of splitting irregular networks and achieves perfect results on the karate network and the dolphin network.

Vulnerability survival analysis: a novel approach to vulnerability management

NASA Astrophysics Data System (ADS)

Farris, Katheryn A.; Sullivan, John; Cybenko, George

2017-05-01

Computer security vulnerabilities span across large, enterprise networks and have to be mitigated by security engineers on a routine basis. Presently, security engineers will assess their "risk posture" through quantifying the number of vulnerabilities with a high Common Vulnerability Severity Score (CVSS). Yet, little to no attention is given to the length of time by which vulnerabilities persist and survive on the network. In this paper, we review a novel approach to quantifying the length of time a vulnerability persists on the network, its time-to-death, and predictors of lower vulnerability survival rates. Our contribution is unique in that we apply the cox proportional hazards regression model to real data from an operational IT environment. This paper provides a mathematical overview of the theory behind survival analysis methods, a description of our vulnerability data, and an interpretation of the results.

What can graph theory tell us about word learning and lexical retrieval?

PubMed

Vitevitch, Michael S

2008-04-01

Graph theory and the new science of networks provide a mathematically rigorous approach to examine the development and organization of complex systems. These tools were applied to the mental lexicon to examine the organization of words in the lexicon and to explore how that structure might influence the acquisition and retrieval of phonological word-forms. Pajek, a program for large network analysis and visualization (V. Batagelj & A. Mvrar, 1998), was used to examine several characteristics of a network derived from a computerized database of the adult lexicon. Nodes in the network represented words, and a link connected two nodes if the words were phonological neighbors. The average path length and clustering coefficient suggest that the phonological network exhibits small-world characteristics. The degree distribution was fit better by an exponential rather than a power-law function. Finally, the network exhibited assortative mixing by degree. Some of these structural characteristics were also found in graphs that were formed by 2 simple stochastic processes suggesting that similar processes might influence the development of the lexicon. The graph theoretic perspective may provide novel insights about the mental lexicon and lead to future studies that help us better understand language development and processing.

Graph Design via Convex Optimization: Online and Distributed Perspectives

NASA Astrophysics Data System (ADS)

Meng, De

Network and graph have long been natural abstraction of relations in a variety of applications, e.g. transportation, power system, social network, communication, electrical circuit, etc. As a large number of computation and optimization problems are naturally defined on graphs, graph structures not only enable important properties of these problems, but also leads to highly efficient distributed and online algorithms. For example, graph separability enables the parallelism for computation and operation as well as limits the size of local problems. More interestingly, graphs can be defined and constructed in order to take best advantage of those problem properties. This dissertation focuses on graph structure and design in newly proposed optimization problems, which establish a bridge between graph properties and optimization problem properties. We first study a new optimization problem called Geodesic Distance Maximization Problem (GDMP). Given a graph with fixed edge weights, finding the shortest path, also known as the geodesic, between two nodes is a well-studied network flow problem. We introduce the Geodesic Distance Maximization Problem (GDMP): the problem of finding the edge weights that maximize the length of the geodesic subject to convex constraints on the weights. We show that GDMP is a convex optimization problem for a wide class of flow costs, and provide a physical interpretation using the dual. We present applications of the GDMP in various fields, including optical lens design, network interdiction, and resource allocation in the control of forest fires. We develop an Alternating Direction Method of Multipliers (ADMM) by exploiting specific problem structures to solve large-scale GDMP, and demonstrate its effectiveness in numerical examples. We then turn our attention to distributed optimization on graph with only local communication. Distributed optimization arises in a variety of applications, e.g. distributed tracking and localization, estimation problems in sensor networks, multi-agent coordination. Distributed optimization aims to optimize a global objective function formed by summation of coupled local functions over a graph via only local communication and computation. We developed a weighted proximal ADMM for distributed optimization using graph structure. This fully distributed, single-loop algorithm allows simultaneous updates and can be viewed as a generalization of existing algorithms. More importantly, we achieve faster convergence by jointly designing graph weights and algorithm parameters. Finally, we propose a new problem on networks called Online Network Formation Problem: starting with a base graph and a set of candidate edges, at each round of the game, player one first chooses a candidate edge and reveals it to player two, then player two decides whether to accept it; player two can only accept limited number of edges and make online decisions with the goal to achieve the best properties of the synthesized network. The network properties considered include the number of spanning trees, algebraic connectivity and total effective resistance. These network formation games arise in a variety of cooperative multiagent systems. We propose a primal-dual algorithm framework for the general online network formation game, and analyze the algorithm performance by the competitive ratio and regret.

Evaluation of Graph Pattern Matching Workloads in Graph Analysis Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hong, Seokyong; Lee, Sangkeun; Lim, Seung-Hwan

2016-01-01

Graph analysis has emerged as a powerful method for data scientists to represent, integrate, query, and explore heterogeneous data sources. As a result, graph data management and mining became a popular area of research, and led to the development of plethora of systems in recent years. Unfortunately, the number of emerging graph analysis systems and the wide range of applications, coupled with a lack of apples-to-apples comparisons, make it difficult to understand the trade-offs between different systems and the graph operations for which they are designed. A fair comparison of these systems is a challenging task for the following reasons:more » multiple data models, non-standardized serialization formats, various query interfaces to users, and diverse environments they operate in. To address these key challenges, in this paper we present a new benchmark suite by extending the Lehigh University Benchmark (LUBM) to cover the most common capabilities of various graph analysis systems. We provide the design process of the benchmark, which generalizes the workflow for data scientists to conduct the desired graph analysis on different graph analysis systems. Equipped with this extended benchmark suite, we present performance comparison for nine subgraph pattern retrieval operations over six graph analysis systems, namely NetworkX, Neo4j, Jena, Titan, GraphX, and uRiKA. Through the proposed benchmark suite, this study reveals both quantitative and qualitative findings in (1) implications in loading data into each system; (2) challenges in describing graph patterns for each query interface; and (3) different sensitivity of each system to query selectivity. We envision that this study will pave the road for: (i) data scientists to select the suitable graph analysis systems, and (ii) data management system designers to advance graph analysis systems.« less

Protein Inference from the Integration of Tandem MS Data and Interactome Networks.

PubMed

Zhong, Jiancheng; Wang, Jianxing; Ding, Xiaojun; Zhang, Zhen; Li, Min; Wu, Fang-Xiang; Pan, Yi

2017-01-01

Since proteins are digested into a mixture of peptides in the preprocessing step of tandem mass spectrometry (MS), it is difficult to determine which specific protein a shared peptide belongs to. In recent studies, besides tandem MS data and peptide identification information, some other information is exploited to infer proteins. Different from the methods which first use only tandem MS data to infer proteins and then use network information to refine them, this study proposes a protein inference method named TMSIN, which uses interactome networks directly. As two interacting proteins should co-exist, it is reasonable to assume that if one of the interacting proteins is confidently inferred in a sample, its interacting partners should have a high probability in the same sample, too. Therefore, we can use the neighborhood information of a protein in an interactome network to adjust the probability that the shared peptide belongs to the protein. In TMSIN, a multi-weighted graph is constructed by incorporating the bipartite graph with interactome network information, where the bipartite graph is built with the peptide identification information. Based on multi-weighted graphs, TMSIN adopts an iterative workflow to infer proteins. At each iterative step, the probability that a shared peptide belongs to a specific protein is calculated by using the Bayes' law based on the neighbor protein support scores of each protein which are mapped by the shared peptides. We carried out experiments on yeast data and human data to evaluate the performance of TMSIN in terms of ROC, q-value, and accuracy. The experimental results show that AUC scores yielded by TMSIN are 0.742 and 0.874 in yeast dataset and human dataset, respectively, and TMSIN yields the maximum number of true positives when q-value less than or equal to 0.05. The overlap analysis shows that TMSIN is an effective complementary approach for protein inference.

Identification of lethal reactions in the Esherichia coli metabolic network: Graph theory approach

NASA Astrophysics Data System (ADS)

Ghim, C.-M.; Goh, K.-I.; Kahng, B.; Kim, D.

2004-03-01

As a first step toward holistic modeling of cells, we analyze the biochemical reactions occurring in the genome-scale metabolism of Esherichia coli. To this end, we construct a directed bipartite graph by assigning metabolite or reaction to each node. We apply various measures of centrality, a well-known concept in the graph theory, and their modifications to the metabolic network, finding that there exist lethal reactions involved in the central metabolism. Such lethal reactions or associated enzymes under diverse environments in silico are identified and compared with earlier results obtained from flux balance analysis.

Network discovery with DCM

PubMed Central

Friston, Karl J.; Li, Baojuan; Daunizeau, Jean; Stephan, Klaas E.

2011-01-01

This paper is about inferring or discovering the functional architecture of distributed systems using Dynamic Causal Modelling (DCM). We describe a scheme that recovers the (dynamic) Bayesian dependency graph (connections in a network) using observed network activity. This network discovery uses Bayesian model selection to identify the sparsity structure (absence of edges or connections) in a graph that best explains observed time-series. The implicit adjacency matrix specifies the form of the network (e.g., cyclic or acyclic) and its graph-theoretical attributes (e.g., degree distribution). The scheme is illustrated using functional magnetic resonance imaging (fMRI) time series to discover functional brain networks. Crucially, it can be applied to experimentally evoked responses (activation studies) or endogenous activity in task-free (resting state) fMRI studies. Unlike conventional approaches to network discovery, DCM permits the analysis of directed and cyclic graphs. Furthermore, it eschews (implausible) Markovian assumptions about the serial independence of random fluctuations. The scheme furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks. The networks are characterised in terms of their connectivity or adjacency matrices and conditional distributions over the directed (and reciprocal) effective connectivity between connected nodes or regions. We envisage that this approach will provide a useful complement to current analyses of functional connectivity for both activation and resting-state studies. PMID:21182971

Temporal networks

NASA Astrophysics Data System (ADS)

Holme, Petter; Saramäki, Jari

2012-10-01

A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via e-mail, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names-temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered, but does not attempt to unify related terminology-rather, we want to make papers readable across disciplines.

Cyber security risk assessment for SCADA and DCS networks.

PubMed

Ralston, P A S; Graham, J H; Hieb, J L

2007-10-01

The growing dependence of critical infrastructures and industrial automation on interconnected physical and cyber-based control systems has resulted in a growing and previously unforeseen cyber security threat to supervisory control and data acquisition (SCADA) and distributed control systems (DCSs). It is critical that engineers and managers understand these issues and know how to locate the information they need. This paper provides a broad overview of cyber security and risk assessment for SCADA and DCS, introduces the main industry organizations and government groups working in this area, and gives a comprehensive review of the literature to date. Major concepts related to the risk assessment methods are introduced with references cited for more detail. Included are risk assessment methods such as HHM, IIM, and RFRM which have been applied successfully to SCADA systems with many interdependencies and have highlighted the need for quantifiable metrics. Presented in broad terms is probability risk analysis (PRA) which includes methods such as FTA, ETA, and FEMA. The paper concludes with a general discussion of two recent methods (one based on compromise graphs and one on augmented vulnerability trees) that quantitatively determine the probability of an attack, the impact of the attack, and the reduction in risk associated with a particular countermeasure.

Assessing cortical synchronization during transcranial direct current stimulation: A graph-theoretical analysis.

PubMed

Mancini, Matteo; Brignani, Debora; Conforto, Silvia; Mauri, Piercarlo; Miniussi, Carlo; Pellicciari, Maria Concetta

2016-10-15

Transcranial direct current stimulation (tDCS) is a neuromodulation technique that can alter cortical excitability and modulate behaviour in a polarity-dependent way. Despite the widespread use of this method in the neuroscience field, its effects on ongoing local or global (network level) neuronal activity are still not foreseeable. A way to shed light on the neuronal mechanisms underlying the cortical connectivity changes induced by tDCS is provided by the combination of tDCS with electroencephalography (EEG). In this study, twelve healthy subjects underwent online tDCS-EEG recording (i.e., simultaneous), during resting-state, using 19 EEG channels. The protocol involved anodal, cathodal and sham stimulation conditions, with the active and the reference electrodes in the left frontocentral area (FC3) and on the forehead over the right eyebrow, respectively. The data were processed using a network model, based on graph theory and the synchronization likelihood. The resulting graphs were analysed for four frequency bands (theta, alpha, beta and gamma) to evaluate the presence of tDCS-induced differences in synchronization patterns and graph theory measures. The resting state network connectivity resulted altered during tDCS, in a polarity-specific manner for theta and alpha bands. Anodal tDCS weakened synchronization with respect to the baseline over the fronto-central areas in the left hemisphere, for theta band (p<0.05). In contrast, during cathodal tDCS a significant increase in inter-hemispheric synchronization connectivity was observed over the centro-parietal, centro-occipital and parieto-occipital areas for the alpha band (p<0.05). Local graph measures showed a tDCS-induced polarity-specific differences that regarded modifications of network activities rather than specific region properties. Our results show that applying tDCS during the resting state modulates local synchronization as well as network properties in slow frequency bands, in a polarity-specific manner. Copyright © 2016 Elsevier Inc. All rights reserved.

Framework based on communicability and flow to analyze complex network dynamics

NASA Astrophysics Data System (ADS)

Gilson, M.; Kouvaris, N. E.; Deco, G.; Zamora-López, G.

2018-05-01

Graph theory constitutes a widely used and established field providing powerful tools for the characterization of complex networks. The intricate topology of networks can also be investigated by means of the collective dynamics observed in the interactions of self-sustained oscillations (synchronization patterns) or propagationlike processes such as random walks. However, networks are often inferred from real-data-forming dynamic systems, which are different from those employed to reveal their topological characteristics. This stresses the necessity for a theoretical framework dedicated to the mutual relationship between the structure and dynamics in complex networks, as the two sides of the same coin. Here we propose a rigorous framework based on the network response over time (i.e., Green function) to study interactions between nodes across time. For this purpose we define the flow that describes the interplay between the network connectivity and external inputs. This multivariate measure relates to the concepts of graph communicability and the map equation. We illustrate our theory using the multivariate Ornstein-Uhlenbeck process, which describes stable and non-conservative dynamics, but the formalism can be adapted to other local dynamics for which the Green function is known. We provide applications to classical network examples, such as small-world ring and hierarchical networks. Our theory defines a comprehensive framework that is canonically related to directed and weighted networks, thus paving a way to revise the standards for network analysis, from the pairwise interactions between nodes to the global properties of networks including community detection.

Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods

PubMed Central

Phillips, David J.; McGlaughlin, Alec; Ruth, David; Jager, Leah R.; Soldan, Anja

2015-01-01

Graph theory is increasingly being used to study brain connectivity across the spectrum of Alzheimer's disease (AD), but prior findings have been inconsistent, likely reflecting methodological differences. We systematically investigated how methods of graph creation (i.e., type of correlation matrix and edge weighting) affect structural network properties and group differences. We estimated the structural connectivity of brain networks based on correlation maps of cortical thickness obtained from MRI. Four groups were compared: 126 cognitively normal older adults, 103 individuals with Mild Cognitive Impairment (MCI) who retained MCI status for at least 3 years (stable MCI), 108 individuals with MCI who progressed to AD-dementia within 3 years (progressive MCI), and 105 individuals with AD-dementia. Small-world measures of connectivity (characteristic path length and clustering coefficient) differed across groups, consistent with prior studies. Groups were best discriminated by the Randić index, which measures the degree to which highly connected nodes connect to other highly connected nodes. The Randić index differentiated the stable and progressive MCI groups, suggesting that it might be useful for tracking and predicting the progression of AD. Notably, however, the magnitude and direction of group differences in all three measures were dependent on the method of graph creation, indicating that it is crucial to take into account how graphs are constructed when interpreting differences across diagnostic groups and studies. The algebraic connectivity measures showed few group differences, independent of the method of graph construction, suggesting that global connectivity as it relates to node degree is not altered in early AD. PMID:25984446

Tokunaga river networks: New empirical evidence and applications to transport problems

NASA Astrophysics Data System (ADS)

Tejedor, A.; Zaliapin, I. V.

2013-12-01

The Tokunaga self-similarity has proven to be an important constraint for the observed river networks. Notably, various Horton laws are naturally satisfied by the Tokunaga networks, which makes this model of considerable interest for theoretical analysis and modeling of environmental transport. Recall that Horton self-similarity is a weaker property of a tree graph that addresses its principal branching; it is a counterpart of the power-law size distribution for system's elements. The stronger Tokunaga self-similarity addresses so-called side branching; it ensures that different levels of a hierarchy have the same probabilistic structure (in a sense that can be rigorously defined). We describe an improved statistical framework for testing self-similarity in a finite tree and estimating the related parameters. The developed inference is applied to the major river basins in continental United States and Iberian Peninsula. The results demonstrate the validity of the Tokunaga model for the majority of the examined networks with very narrow (universal) range of parameter values. Next, we explore possible relationships between the Tokunaga parameter anomalies (deviations from the universal values) and climatic and geomorphologic characteristics of a region. Finally, we apply the Tokunaga model to explore vulnerability of river networks, defined via reaction of the river discharge to a storm.

Efficient Graph-Based Resource Allocation Scheme Using Maximal Independent Set for Randomly- Deployed Small Star Networks

PubMed Central

Zhou, Jian; Wang, Lusheng; Wang, Weidong; Zhou, Qingfeng

2017-01-01

In future scenarios of heterogeneous and dense networks, randomly-deployed small star networks (SSNs) become a key paradigm, whose system performance is restricted to inter-SSN interference and requires an efficient resource allocation scheme for interference coordination. Traditional resource allocation schemes do not specifically focus on this paradigm and are usually too time consuming in dense networks. In this article, a very efficient graph-based scheme is proposed, which applies the maximal independent set (MIS) concept in graph theory to help divide SSNs into almost interference-free groups. We first construct an interference graph for the system based on a derived distance threshold indicating for any pair of SSNs whether there is intolerable inter-SSN interference or not. Then, SSNs are divided into MISs, and the same resource can be repetitively used by all the SSNs in each MIS. Empirical parameters and equations are set in the scheme to guarantee high performance. Finally, extensive scenarios both dense and nondense are randomly generated and simulated to demonstrate the performance of our scheme, indicating that it outperforms the classical max K-cut-based scheme in terms of system capacity, utility and especially time cost. Its achieved system capacity, utility and fairness can be close to the near-optimal strategy obtained by a time-consuming simulated annealing search. PMID:29113109

Altered Integration of Structural Covariance Networks in Young Children With Type 1 Diabetes.

PubMed

Hosseini, S M Hadi; Mazaika, Paul; Mauras, Nelly; Buckingham, Bruce; Weinzimer, Stuart A; Tsalikian, Eva; White, Neil H; Reiss, Allan L

2016-11-01

Type 1 diabetes mellitus (T1D), one of the most frequent chronic diseases in children, is associated with glucose dysregulation that contributes to an increased risk for neurocognitive deficits. While there is a bulk of evidence regarding neurocognitive deficits in adults with T1D, little is known about how early-onset T1D affects neural networks in young children. Recent data demonstrated widespread alterations in regional gray matter and white matter associated with T1D in young children. These widespread neuroanatomical changes might impact the organization of large-scale brain networks. In the present study, we applied graph-theoretical analysis to test whether the organization of structural covariance networks in the brain for a cohort of young children with T1D (N = 141) is altered compared to healthy controls (HC; N = 69). While the networks in both groups followed a small world organization-an architecture that is simultaneously highly segregated and integrated-the T1D network showed significantly longer path length compared with HC, suggesting reduced global integration of brain networks in young children with T1D. In addition, network robustness analysis revealed that the T1D network model showed more vulnerability to neural insult compared with HC. These results suggest that early-onset T1D negatively impacts the global organization of structural covariance networks and influences the trajectory of brain development in childhood. This is the first study to examine structural covariance networks in young children with T1D. Improving glycemic control for young children with T1D might help prevent alterations in brain networks in this population. Hum Brain Mapp 37:4034-4046, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Social networks: Evolving graphs with memory dependent edges

NASA Astrophysics Data System (ADS)

Grindrod, Peter; Parsons, Mark

2011-10-01

The plethora of digital communication technologies, and their mass take up, has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the existence or otherwise of certain infinite products and series involving age dependent model parameters. We show how to differentiate between the alternatives based on a finite set of observations. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

Automated visualization of rule-based models

PubMed Central

Tapia, Jose-Juan; Faeder, James R.

2017-01-01

Frameworks such as BioNetGen, Kappa and Simmune use “reaction rules” to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size. Also, inferring design motifs that emerge from biochemical interactions is an open problem, so current approaches to visualize model architecture rely on manual interpretation of the model. Here, we present three new visualization tools that constitute an automated visualization framework for rule-based models: (i) a compact rule visualization that efficiently displays each rule, (ii) the atom-rule graph that conveys regulatory interactions in the model as a bipartite network, and (iii) a tunable compression pipeline that incorporates expert knowledge and produces compact diagrams of model architecture when applied to the atom-rule graph. The compressed graphs convey network motifs and architectural features useful for understanding both small and large rule-based models, as we show by application to specific examples. Our tools also produce more readable diagrams than current approaches, as we show by comparing visualizations of 27 published models using standard graph metrics. We provide an implementation in the open source and freely available BioNetGen framework, but the underlying methods are general and can be applied to rule-based models from the Kappa and Simmune frameworks also. We expect that these tools will promote communication and analysis of rule-based models and their eventual integration into comprehensive whole-cell models. PMID:29131816

Software Defined Network Monitoring Scheme Using Spectral Graph Theory and Phantom Nodes

DTIC Science & Technology

2014-09-01

networks is the emergence of software - defined networking ( SDN ) [1]. SDN has existed for the...Chapter III for network monitoring. A. SOFTWARE DEFINED NETWORKS SDNs provide a new and innovative method to simplify network hardware by logically...and R. Giladi, “Performance analysis of software - defined networking ( SDN ),” in Proc. of IEEE 21st International Symposium on Modeling, Analysis

A local structure model for network analysis

DOE PAGES

Casleton, Emily; Nordman, Daniel; Kaiser, Mark

2017-04-01

The statistical analysis of networks is a popular research topic with ever widening applications. Exponential random graph models (ERGMs), which specify a model through interpretable, global network features, are common for this purpose. In this study we introduce a new class of models for network analysis, called local structure graph models (LSGMs). In contrast to an ERGM, a LSGM specifies a network model through local features and allows for an interpretable and controllable local dependence structure. In particular, LSGMs are formulated by a set of full conditional distributions for each network edge, e.g., the probability of edge presence/absence, depending onmore » neighborhoods of other edges. Additional model features are introduced to aid in specification and to help alleviate a common issue (occurring also with ERGMs) of model degeneracy. Finally, the proposed models are demonstrated on a network of tornadoes in Arkansas where a LSGM is shown to perform significantly better than a model without local dependence.« less

A local structure model for network analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Casleton, Emily; Nordman, Daniel; Kaiser, Mark

The statistical analysis of networks is a popular research topic with ever widening applications. Exponential random graph models (ERGMs), which specify a model through interpretable, global network features, are common for this purpose. In this study we introduce a new class of models for network analysis, called local structure graph models (LSGMs). In contrast to an ERGM, a LSGM specifies a network model through local features and allows for an interpretable and controllable local dependence structure. In particular, LSGMs are formulated by a set of full conditional distributions for each network edge, e.g., the probability of edge presence/absence, depending onmore » neighborhoods of other edges. Additional model features are introduced to aid in specification and to help alleviate a common issue (occurring also with ERGMs) of model degeneracy. Finally, the proposed models are demonstrated on a network of tornadoes in Arkansas where a LSGM is shown to perform significantly better than a model without local dependence.« less

Querying graphs in protein-protein interactions networks using feedback vertex set.

PubMed

Blin, Guillaume; Sikora, Florian; Vialette, Stéphane

2010-01-01

Recent techniques increase rapidly the amount of our knowledge on interactions between proteins. The interpretation of these new information depends on our ability to retrieve known substructures in the data, the Protein-Protein Interactions (PPIs) networks. In an algorithmic point of view, it is an hard task since it often leads to NP-hard problems. To overcome this difficulty, many authors have provided tools for querying patterns with a restricted topology, i.e., paths or trees in PPI networks. Such restriction leads to the development of fixed parameter tractable (FPT) algorithms, which can be practicable for restricted sizes of queries. Unfortunately, Graph Homomorphism is a W[1]-hard problem, and hence, no FPT algorithm can be found when patterns are in the shape of general graphs. However, Dost et al. gave an algorithm (which is not implemented) to query graphs with a bounded treewidth in PPI networks (the treewidth of the query being involved in the time complexity). In this paper, we propose another algorithm for querying pattern in the shape of graphs, also based on dynamic programming and the color-coding technique. To transform graphs queries into trees without loss of informations, we use feedback vertex set coupled to a node duplication mechanism. Hence, our algorithm is FPT for querying graphs with a bounded size of their feedback vertex set. It gives an alternative to the treewidth parameter, which can be better or worst for a given query. We provide a python implementation which allows us to validate our implementation on real data. Especially, we retrieve some human queries in the shape of graphs into the fly PPI network.

Change of Brain Functional Connectivity in Patients With Spinal Cord Injury: Graph Theory Based Approach.

PubMed

Min, Yu-Sun; Chang, Yongmin; Park, Jang Woo; Lee, Jong-Min; Cha, Jungho; Yang, Jin-Ju; Kim, Chul-Hyun; Hwang, Jong-Moon; Yoo, Ji-Na; Jung, Tae-Du

2015-06-01

To investigate the global functional reorganization of the brain following spinal cord injury with graph theory based approach by creating whole brain functional connectivity networks from resting state-functional magnetic resonance imaging (rs-fMRI), characterizing the reorganization of these networks using graph theoretical metrics and to compare these metrics between patients with spinal cord injury (SCI) and age-matched controls. Twenty patients with incomplete cervical SCI (14 males, 6 females; age, 55±14.1 years) and 20 healthy subjects (10 males, 10 females; age, 52.9±13.6 years) participated in this study. To analyze the characteristics of the whole brain network constructed with functional connectivity using rs-fMRI, graph theoretical measures were calculated including clustering coefficient, characteristic path length, global efficiency and small-worldness. Clustering coefficient, global efficiency and small-worldness did not show any difference between controls and SCIs in all density ranges. The normalized characteristic path length to random network was higher in SCI patients than in controls and reached statistical significance at 12%-13% of density (p<0.05, uncorrected). The graph theoretical approach in brain functional connectivity might be helpful to reveal the information processing after SCI. These findings imply that patients with SCI can build on preserved competent brain control. Further analyses, such as topological rearrangement and hub region identification, will be needed for better understanding of neuroplasticity in patients with SCI.

Terminal Sliding Mode-Based Consensus Tracking Control for Networked Uncertain Mechanical Systems on Digraphs.

PubMed

Chen, Gang; Song, Yongduan; Guan, Yanfeng

2018-03-01

This brief investigates the finite-time consensus tracking control problem for networked uncertain mechanical systems on digraphs. A new terminal sliding-mode-based cooperative control scheme is developed to guarantee that the tracking errors converge to an arbitrarily small bound around zero in finite time. All the networked systems can have different dynamics and all the dynamics are unknown. A neural network is used at each node to approximate the local unknown dynamics. The control schemes are implemented in a fully distributed manner. The proposed control method eliminates some limitations in the existing terminal sliding-mode-based consensus control methods and extends the existing analysis methods to the case of directed graphs. Simulation results on networked robot manipulators are provided to show the effectiveness of the proposed control algorithms.

Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moietymore » with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. Finally, we also give examples of new applications made possible by elucidating the atomic structure of conserved moieties.« less

Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

PubMed Central

Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

2016-01-01

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845

Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

DOE PAGES

Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

2016-11-21

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moietymore » with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. Finally, we also give examples of new applications made possible by elucidating the atomic structure of conserved moieties.« less

Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks.

PubMed

Haraldsdóttir, Hulda S; Fleming, Ronan M T

2016-11-01

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties.

Graph theoretical analysis of functional network for comprehension of sign language.

PubMed

Liu, Lanfang; Yan, Xin; Liu, Jin; Xia, Mingrui; Lu, Chunming; Emmorey, Karen; Chu, Mingyuan; Ding, Guosheng

2017-09-15

Signed languages are natural human languages using the visual-motor modality. Previous neuroimaging studies based on univariate activation analysis show that a widely overlapped cortical network is recruited regardless whether the sign language is comprehended (for signers) or not (for non-signers). Here we move beyond previous studies by examining whether the functional connectivity profiles and the underlying organizational structure of the overlapped neural network may differ between signers and non-signers when watching sign language. Using graph theoretical analysis (GTA) and fMRI, we compared the large-scale functional network organization in hearing signers with non-signers during the observation of sentences in Chinese Sign Language. We found that signed sentences elicited highly similar cortical activations in the two groups of participants, with slightly larger responses within the left frontal and left temporal gyrus in signers than in non-signers. Crucially, further GTA revealed substantial group differences in the topologies of this activation network. Globally, the network engaged by signers showed higher local efficiency (t (24) =2.379, p=0.026), small-worldness (t (24) =2.604, p=0.016) and modularity (t (24) =3.513, p=0.002), and exhibited different modular structures, compared to the network engaged by non-signers. Locally, the left ventral pars opercularis served as a network hub in the signer group but not in the non-signer group. These findings suggest that, despite overlap in cortical activation, the neural substrates underlying sign language comprehension are distinguishable at the network level from those for the processing of gestural action. Copyright © 2017 Elsevier B.V. All rights reserved.

Contact Graph Routing Enhancements Developed in ION for DTN

NASA Technical Reports Server (NTRS)

Segui, John S.; Burleigh, Scott

2013-01-01

The Interplanetary Overlay Network (ION) software suite is an open-source, flight-ready implementation of networking protocols including the Delay/Disruption Tolerant Networking (DTN) Bundle Protocol (BP), the CCSDS (Consultative Committee for Space Data Systems) File Delivery Protocol (CFDP), and many others including the Contact Graph Routing (CGR) DTN routing system. While DTN offers the capability to tolerate disruption and long signal propagation delays in transmission, without an appropriate routing protocol, no data can be delivered. CGR was built for space exploration networks with scheduled communication opportunities (typically based on trajectories and orbits), represented as a contact graph. Since CGR uses knowledge of future connectivity, the contact graph can grow rather large, and so efficient processing is desired. These enhancements allow CGR to scale to predicted NASA space network complexities and beyond. This software improves upon CGR by adopting an earliest-arrival-time cost metric and using the Dijkstra path selection algorithm. Moving to Dijkstra path selection also enables construction of an earliest- arrival-time tree for multicast routing. The enhancements have been rolled into ION 3.0 available on sourceforge.net.

PerSubs: A Graph-Based Algorithm for the Identification of Perturbed Subpathways Caused by Complex Diseases.

PubMed

Vrahatis, Aristidis G; Rapti, Angeliki; Sioutas, Spyros; Tsakalidis, Athanasios

2017-01-01

In the era of Systems Biology and growing flow of omics experimental data from high throughput techniques, experimentalists are in need of more precise pathway-based tools to unravel the inherent complexity of diseases and biological processes. Subpathway-based approaches are the emerging generation of pathway-based analysis elucidating the biological mechanisms under the perspective of local topologies onto a complex pathway network. Towards this orientation, we developed PerSub, a graph-based algorithm which detects subpathways perturbed by a complex disease. The perturbations are imprinted through differentially expressed and co-expressed subpathways as recorded by RNA-seq experiments. Our novel algorithm is applied on data obtained from a real experimental study and the identified subpathways provide biological evidence for the brain aging.

Metric learning with spectral graph convolutions on brain connectivity networks.

PubMed

Ktena, Sofia Ira; Parisot, Sarah; Ferrante, Enzo; Rajchl, Martin; Lee, Matthew; Glocker, Ben; Rueckert, Daniel

2018-04-01

Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non-trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non-matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods. Copyright © 2017 Elsevier Inc. All rights reserved.

Default mode network abnormalities in posttraumatic stress disorder: A novel network-restricted topology approach.

PubMed

Akiki, Teddy J; Averill, Christopher L; Wrocklage, Kristen M; Scott, J Cobb; Averill, Lynnette A; Schweinsburg, Brian; Alexander-Bloch, Aaron; Martini, Brenda; Southwick, Steven M; Krystal, John H; Abdallah, Chadi G

2018-08-01

Disruption in the default mode network (DMN) has been implicated in numerous neuropsychiatric disorders, including posttraumatic stress disorder (PTSD). However, studies have largely been limited to seed-based methods and involved inconsistent definitions of the DMN. Recent advances in neuroimaging and graph theory now permit the systematic exploration of intrinsic brain networks. In this study, we used resting-state functional magnetic resonance imaging (fMRI), diffusion MRI, and graph theoretical analyses to systematically examine the DMN connectivity and its relationship with PTSD symptom severity in a cohort of 65 combat-exposed US Veterans. We employed metrics that index overall connectivity strength, network integration (global efficiency), and network segregation (clustering coefficient). Then, we conducted a modularity and network-based statistical analysis to identify DMN regions of particular importance in PTSD. Finally, structural connectivity analyses were used to probe whether white matter abnormalities are associated with the identified functional DMN changes. We found decreased DMN functional connectivity strength to be associated with increased PTSD symptom severity. Further topological characterization suggests decreased functional integration and increased segregation in subjects with severe PTSD. Modularity analyses suggest a spared connectivity in the posterior DMN community (posterior cingulate, precuneus, angular gyrus) despite overall DMN weakened connections with increasing PTSD severity. Edge-wise network-based statistical analyses revealed a prefrontal dysconnectivity. Analysis of the diffusion networks revealed no alterations in overall strength or prefrontal structural connectivity. DMN abnormalities in patients with severe PTSD symptoms are characterized by decreased overall interconnections. On a finer scale, we found a pattern of prefrontal dysconnectivity, but increased cohesiveness in the posterior DMN community and relative sparing of connectivity in this region. The DMN measures established in this study may serve as a biomarker of disease severity and could have potential utility in developing circuit-based therapeutics. Published by Elsevier Inc.

Biomarker microRNAs for prostate cancer metastasis: screened with a network vulnerability analysis model.

PubMed

Lin, Yuxin; Chen, Feifei; Shen, Li; Tang, Xiaoyu; Du, Cui; Sun, Zhandong; Ding, Huijie; Chen, Jiajia; Shen, Bairong

2018-05-21

Prostate cancer (PCa) is a fatal malignant tumor among males in the world and the metastasis is a leading cause for PCa death. Biomarkers are therefore urgently needed to detect PCa metastatic signature at the early time. MicroRNAs are small non-coding RNAs with the potential to be biomarkers for disease prediction. In addition, computer-aided biomarker discovery is now becoming an attractive paradigm for precision diagnosis and prognosis of complex diseases. In this study, we identified key microRNAs as biomarkers for predicting PCa metastasis based on network vulnerability analysis. We first extracted microRNAs and mRNAs that were differentially expressed between primary PCa and metastatic PCa (MPCa) samples. Then we constructed the MPCa-specific microRNA-mRNA network and screened microRNA biomarkers by a novel bioinformatics model. The model emphasized the characterization of systems stability changes and the network vulnerability with three measurements, i.e. the structurally single-line regulation, the functional importance of microRNA targets and the percentage of transcription factor genes in microRNA unique targets. With this model, we identified five microRNAs as putative biomarkers for PCa metastasis. Among them, miR-101-3p and miR-145-5p have been previously reported as biomarkers for PCa metastasis and the remaining three, i.e. miR-204-5p, miR-198 and miR-152, were screened as novel biomarkers for PCa metastasis. The results were further confirmed by the assessment of their predictive power and biological function analysis. Five microRNAs were identified as candidate biomarkers for predicting PCa metastasis based on our network vulnerability analysis model. The prediction performance, literature exploration and functional enrichment analysis convinced our findings. This novel bioinformatics model could be applied to biomarker discovery for other complex diseases.

Evaluating the effect of aging on interference resolution with time-varying complex networks analysis

PubMed Central

Ariza, Pedro; Solesio-Jofre, Elena; Martínez, Johann H.; Pineda-Pardo, José A.; Niso, Guiomar; Maestú, Fernando; Buldú, Javier M.

2015-01-01

In this study we used graph theory analysis to investigate age-related reorganization of functional networks during the active maintenance of information that is interrupted by external interference. Additionally, we sought to investigate network differences before and after averaging network parameters between both maintenance and interference windows. We compared young and older adults by measuring their magnetoencephalographic recordings during an interference-based working memory task restricted to successful recognitions. Data analysis focused on the topology/temporal evolution of functional networks during both the maintenance and interference windows. We observed that: (a) Older adults require higher synchronization between cortical brain sites in order to achieve a successful recognition, (b) The main differences between age groups arise during the interference window, (c) Older adults show reduced ability to reorganize network topology when interference is introduced, and (d) Averaging network parameters leads to a loss of sensitivity to detect age differences. PMID:26029079

A system view and analysis of essential hypertension.

PubMed

Botzer, Alon; Grossman, Ehud; Moult, John; Unger, Ron

2018-05-01

The goal of this study was to investigate genes associated with essential hypertension from a system perspective, making use of bioinformatic tools to gain insights that are not evident when focusing at a detail-based resolution. Using various databases (pathways, Genome Wide Association Studies, knockouts etc.), we compiled a set of about 200 genes that play a major role in hypertension and identified the interactions between them. This enabled us to create a protein-protein interaction network graph, from which we identified key elements, based on graph centrality analysis. Enriched gene regulatory elements (transcription factors and microRNAs) were extracted by motif finding techniques and knowledge-based tools. We found that the network is composed of modules associated with functions such as water retention, endothelial vasoconstriction, sympathetic activity and others. We identified the transcription factor SP1 and the two microRNAs miR27 (a and b) and miR548c-3p that seem to play a major role in regulating the network as they exert their control over several modules and are not restricted to specific functions. We also noticed that genes involved in metabolic diseases (e.g. insulin) are central to the network. We view the blood-pressure regulation mechanism as a system-of-systems, composed of several contributing subsystems and pathways rather than a single module. The system is regulated by distributed elements. Understanding this mode of action can lead to a more precise treatment and drug target discovery. Our analysis suggests that insulin plays a primary role in hypertension, highlighting the tight link between essential hypertension and diseases associated with the metabolic syndrome.

Altered structural connectivity of pain-related brain network in burning mouth syndrome-investigation by graph analysis of probabilistic tractography.

PubMed

Wada, Akihiko; Shizukuishi, Takashi; Kikuta, Junko; Yamada, Haruyasu; Watanabe, Yusuke; Imamura, Yoshiki; Shinozaki, Takahiro; Dezawa, Ko; Haradome, Hiroki; Abe, Osamu

2017-05-01

Burning mouth syndrome (BMS) is a chronic intraoral pain syndrome featuring idiopathic oral pain and burning discomfort despite clinically normal oral mucosa. The etiology of chronic pain syndrome is unclear, but preliminary neuroimaging research has suggested the alteration of volume, metabolism, blood flow, and diffusion at multiple brain regions. According to the neuromatrix theory of Melzack, pain sense is generated in the brain by the network of multiple pain-related brain regions. Therefore, the alteration of pain-related network is also assumed as an etiology of chronic pain. In this study, we investigated the brain network of BMS brain by using probabilistic tractography and graph analysis. Fourteen BMS patients and 14 age-matched healthy controls underwent 1.5T MRI. Structural connectivity was calculated in 83 anatomically defined regions with probabilistic tractography of 60-axis diffusion tensor imaging and 3D T1-weighted imaging. Graph theory network analysis was used to evaluate the brain network at local and global connectivity. In BMS brain, a significant difference of local brain connectivity was recognized at the bilateral rostral anterior cingulate cortex, right medial orbitofrontal cortex, and left pars orbitalis which belong to the medial pain system; however, no significant difference was recognized at the lateral system including the somatic sensory cortex. A strengthened connection of the anterior cingulate cortex and medial prefrontal cortex with the basal ganglia, thalamus, and brain stem was revealed. Structural brain network analysis revealed the alteration of the medial system of the pain-related brain network in chronic pain syndrome.

Correlation based networks of equity returns sampled at different time horizons

NASA Astrophysics Data System (ADS)

Tumminello, M.; di Matteo, T.; Aste, T.; Mantegna, R. N.

2007-01-01

We investigate the planar maximally filtered graphs of the portfolio of the 300 most capitalized stocks traded at the New York Stock Exchange during the time period 2001 2003. Topological properties such as the average length of shortest paths, the betweenness and the degree are computed on different planar maximally filtered graphs generated by sampling the returns at different time horizons ranging from 5 min up to one trading day. This analysis confirms that the selected stocks compose a hierarchical system progressively structuring as the sampling time horizon increases. Finally, a cluster formation, associated to economic sectors, is quantitatively investigated.

PageRank without hyperlinks: Reranking with PubMed related article networks for biomedical text retrieval

PubMed Central

Lin, Jimmy

2008-01-01

Background Graph analysis algorithms such as PageRank and HITS have been successful in Web environments because they are able to extract important inter-document relationships from manually-created hyperlinks. We consider the application of these techniques to biomedical text retrieval. In the current PubMed® search interface, a MEDLINE® citation is connected to a number of related citations, which are in turn connected to other citations. Thus, a MEDLINE record represents a node in a vast content-similarity network. This article explores the hypothesis that these networks can be exploited for text retrieval, in the same manner as hyperlink graphs on the Web. Results We conducted a number of reranking experiments using the TREC 2005 genomics track test collection in which scores extracted from PageRank and HITS analysis were combined with scores returned by an off-the-shelf retrieval engine. Experiments demonstrate that incorporating PageRank scores yields significant improvements in terms of standard ranked-retrieval metrics. Conclusion The link structure of content-similarity networks can be exploited to improve the effectiveness of information retrieval systems. These results generalize the applicability of graph analysis algorithms to text retrieval in the biomedical domain. PMID:18538027

A Graph Oriented Approach for Network Forensic Analysis

ERIC Educational Resources Information Center

Wang, Wei

2010-01-01

Network forensic analysis is a process that analyzes intrusion evidence captured from networked environment to identify suspicious entities and stepwise actions in an attack scenario. Unfortunately, the overwhelming amount and low quality of output from security sensors make it difficult for analysts to obtain a succinct high-level view of complex…

NET: a new framework for the vectorization and examination of network data.

PubMed

Lasser, Jana; Katifori, Eleni

2017-01-01

The analysis of complex networks both in general and in particular as pertaining to real biological systems has been the focus of intense scientific attention in the past and present. In this paper we introduce two tools that provide fast and efficient means for the processing and quantification of biological networks like Drosophila tracheoles or leaf venation patterns: the Network Extraction Tool ( NET ) to extract data and the Graph-edit-GUI ( GeGUI ) to visualize and modify networks. NET is especially designed for high-throughput semi-automated analysis of biological datasets containing digital images of networks. The framework starts with the segmentation of the image and then proceeds to vectorization using methodologies from optical character recognition. After a series of steps to clean and improve the quality of the extracted data the framework produces a graph in which the network is represented only by its nodes and neighborhood-relations. The final output contains information about the adjacency matrix of the graph, the width of the edges and the positions of the nodes in space. NET also provides tools for statistical analysis of the network properties, such as the number of nodes or total network length. Other, more complex metrics can be calculated by importing the vectorized network to specialized network analysis packages. GeGUI is designed to facilitate manual correction of non-planar networks as these may contain artifacts or spurious junctions due to branches crossing each other. It is tailored for but not limited to the processing of networks from microscopy images of Drosophila tracheoles. The networks extracted by NET closely approximate the network depicted in the original image. NET is fast, yields reproducible results and is able to capture the full geometry of the network, including curved branches. Additionally GeGUI allows easy handling and visualization of the networks.

Detection of protein complex from protein-protein interaction network using Markov clustering

NASA Astrophysics Data System (ADS)

Ochieng, P. J.; Kusuma, W. A.; Haryanto, T.

2017-05-01

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.

Non-Markovian Infection Spread Dramatically Alters the Susceptible-Infected-Susceptible Epidemic Threshold in Networks

NASA Astrophysics Data System (ADS)

Van Mieghem, P.; van de Bovenkamp, R.

2013-03-01

Most studies on susceptible-infected-susceptible epidemics in networks implicitly assume Markovian behavior: the time to infect a direct neighbor is exponentially distributed. Much effort so far has been devoted to characterize and precisely compute the epidemic threshold in susceptible-infected-susceptible Markovian epidemics on networks. Here, we report the rather dramatic effect of a nonexponential infection time (while still assuming an exponential curing time) on the epidemic threshold by considering Weibullean infection times with the same mean, but different power exponent α. For three basic classes of graphs, the Erdős-Rényi random graph, scale-free graphs and lattices, the average steady-state fraction of infected nodes is simulated from which the epidemic threshold is deduced. For all graph classes, the epidemic threshold significantly increases with the power exponents α. Hence, real epidemics that violate the exponential or Markovian assumption can behave seriously differently than anticipated based on Markov theory.

A novel time series link prediction method: Learning automata approach

NASA Astrophysics Data System (ADS)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2017-09-01

Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

The Role of Intrinsic Brain Functional Connectivity in Vulnerability and Resilience to Bipolar Disorder.

PubMed

Doucet, Gaelle E; Bassett, Danielle S; Yao, Nailin; Glahn, David C; Frangou, Sophia

2017-12-01

Bipolar disorder is a heritable disorder characterized by mood dysregulation associated with brain functional dysconnectivity. Previous research has focused on the detection of risk- and disease-associated dysconnectivity in individuals with bipolar disorder and their first-degree relatives. The present study seeks to identify adaptive brain connectivity features associated with resilience, defined here as avoidance of illness or delayed illness onset in unaffected siblings of patients with bipolar disorder. Graph theoretical methods were used to examine global and regional brain network topology in head-motion-corrected resting-state functional MRI data acquired from 78 patients with bipolar disorder, 64 unaffected siblings, and 41 healthy volunteers. Global network properties were preserved in patients and their siblings while both groups showed reductions in the cohesiveness of the sensorimotor network. In the patient group, these sensorimotor network abnormalities were coupled with reduced integration of core default mode network regions in the ventromedial cortex and hippocampus. Conversely, integration of the default mode network was increased in the sibling group compared with both the patient group and the healthy volunteer group. The authors found that trait-related vulnerability to bipolar disorder was associated with reduced resting-state cohesiveness of the sensorimotor network in patients with bipolar disorder. However, integration of the default mode network emerged as a key feature differentiating disease expression and resilience between the patients and their siblings. This is indicative of the presence of neural mechanisms that may promote resilience, or at least delay illness onset.

Multiplex visibility graphs to investigate recurrent neural network dynamics

NASA Astrophysics Data System (ADS)

Bianchi, Filippo Maria; Livi, Lorenzo; Alippi, Cesare; Jenssen, Robert

2017-03-01

A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning them properly may be difficult and, typically, based on a trial-and-error approach. In this work, we adopt a graph-based framework to interpret and characterize internal dynamics of a class of RNNs called echo state networks (ESNs). We design principled unsupervised methods to derive hyperparameters configurations yielding maximal ESN performance, expressed in terms of prediction error and memory capacity. In particular, we propose to model time series generated by each neuron activations with a horizontal visibility graph, whose topological properties have been shown to be related to the underlying system dynamics. Successively, horizontal visibility graphs associated with all neurons become layers of a larger structure called a multiplex. We show that topological properties of such a multiplex reflect important features of ESN dynamics that can be used to guide the tuning of its hyperparamers. Results obtained on several benchmarks and a real-world dataset of telephone call data records show the effectiveness of the proposed methods.

Multiplex visibility graphs to investigate recurrent neural network dynamics

PubMed Central

Bianchi, Filippo Maria; Livi, Lorenzo; Alippi, Cesare; Jenssen, Robert

2017-01-01

A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning them properly may be difficult and, typically, based on a trial-and-error approach. In this work, we adopt a graph-based framework to interpret and characterize internal dynamics of a class of RNNs called echo state networks (ESNs). We design principled unsupervised methods to derive hyperparameters configurations yielding maximal ESN performance, expressed in terms of prediction error and memory capacity. In particular, we propose to model time series generated by each neuron activations with a horizontal visibility graph, whose topological properties have been shown to be related to the underlying system dynamics. Successively, horizontal visibility graphs associated with all neurons become layers of a larger structure called a multiplex. We show that topological properties of such a multiplex reflect important features of ESN dynamics that can be used to guide the tuning of its hyperparamers. Results obtained on several benchmarks and a real-world dataset of telephone call data records show the effectiveness of the proposed methods. PMID:28281563

Exponential stability of stochastic complex networks with multi-weights based on graph theory

NASA Astrophysics Data System (ADS)

Zhang, Chunmei; Chen, Tianrui

2018-04-01

In this paper, a novel approach to exponential stability of stochastic complex networks with multi-weights is investigated by means of the graph-theoretical method. New sufficient conditions are provided to ascertain the moment exponential stability and almost surely exponential stability of stochastic complex networks with multiple weights. It is noted that our stability results are closely related with multi-weights and the intensity of stochastic disturbance. Numerical simulations are also presented to substantiate the theoretical results.

F-RAG: Generating Atomic Coordinates from RNA Graphs by Fragment Assembly.

PubMed

Jain, Swati; Schlick, Tamar

2017-11-24

Coarse-grained models represent attractive approaches to analyze and simulate ribonucleic acid (RNA) molecules, for example, for structure prediction and design, as they simplify the RNA structure to reduce the conformational search space. Our structure prediction protocol RAGTOP (RNA-As-Graphs Topology Prediction) represents RNA structures as tree graphs and samples graph topologies to produce candidate graphs. However, for a more detailed study and analysis, construction of atomic from coarse-grained models is required. Here we present our graph-based fragment assembly algorithm (F-RAG) to convert candidate three-dimensional (3D) tree graph models, produced by RAGTOP into atomic structures. We use our related RAG-3D utilities to partition graphs into subgraphs and search for structurally similar atomic fragments in a data set of RNA 3D structures. The fragments are edited and superimposed using common residues, full atomic models are scored using RAGTOP's knowledge-based potential, and geometries of top scoring models is optimized. To evaluate our models, we assess all-atom RMSDs and Interaction Network Fidelity (a measure of residue interactions) with respect to experimentally solved structures and compare our results to other fragment assembly programs. For a set of 50 RNA structures, we obtain atomic models with reasonable geometries and interactions, particularly good for RNAs containing junctions. Additional improvements to our protocol and databases are outlined. These results provide a good foundation for further work on RNA structure prediction and design applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automatic system for detecting pornographic images

NASA Astrophysics Data System (ADS)

Ho, Kevin I. C.; Chen, Tung-Shou; Ho, Jun-Der

2002-09-01

Due to the dramatic growth of network and multimedia technology, people can more easily get variant information by using Internet. Unfortunately, it also makes the diffusion of illegal and harmful content much easier. So, it becomes an important topic for the Internet society to protect and safeguard Internet users from these content that may be encountered while surfing on the Net, especially children. Among these content, porno graphs cause more serious harm. Therefore, in this study, we propose an automatic system to detect still colour porno graphs. Starting from this result, we plan to develop an automatic system to search porno graphs or to filter porno graphs. Almost all the porno graphs possess one common characteristic that is the ratio of the size of skin region and non-skin region is high. Based on this characteristic, our system first converts the colour space from RGB colour space to HSV colour space so as to segment all the possible skin-colour regions from scene background. We also apply the texture analysis on the selected skin-colour regions to separate the skin regions from non-skin regions. Then, we try to group the adjacent pixels located in skin regions. If the ratio is over a given threshold, we can tell if the given image is a possible porno graph. Based on our experiment, less than 10% of non-porno graphs are classified as pornography, and over 80% of the most harmful porno graphs are classified correctly.

Small-World Brain Networks Revisited

PubMed Central

Bassett, Danielle S.; Bullmore, Edward T.

2016-01-01

It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex. PMID:27655008

The Robustness Analysis of Wireless Sensor Networks under Uncertain Interference

PubMed Central

Deng, Changjian

2013-01-01

Based on the complex network theory, robustness analysis of condition monitoring wireless sensor network under uncertain interference is present. In the evolution of the topology of sensor networks, the density weighted algebraic connectivity is taken into account, and the phenomenon of removing and repairing the link and node in the network is discussed. Numerical simulation is conducted to explore algebraic connectivity characteristics and network robustness performance. It is found that nodes density has the effect on algebraic connectivity distribution in the random graph model; high density nodes carry more connections, use more throughputs, and may be more unreliable. Moreover, the results show that, when network should be more error tolerant or robust by repairing nodes or adding new nodes, the network should be better clustered in median and high scale wireless sensor networks and be meshing topology in small scale networks. PMID:24363613

HIV Prevention and Sex Behaviors as Organizing Mechanisms in a Facebook Group Affiliation Network Among Young Black Men Who Have Sex with Men.

PubMed

Young, Lindsay E; Fujimoto, Kayo; Schneider, John A

2018-03-13

Online social networking sites (SNS)-the Internet-based platforms that enable connection and communication between users-are increasingly salient social environments for young adults and, consequently, offer tremendous opportunity for HIV behavioral research and intervention among vulnerable populations like young men who have sex with men (YMSM). Drawing from a cohort of 525 young Black MSM (YBMSM) living in Chicago, IL, USA April 2014-May 2015, we conducted social network analysis, estimating an exponential random graph model (ERGM) to model YBMSM's group affiliations on Facebook in relation to their sex behaviors and HIV prevention traits. A group's privacy setting-public, closed, or secret-was also modeled as a potential moderator of that relationship. Findings reveal that HIV positive individuals were more likely to affiliate with Facebook groups, while those who engaged in group sex were less likely to do so. When it came to the privacy of groups, we learned that HIV positive individuals tended not to belong to groups with greater privacy (e.g., closed and secret groups), while individuals who engaged in group sex and those who engaged in regular HIV testing were more likely to belong to those groups. Results also showed that individuals who engaged in condomless sex showed significant signs of clustering around the same set of groups. HIV positive individuals, on the other hand, were significantly less likely to demonstrate clustering. Implications for interventions and future research are discussed.

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning.

PubMed

Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C M

2016-03-01

A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The "competition" (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest--ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success. Copyright © 2015 Elsevier Ltd. All rights reserved.

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning

PubMed Central

Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C.M.

2016-01-01

A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The “competition” (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest – ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success. PMID:26866283

Vulnerability of countries to food-production crises propagating in the virtual water trade network

NASA Astrophysics Data System (ADS)

Tamea, S.; Laio, F.; Ridolfi, L.

2015-12-01

In recent years, the international trade of food and agricultural commodities has undergone a marked increase of exchanged volumes and an expansion of the trade network. This globalization of trade has both positive and negative effects, but the interconnectedness and external dependency of countries generate complex dynamics which are often difficult to understand and model. In this study we consider the volume of water used for the production of agricultural commodities, virtually exchanged among countries through commodity trade, i.e. the virtual water trade. Then, we set up a parsimonious mechanistic model describing the propagation, into the global trade network, of food-production crises generated locally by a social, economic or environmental event (such as war, economic crisis, drought, pest). The model, accounting for the network structure and the virtual water balance of all countries, bases on rules derived from observed virtual water flows and on data-based and statistically verified assumption. It is also tested on real case studies that prove its capability to capture the main features of crises propagation. The model is then employed as the basis for the development of an index of country vulnerability, measuring the exposure of countries to crises propagating in the virtual water trade network. Results of the analysis are discussed within the context of socio-economic and environmental conditions of countries, showing that not only water-scarce, but also wealthy and globalized countries, are among the most vulnerable to external crises. The temporal analysis for the period 1986-2011 reveals that the global average vulnerability has strongly increased over time, confirming the increased exposure of countries to external crises which may occur in the virtual water trade network.

Network-based model of the growth of termite nests

NASA Astrophysics Data System (ADS)

Eom, Young-Ho; Perna, Andrea; Fortunato, Santo; Darrouzet, Eric; Theraulaz, Guy; Jost, Christian

2015-12-01

We present a model for the growth of the transportation network inside nests of the social insect subfamily Termitinae (Isoptera, termitidae). These nests consist of large chambers (nodes) connected by tunnels (edges). The model based on the empirical analysis of the real nest networks combined with pruning (edge removal, either random or weighted by betweenness centrality) and a memory effect (preferential growth from the latest added chambers) successfully predicts emergent nest properties (degree distribution, size of the largest connected component, average path lengths, backbone link ratios, and local graph redundancy). The two pruning alternatives can be associated with different genuses in the subfamily. A sensitivity analysis on the pruning and memory parameters indicates that Termitinae networks favor fast internal transportation over efficient defense strategies against ant predators. Our results provide an example of how complex network organization and efficient network properties can be generated from simple building rules based on local interactions and contribute to our understanding of the mechanisms that come into play for the formation of termite networks and of biological transportation networks in general.

The correlation of metrics in complex networks with applications in functional brain networks

NASA Astrophysics Data System (ADS)

Li, C.; Wang, H.; de Haan, W.; Stam, C. J.; Van Mieghem, P.

2011-11-01

An increasing number of network metrics have been applied in network analysis. If metric relations were known better, we could more effectively characterize networks by a small set of metrics to discover the association between network properties/metrics and network functioning. In this paper, we investigate the linear correlation coefficients between widely studied network metrics in three network models (Bárabasi-Albert graphs, Erdös-Rényi random graphs and Watts-Strogatz small-world graphs) as well as in functional brain networks of healthy subjects. The metric correlations, which we have observed and theoretically explained, motivate us to propose a small representative set of metrics by including only one metric from each subset of mutually strongly dependent metrics. The following contributions are considered important. (a) A network with a given degree distribution can indeed be characterized by a small representative set of metrics. (b) Unweighted networks, which are obtained from weighted functional brain networks with a fixed threshold, and Erdös-Rényi random graphs follow a similar degree distribution. Moreover, their metric correlations and the resultant representative metrics are similar as well. This verifies the influence of degree distribution on metric correlations. (c) Most metric correlations can be explained analytically. (d) Interestingly, the most studied metrics so far, the average shortest path length and the clustering coefficient, are strongly correlated and, thus, redundant. Whereas spectral metrics, though only studied recently in the context of complex networks, seem to be essential in network characterizations. This representative set of metrics tends to both sufficiently and effectively characterize networks with a given degree distribution. In the study of a specific network, however, we have to at least consider the representative set so that important network properties will not be neglected.

What Can Graph Theory Tell Us About Word Learning and Lexical Retrieval?

PubMed Central

Vitevitch, Michael S.

2008-01-01

Purpose Graph theory and the new science of networks provide a mathematically rigorous approach to examine the development and organization of complex systems. These tools were applied to the mental lexicon to examine the organization of words in the lexicon and to explore how that structure might influence the acquisition and retrieval of phonological word-forms. Method Pajek, a program for large network analysis and visualization (V. Batagelj & A. Mvrar, 1998), was used to examine several characteristics of a network derived from a computerized database of the adult lexicon. Nodes in the network represented words, and a link connected two nodes if the words were phonological neighbors. Results The average path length and clustering coefficient suggest that the phonological network exhibits small-world characteristics. The degree distribution was fit better by an exponential rather than a power-law function. Finally, the network exhibited assortative mixing by degree. Some of these structural characteristics were also found in graphs that were formed by 2 simple stochastic processes suggesting that similar processes might influence the development of the lexicon. Conclusions The graph theoretic perspective may provide novel insights about the mental lexicon and lead to future studies that help us better understand language development and processing. PMID:18367686

A killer whale social network is vulnerable to targeted removals

PubMed Central

Williams, Rob; Lusseau, David

2006-01-01

Individuals play various roles in maintaining social integrity of mammalian populations. However, many models developed for managing wildlife resources assume that all individuals are equal. Killer whales are social animals that rely on relationships within and among family groups for survival. In the northeastern Pacific, fish-eating, ‘resident’ killer whale populations are composed of matrilines from which offspring do not disperse. We analysed the influence of various individuals' age, sex and matrilineal affiliation on their position in a social network. Here, we show that some matrilines appeared to play more central roles than others in the network. Furthermore, juvenile whales, especially females, appeared to play a central role in maintaining network cohesion. These two key findings were supported subsequently by simulating removal of different individuals. The network was robust to random removals; however, simulations that mimicked historic live-captures from the northeastern Pacific were likely to break the network graph into isolated groups. This finding raises concern regarding targeted takes, such as live-capture or drive fisheries, of matrilineal cetaceans. PMID:17148272

Model validation of simple-graph representations of metabolism

PubMed Central

Holme, Petter

2009-01-01

The large-scale properties of chemical reaction systems, such as metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information, lists of chemical reactions, available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure—network modularity (the propensity for edges to be clustered into dense groups that are sparsely connected between each other). To achieve this goal, we design a model of reaction systems where network modularity can be controlled and measure how well the reduction to simple graphs captures the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate–product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreates correlations between degree and mass of the molecules. PMID:19158012

Networking in Education: From Concept to Action--An Analytical View on the Educational Territories of Priority Intervention (TEIP) in Northern Portugal

ERIC Educational Resources Information Center

Silva, Marisa; da Silva, Sofia Marques; Araújo, Helena C

2017-01-01

This article presents an analysis of school principals' perspectives on networking concerning schools and school clusters from areas of social vulnerability (Educational Territories of Priority Intervention (TEIP)) in Northern Portugal. The meanings, purpose, benefits and difficulties of networking in education are examined, based on interviews…

Hemispheric asymmetry of electroencephalography-based functional brain networks.

PubMed

Jalili, Mahdi

2014-11-12

Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically meaningful graph theory metrics: global and local efficiency measures. The global efficiency did not show any hemispheric asymmetry, whereas the local connectivity showed rightward asymmetry for a range of intermediate density values for the constructed networks. Furthermore, the age of the participants showed significant direct correlations with the global efficiency of the left hemisphere, but only in the right hemisphere, with local connectivity. These results suggest that only local connectivity of EEG-based functional networks is associated with brain hemispheres.

Random sequential renormalization and agglomerative percolation in networks: application to Erdös-Rényi and scale-free graphs.

PubMed

Bizhani, Golnoosh; Grassberger, Peter; Paczuski, Maya

2011-12-01

We study the statistical behavior under random sequential renormalization (RSR) of several network models including Erdös-Rényi (ER) graphs, scale-free networks, and an annealed model related to ER graphs. In RSR the network is locally coarse grained by choosing at each renormalization step a node at random and joining it to all its neighbors. Compared to previous (quasi-)parallel renormalization methods [Song et al., Nature (London) 433, 392 (2005)], RSR allows a more fine-grained analysis of the renormalization group (RG) flow and unravels new features that were not discussed in the previous analyses. In particular, we find that all networks exhibit a second-order transition in their RG flow. This phase transition is associated with the emergence of a giant hub and can be viewed as a new variant of percolation, called agglomerative percolation. We claim that this transition exists also in previous graph renormalization schemes and explains some of the scaling behavior seen there. For critical trees it happens as N/N(0) → 0 in the limit of large systems (where N(0) is the initial size of the graph and N its size at a given RSR step). In contrast, it happens at finite N/N(0) in sparse ER graphs and in the annealed model, while it happens for N/N(0) → 1 on scale-free networks. Critical exponents seem to depend on the type of the graph but not on the average degree and obey usual scaling relations for percolation phenomena. For the annealed model they agree with the exponents obtained from a mean-field theory. At late times, the networks exhibit a starlike structure in agreement with the results of Radicchi et al. [Phys. Rev. Lett. 101, 148701 (2008)]. While degree distributions are of main interest when regarding the scheme as network renormalization, mass distributions (which are more relevant when considering "supernodes" as clusters) are much easier to study using the fast Newman-Ziff algorithm for percolation, allowing us to obtain very high statistics.

A new measure based on degree distribution that links information theory and network graph analysis

PubMed Central

2012-01-01

Background Detailed connection maps of human and nonhuman brains are being generated with new technologies, and graph metrics have been instrumental in understanding the general organizational features of these structures. Neural networks appear to have small world properties: they have clustered regions, while maintaining integrative features such as short average pathlengths. Results We captured the structural characteristics of clustered networks with short average pathlengths through our own variable, System Difference (SD), which is computationally simple and calculable for larger graph systems. SD is a Jaccardian measure generated by averaging all of the differences in the connection patterns between any two nodes of a system. We calculated SD over large random samples of matrices and found that high SD matrices have a low average pathlength and a larger number of clustered structures. SD is a measure of degree distribution with high SD matrices maximizing entropic properties. Phi (Φ), an information theory metric that assesses a system’s capacity to integrate information, correlated well with SD - with SD explaining over 90% of the variance in systems above 11 nodes (tested for 4 to 13 nodes). However, newer versions of Φ do not correlate well with the SD metric. Conclusions The new network measure, SD, provides a link between high entropic structures and degree distributions as related to small world properties. PMID:22726594

Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity

PubMed Central

2013-01-01

Background Graph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders. A current model proposes autism spectrum disorder (ASD) as a developmental disconnection syndrome, supported by converging evidence in both non-syndromic and syndromic ASD. However, the effects of abnormal connectivity on network properties have not been well studied, particularly in syndromic ASD. To close this gap, brain functional networks of electroencephalographic (EEG) connectivity were studied through graph measures in patients with Tuberous Sclerosis Complex (TSC), a disorder with a high prevalence of ASD, as well as in patients with non-syndromic ASD. Methods EEG data were collected from TSC patients with ASD (n = 14) and without ASD (n = 29), from patients with non-syndromic ASD (n = 16), and from controls (n = 46). First, EEG connectivity was characterized by the mean coherence, the ratio of inter- over intra-hemispheric coherence and the ratio of long- over short-range coherence. Next, graph measures of the functional networks were computed and a resilience analysis was conducted. To distinguish effects related to ASD from those related to TSC, a two-way analysis of covariance (ANCOVA) was applied, using age as a covariate. Results Analysis of network properties revealed differences specific to TSC and ASD, and these differences were very consistent across subgroups. In TSC, both with and without a concurrent diagnosis of ASD, mean coherence, global efficiency, and clustering coefficient were decreased and the average path length was increased. These findings indicate an altered network topology. In ASD, both with and without a concurrent diagnosis of TSC, decreased long- over short-range coherence and markedly increased network resilience were found. Conclusions The altered network topology in TSC represents a functional correlate of structural abnormalities and may play a role in the pathogenesis of neurological deficits. The increased resilience in ASD may reflect an excessively degenerate network with local overconnection and decreased functional specialization. This joint study of TSC and ASD networks provides a unique window to common neurobiological mechanisms in autism. PMID:23445896

Distributed Sensing and Processing: A Graphical Model Approach

DTIC Science & Technology

2005-11-30

that Ramanujan graph toplogies maximize the convergence rate of distributed detection consensus algorithms, improving over three orders of...small world type network designs. 14. SUBJECT TERMS Ramanujan graphs, sensor network topology, sensor network...that Ramanujan graphs, for which there are explicit algebraic constructions, have large eigenratios, converging much faster than structured graphs

Aircraft vulnerability analysis by modeling and simulation

NASA Astrophysics Data System (ADS)

Willers, Cornelius J.; Willers, Maria S.; de Waal, Alta

2014-10-01

Infrared missiles pose a significant threat to civilian and military aviation. ManPADS missiles are especially dangerous in the hands of rogue and undisciplined forces. Yet, not all the launched missiles hit their targets; the miss being either attributable to misuse of the weapon or to missile performance restrictions. This paper analyses some of the factors affecting aircraft vulnerability and demonstrates a structured analysis of the risk and aircraft vulnerability problem. The aircraft-missile engagement is a complex series of events, many of which are only partially understood. Aircraft and missile designers focus on the optimal design and performance of their respective systems, often testing only in a limited set of scenarios. Most missiles react to the contrast intensity, but the variability of the background is rarely considered. Finally, the vulnerability of the aircraft depends jointly on the missile's performance and the doctrine governing the missile's launch. These factors are considered in a holistic investigation. The view direction, altitude, time of day, sun position, latitude/longitude and terrain determine the background against which the aircraft is observed. Especially high gradients in sky radiance occur around the sun and on the horizon. This paper considers uncluttered background scenes (uniform terrain and clear sky) and presents examples of background radiance at all view angles across a sphere around the sensor. A detailed geometrical and spatially distributed radiometric model is used to model the aircraft. This model provides the signature at all possible view angles across the sphere around the aircraft. The signature is determined in absolute terms (no background) and in contrast terms (with background). It is shown that the background significantly affects the contrast signature as observed by the missile sensor. A simplified missile model is constructed by defining the thrust and mass profiles, maximum seeker tracking rate, maximum guidance acceleration and seeker sensitivity. For the purpose of this investigation the aircraft is equipped with conventional pyrotechnic decoy flares and the missile has no counter-countermeasure means (security restrictions on open publication). This complete simulation is used to calculate the missile miss distance, when the missile is launched from different locations around the aircraft. The miss distance data is then graphically presented showing miss distance (aircraft vulnerability) as a function of launch direction and range. The aircraft vulnerability graph accounts for aircraft and missile characteristics, but does not account for missile deployment doctrine. A Bayesian network is constructed to fuse the doctrinal rules with the aircraft vulnerability data. The Bayesian network now provides the capability to evaluate the combined risk of missile launch and aircraft vulnerability. It is shown in this paper that it is indeed possible to predict the aircraft vulnerability to missile attack in a comprehensive modelling and a holistic process. By using the appropriate real-world models, this approach is used to evaluate the effectiveness of specific countermeasure techniques against specific missile threats. The use of a Bayesian network provides the means to fuse simulated performance data with more abstract doctrinal rules to provide a realistic assessment of the aircraft vulnerability.

Changes in functional brain networks following sports-related concussion in adolescents.

PubMed

Virji-Babul, Naznin; Hilderman, Courtney G E; Makan, Nadia; Liu, Aiping; Smith-Forrester, Jenna; Franks, Chris; Wang, Z J

2014-12-01

Sports-related concussion is a major public health issue; however, little is known about the underlying changes in functional brain networks in adolescents following injury. Our aim was to use the tools from graph theory to evaluate the changes in brain network properties following concussion in adolescent athletes. We recorded resting state electroencephalography (EEG) in 33 healthy adolescent athletes and 9 adolescent athletes with a clinical diagnosis of subacute concussion. Graph theory analysis was applied to these data to evaluate changes in brain networks. Global and local metrics of the structural properties of the graph were calculated for each group and correlated with Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) scores. Brain networks of both groups showed small-world topology with no statistically significant differences in the global metrics; however, significant differences were found in the local metrics. Specifically, in the concussed group, we noted: 1) increased values of betweenness and degree in frontal electrode sites corresponding to the (R) dorsolateral prefrontal cortex and the (R) inferior frontal gyrus and 2) decreased values of degree in the region corresponding to the (R) frontopolar prefrontal cortex. In addition, there was significant negative correlation between degree and hub value, with total symptom score at the electrode site corresponding to the (R) prefrontal cortex. This preliminary report in adolescent athletes shows for the first time that resting-state EEG combined with graph theoretical analysis may provide an objective method of evaluating changes in brain networks following concussion. This approach may be useful in identifying individuals at risk for future injury.

Predicting catastrophes of non-autonomous networks with visibility graphs and horizontal visibility

NASA Astrophysics Data System (ADS)

Zhang, Haicheng; Xu, Daolin; Wu, Yousheng

2018-05-01

Prediction of potential catastrophes in engineering systems is a challenging problem. We first attempt to construct a complex network to predict catastrophes of a multi-modular floating system in advance of their occurrences. Response time series of the system can be mapped into an virtual network by using visibility graph or horizontal visibility algorithm. The topology characteristics of the networks can be used to forecast catastrophes of the system. Numerical results show that there is an obvious corresponding relationship between the variation of topology characteristics and the onset of catastrophes. A Catastrophe Index (CI) is proposed as a numerical indicator to measure a qualitative change from a stable state to a catastrophic state. The two approaches, the visibility graph and horizontal visibility algorithms, are compared by using the index in the reliability analysis with different data lengths and sampling frequencies. The technique of virtual network method is potentially extendable to catastrophe predictions of other engineering systems.

Test-Retest Reliability of Graph Metrics in Functional Brain Networks: A Resting-State fNIRS Study

PubMed Central

Niu, Haijing; Li, Zhen; Liao, Xuhong; Wang, Jinhui; Zhao, Tengda; Shu, Ni; Zhao, Xiaohu; He, Yong

2013-01-01

Recent research has demonstrated the feasibility of combining functional near-infrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the test-retest (TRT) reliability of the application of graph metrics to these networks remains to be elucidated. Here, we used resting-state fNIRS and a graph-theoretical approach to systematically address TRT reliability as it applies to various features of human brain networks, including functional connectivity, global network metrics and regional nodal centrality metrics. Eighteen subjects participated in two resting-state fNIRS scan sessions held ∼20 min apart. Functional brain networks were constructed for each subject by computing temporal correlations on three types of hemoglobin concentration information (HbO, HbR, and HbT). This was followed by a graph-theoretical analysis, and then an intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of each network metric. We observed that a large proportion of resting-state functional connections (∼90%) exhibited good reliability (0.6< ICC <0.74). For global and nodal measures, reliability was generally threshold-sensitive and varied among both network metrics and hemoglobin concentration signals. Specifically, the majority of global metrics exhibited fair to excellent reliability, with notably higher ICC values for the clustering coefficient (HbO: 0.76; HbR: 0.78; HbT: 0.53) and global efficiency (HbO: 0.76; HbR: 0.70; HbT: 0.78). Similarly, both nodal degree and efficiency measures also showed fair to excellent reliability across nodes (degree: 0.52∼0.84; efficiency: 0.50∼0.84); reliability was concordant across HbO, HbR and HbT and was significantly higher than that of nodal betweenness (0.28∼0.68). Together, our results suggest that most graph-theoretical network metrics derived from fNIRS are TRT reliable and can be used effectively for brain network research. This study also provides important guidance on the choice of network metrics of interest for future applied research in developmental and clinical neuroscience. PMID:24039763

Community structure from spectral properties in complex networks

NASA Astrophysics Data System (ADS)

Servedio, V. D. P.; Colaiori, F.; Capocci, A.; Caldarelli, G.

2005-06-01

We analyze the spectral properties of complex networks focusing on their relation to the community structure, and develop an algorithm based on correlations among components of different eigenvectors. The algorithm applies to general weighted networks, and, in a suitably modified version, to the case of directed networks. Our method allows to correctly detect communities in sharply partitioned graphs, however it is useful to the analysis of more complex networks, without a well defined cluster structure, as social and information networks. As an example, we test the algorithm on a large scale data-set from a psychological experiment of free word association, where it proves to be successful both in clustering words, and in uncovering mental association patterns.

A simple rule for the evolution of cooperation on graphs and social networks.

PubMed

Ohtsuki, Hisashi; Hauert, Christoph; Lieberman, Erez; Nowak, Martin A

2006-05-25

A fundamental aspect of all biological systems is cooperation. Cooperative interactions are required for many levels of biological organization ranging from single cells to groups of animals. Human society is based to a large extent on mechanisms that promote cooperation. It is well known that in unstructured populations, natural selection favours defectors over cooperators. There is much current interest, however, in studying evolutionary games in structured populations and on graphs. These efforts recognize the fact that who-meets-whom is not random, but determined by spatial relationships or social networks. Here we describe a surprisingly simple rule that is a good approximation for all graphs that we have analysed, including cycles, spatial lattices, random regular graphs, random graphs and scale-free networks: natural selection favours cooperation, if the benefit of the altruistic act, b, divided by the cost, c, exceeds the average number of neighbours, k, which means b/c > k. In this case, cooperation can evolve as a consequence of 'social viscosity' even in the absence of reputation effects or strategic complexity.

Network reconstruction via graph blending

NASA Astrophysics Data System (ADS)

Estrada, Rolando

2016-05-01

Graphs estimated from empirical data are often noisy and incomplete due to the difficulty of faithfully observing all the components (nodes and edges) of the true graph. This problem is particularly acute for large networks where the number of components may far exceed available surveillance capabilities. Errors in the observed graph can render subsequent analyses invalid, so it is vital to develop robust methods that can minimize these observational errors. Errors in the observed graph may include missing and spurious components, as well fused (multiple nodes are merged into one) and split (a single node is misinterpreted as many) nodes. Traditional graph reconstruction methods are only able to identify missing or spurious components (primarily edges, and to a lesser degree nodes), so we developed a novel graph blending framework that allows us to cast the full estimation problem as a simple edge addition/deletion problem. Armed with this framework, we systematically investigate the viability of various topological graph features, such as the degree distribution or the clustering coefficients, and existing graph reconstruction methods for tackling the full estimation problem. Our experimental results suggest that incorporating any topological feature as a source of information actually hinders reconstruction accuracy. We provide a theoretical analysis of this phenomenon and suggest several avenues for improving this estimation problem.

Complexity and approximability for a problem of intersecting of proximity graphs with minimum number of equal disks

NASA Astrophysics Data System (ADS)

Kobylkin, Konstantin

2016-10-01

Computational complexity and approximability are studied for the problem of intersecting of a set of straight line segments with the smallest cardinality set of disks of fixed radii r > 0 where the set of segments forms straight line embedding of possibly non-planar geometric graph. This problem arises in physical network security analysis for telecommunication, wireless and road networks represented by specific geometric graphs defined by Euclidean distances between their vertices (proximity graphs). It can be formulated in a form of known Hitting Set problem over a set of Euclidean r-neighbourhoods of segments. Being of interest computational complexity and approximability of Hitting Set over so structured sets of geometric objects did not get much focus in the literature. Strong NP-hardness of the problem is reported over special classes of proximity graphs namely of Delaunay triangulations, some of their connected subgraphs, half-θ6 graphs and non-planar unit disk graphs as well as APX-hardness is given for non-planar geometric graphs at different scales of r with respect to the longest graph edge length. Simple constant factor approximation algorithm is presented for the case where r is at the same scale as the longest edge length.

Phase-Space Detection of Cyber Events

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hernandez Jimenez, Jarilyn M; Ferber, Aaron E; Prowell, Stacy J

Energy Delivery Systems (EDS) are a network of processes that produce, transfer and distribute energy. EDS are increasingly dependent on networked computing assets, as are many Industrial Control Systems. Consequently, cyber-attacks pose a real and pertinent threat, as evidenced by Stuxnet, Shamoon and Dragonfly. Hence, there is a critical need for novel methods to detect, prevent, and mitigate effects of such attacks. To detect cyber-attacks in EDS, we developed a framework for gathering and analyzing timing data that involves establishing a baseline execution profile and then capturing the effect of perturbations in the state from injecting various malware. The datamore » analysis was based on nonlinear dynamics and graph theory to improve detection of anomalous events in cyber applications. The goal was the extraction of changing dynamics or anomalous activity in the underlying computer system. Takens' theorem in nonlinear dynamics allows reconstruction of topologically invariant, time-delay-embedding states from the computer data in a sufficiently high-dimensional space. The resultant dynamical states were nodes, and the state-to-state transitions were links in a mathematical graph. Alternatively, sequential tabulation of executing instructions provides the nodes with corresponding instruction-to-instruction links. Graph theorems guarantee graph-invariant measures to quantify the dynamical changes in the running applications. Results showed a successful detection of cyber events.« less

A binary-decision-diagram-based two-bit arithmetic logic unit on a GaAs-based regular nanowire network with hexagonal topology.

PubMed

Zhao, Hong-Quan; Kasai, Seiya; Shiratori, Yuta; Hashizume, Tamotsu

2009-06-17

A two-bit arithmetic logic unit (ALU) was successfully fabricated on a GaAs-based regular nanowire network with hexagonal topology. This fundamental building block of central processing units can be implemented on a regular nanowire network structure with simple circuit architecture based on graphical representation of logic functions using a binary decision diagram and topology control of the graph. The four-instruction ALU was designed by integrating subgraphs representing each instruction, and the circuitry was implemented by transferring the logical graph structure to a GaAs-based nanowire network formed by electron beam lithography and wet chemical etching. A path switching function was implemented in nodes by Schottky wrap gate control of nanowires. The fabricated circuit integrating 32 node devices exhibits the correct output waveforms at room temperature allowing for threshold voltage variation.

Deciding where to attend: Large-scale network mechanisms underlying attention and intention revealed by graph-theoretic analysis.

PubMed

Liu, Yuelu; Hong, Xiangfei; Bengson, Jesse J; Kelley, Todd A; Ding, Mingzhou; Mangun, George R

2017-08-15

The neural mechanisms by which intentions are transformed into actions remain poorly understood. We investigated the network mechanisms underlying spontaneous voluntary decisions about where to focus visual-spatial attention (willed attention). Graph-theoretic analysis of two independent datasets revealed that regions activated during willed attention form a set of functionally-distinct networks corresponding to the frontoparietal network, the cingulo-opercular network, and the dorsal attention network. Contrasting willed attention with instructed attention (where attention is directed by external cues), we observed that the dorsal anterior cingulate cortex was allied with the dorsal attention network in instructed attention, but shifted connectivity during willed attention to interact with the cingulo-opercular network, which then mediated communications between the frontoparietal network and the dorsal attention network. Behaviorally, greater connectivity in network hubs, including the dorsolateral prefrontal cortex, the dorsal anterior cingulate cortex, and the inferior parietal lobule, was associated with faster reaction times. These results, shown to be consistent across the two independent datasets, uncover the dynamic organization of functionally-distinct networks engaged to support intentional acts. Copyright © 2017 Elsevier Inc. All rights reserved.

Network analysis of mesoscale optical recordings to assess regional, functional connectivity.

PubMed

Lim, Diana H; LeDue, Jeffrey M; Murphy, Timothy H

2015-10-01

With modern optical imaging methods, it is possible to map structural and functional connectivity. Optical imaging studies that aim to describe large-scale neural connectivity often need to handle large and complex datasets. In order to interpret these datasets, new methods for analyzing structural and functional connectivity are being developed. Recently, network analysis, based on graph theory, has been used to describe and quantify brain connectivity in both experimental and clinical studies. We outline how to apply regional, functional network analysis to mesoscale optical imaging using voltage-sensitive-dye imaging and channelrhodopsin-2 stimulation in a mouse model. We include links to sample datasets and an analysis script. The analyses we employ can be applied to other types of fluorescence wide-field imaging, including genetically encoded calcium indicators, to assess network properties. We discuss the benefits and limitations of using network analysis for interpreting optical imaging data and define network properties that may be used to compare across preparations or other manipulations such as animal models of disease.

Quantum Graphical Models and Belief Propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leifer, M.S.; Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo Ont., N2L 2Y5; Poulin, D.

Belief Propagation algorithms acting on Graphical Models of classical probability distributions, such as Markov Networks, Factor Graphs and Bayesian Networks, are amongst the most powerful known methods for deriving probabilistic inferences amongst large numbers of random variables. This paper presents a generalization of these concepts and methods to the quantum case, based on the idea that quantum theory can be thought of as a noncommutative, operator-valued, generalization of classical probability theory. Some novel characterizations of quantum conditional independence are derived, and definitions of Quantum n-Bifactor Networks, Markov Networks, Factor Graphs and Bayesian Networks are proposed. The structure of Quantum Markovmore » Networks is investigated and some partial characterization results are obtained, along the lines of the Hammersley-Clifford theorem. A Quantum Belief Propagation algorithm is presented and is shown to converge on 1-Bifactor Networks and Markov Networks when the underlying graph is a tree. The use of Quantum Belief Propagation as a heuristic algorithm in cases where it is not known to converge is discussed. Applications to decoding quantum error correcting codes and to the simulation of many-body quantum systems are described.« less

A brief historical introduction to Euler's formula for polyhedra, topology, graph theory and networks

NASA Astrophysics Data System (ADS)

Debnath, Lokenath

2010-09-01

This article is essentially devoted to a brief historical introduction to Euler's formula for polyhedra, topology, theory of graphs and networks with many examples from the real-world. Celebrated Königsberg seven-bridge problem and some of the basic properties of graphs and networks for some understanding of the macroscopic behaviour of real physical systems are included. We also mention some important and modern applications of graph theory or network problems from transportation to telecommunications. Graphs or networks are effectively used as powerful tools in industrial, electrical and civil engineering, communication networks in the planning of business and industry. Graph theory and combinatorics can be used to understand the changes that occur in many large and complex scientific, technical and medical systems. With the advent of fast large computers and the ubiquitous Internet consisting of a very large network of computers, large-scale complex optimization problems can be modelled in terms of graphs or networks and then solved by algorithms available in graph theory. Many large and more complex combinatorial problems dealing with the possible arrangements of situations of various kinds, and computing the number and properties of such arrangements can be formulated in terms of networks. The Knight's tour problem, Hamilton's tour problem, problem of magic squares, the Euler Graeco-Latin squares problem and their modern developments in the twentieth century are also included.

Protein-Protein Interaction Network and Gene Ontology

NASA Astrophysics Data System (ADS)

Choi, Yunkyu; Kim, Seok; Yi, Gwan-Su; Park, Jinah

Evolution of computer technologies makes it possible to access a large amount and various kinds of biological data via internet such as DNA sequences, proteomics data and information discovered about them. It is expected that the combination of various data could help researchers find further knowledge about them. Roles of a visualization system are to invoke human abilities to integrate information and to recognize certain patterns in the data. Thus, when the various kinds of data are examined and analyzed manually, an effective visualization system is an essential part. One instance of these integrated visualizations can be combination of protein-protein interaction (PPI) data and Gene Ontology (GO) which could help enhance the analysis of PPI network. We introduce a simple but comprehensive visualization system that integrates GO and PPI data where GO and PPI graphs are visualized side-by-side and supports quick reference functions between them. Furthermore, the proposed system provides several interactive visualization methods for efficiently analyzing the PPI network and GO directedacyclic- graph such as context-based browsing and common ancestors finding.

Connectivity of Multi-Channel Fluvial Systems: A Comparison of Topology Metrics for Braided Rivers and Delta Networks

NASA Astrophysics Data System (ADS)

Tejedor, A.; Marra, W. A.; Addink, E. A.; Foufoula-Georgiou, E.; Kleinhans, M. G.

2016-12-01

Advancing quantitative understanding of the structure and dynamics of complex networks has transformed research in many fields as diverse as protein interactions in a cell to page connectivity in the World Wide Web and relationships in human societies. However, Geosciences have not benefited much from this new conceptual framework, although connectivity is at the center of many processes in hydro-geomorphology. One of the first efforts in this direction was the seminal work of Smart and Moruzzi (1971), proposing the use of graph theory for studying the intricate structure of delta channel networks. In recent years, this preliminary work has precipitated in a body of research that examines the connectivity of multiple-channel fluvial systems, such as delta networks and braided rivers. In this work, we compare two approaches recently introduced in the literature: (1) Marra et al. (2014) utilized network centrality measures to identify important channels in a braided section of the Jamuna River, and used the changes of bifurcations within the network over time to explain the overall river evolution; and (2) Tejedor et al. (2015a,b) developed a set of metrics to characterize the complexity of deltaic channel networks, as well as defined a vulnerability index that quantifies the relative change of sediment and water delivery to the shoreline outlets in response to upstream perturbations. Here we present a comparative analysis of metrics of centrality and vulnerability applied to both braided and deltaic channel networks to depict critical channels in those systems, i.e., channels where a change would contribute more substantially to overall system changes, and to understand what attributes of interest in a channel network are most succinctly depicted in what metrics. Marra, W. A., Kleinhans, M. G., & Addink, E. A. (2014). Earth Surface Processes and Landforms, doi:10.1002/esp.3482Smart, J. S., and V. L. Moruzzi (1971), Quantitative properties of delta channel networks, Tech. Rep. 3, 27 pp., IBM Thomas J. Watson Res. Cent., Yorktown, NYTejedor, A., Longjas, A., Zaliapin, I., & Foufoula-Georgiou, E. (2015a/b). Water Resources Research, doi:10.1002/2014WR016259 & doi:10.1002/2014WR016604

Optimal tree-stem bucking of northeastern species of China

Treesearch

Jingxin Wang; Chris B. LeDoux; Joseph McNeel

2004-01-01

An application of optimal tree-stem bucking to the northeastern tree species of China is reported. The bucking procedures used in this region are summarized, which are the basic guidelines for the optimal bucking design. The directed graph approach was adopted to generate the bucking patterns by using the network analysis labeling algorithm. A computer-based bucking...

Measuring Asymmetric Interactions in Resting State Brain Networks*

PubMed Central

Joshi, Anand A.; Salloum, Ronald; Bhushan, Chitresh; Leahy, Richard M.

2015-01-01

Directed graph representations of brain networks are increasingly being used in brain image analysis to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network. PMID:26221690

Evaluating the impact of bike network indicators on cyclist safety using macro-level collision prediction models.

PubMed

Osama, Ahmed; Sayed, Tarek

2016-12-01

Many cities worldwide are recognizing the important role that cycling plays in creating green and livable communities. However, vulnerable road users such as cyclists are usually subjected to an elevated level of injury risk which discourages many road users to cycle. This paper studies cyclist-vehicle collisions at 134 traffic analysis zones in the city of Vancouver to assess the impact of bike network structure on cyclist safety. Several network indicators were developed using Graph theory and their effect on cyclist safety was investigated. The indicators included measures of connectivity, directness, and topography of the bike network. The study developed several macro-level (zonal) collision prediction models that explicitly incorporated bike network indicators as explanatory variables. As well, the models incorporated the actual cyclist exposure (bike kilometers travelled) as opposed to relying on proxies such as population or bike network length. The macro-level collision prediction models were developed using generalized linear regression and full Bayesian techniques, with and without spatial effects. The models showed that cyclist collisions were positively associated with bike and vehicle exposure. The exponents of the exposure variables were less than one which supports the "safety in numbers" hypothesis. Moreover, the models showed positive associations between cyclist collisions and the bike network connectivity and linearity indicators. In contrast, negative associations were found between cyclist collisions and the bike network continuity and topography indicators. The spatial effects were statistically significant in all of the developed models. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Mapping environmental vulnerability from ETM + data in the Yellow River Mouth Area].

PubMed

Wang, Rui-Yan; Yu, Zhen-Wen; Xia, Yan-Ling; Wang, Xiang-Feng; Zhao, Geng-Xing; Jiang, Shu-Qian

2013-10-01

The environmental vulnerability retrieval is important to support continuing data. The spatial distribution of regional environmental vulnerability was got through remote sensing retrieval. In view of soil and vegetation, the environmental vulnerability evaluation index system was built, and the environmental vulnerability of sampling points was calculated by the AHP-fuzzy method, then the correlation between the sampling points environmental vulnerability and ETM + spectral reflectance ratio including some kinds of conversion data was analyzed to determine the sensitive spectral parameters. Based on that, models of correlation analysis, traditional regression, BP neural network and support vector regression were taken to explain the quantitative relationship between the spectral reflectance and the environmental vulnerability. With this model, the environmental vulnerability distribution was retrieved in the Yellow River Mouth Area. The results showed that the correlation between the environmental vulnerability and the spring NDVI, the September NDVI and the spring brightness was better than others, so they were selected as the sensitive spectral parameters. The model precision result showed that in addition to the support vector model, the other model reached the significant level. While all the multi-variable regression was better than all one-variable regression, and the model accuracy of BP neural network was the best. This study will serve as a reliable theoretical reference for the large spatial scale environmental vulnerability estimation based on remote sensing data.

Inference of Spatio-Temporal Functions Over Graphs via Multikernel Kriged Kalman Filtering

NASA Astrophysics Data System (ADS)

Ioannidis, Vassilis N.; Romero, Daniel; Giannakis, Georgios B.

2018-06-01

Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filter that accounts for the spatio-temporal variations, and offers efficient online reconstruction, even for dynamically evolving network topologies. The kernel-based learning framework bypasses the need for statistical information by capitalizing on the smoothness that graph signals exhibit with respect to the underlying graph. To address the challenge of selecting the appropriate kernel, the proposed filter is combined with a multi-kernel selection module. Such a data-driven method selects a kernel attuned to the signal dynamics on-the-fly within the linear span of a pre-selected dictionary. The novel multi-kernel learning algorithm exploits the eigenstructure of Laplacian kernel matrices to reduce computational complexity. Numerical tests with synthetic and real data demonstrate the superior reconstruction performance of the novel approach relative to state-of-the-art alternatives.

Fast determination of structurally cohesive subgroups in large networks

PubMed Central

Sinkovits, Robert S.; Moody, James; Oztan, B. Tolga; White, Douglas R.

2016-01-01

Structurally cohesive subgroups are a powerful and mathematically rigorous way to characterize network robustness. Their strength lies in the ability to detect strong connections among vertices that not only have no neighbors in common, but that may be distantly separated in the graph. Unfortunately, identifying cohesive subgroups is a computationally intensive problem, which has limited empirical assessments of cohesion to relatively small graphs of at most a few thousand vertices. We describe here an approach that exploits the properties of cliques, k-cores and vertex separators to iteratively reduce the complexity of the graph to the point where standard algorithms can be used to complete the analysis. As a proof of principle, we apply our method to the cohesion analysis of a 29,462-vertex biconnected component extracted from a 128,151-vertex co-authorship data set. PMID:28503215

Naming games in two-dimensional and small-world-connected random geometric networks.

PubMed

Lu, Qiming; Korniss, G; Szymanski, B K

2008-01-01

We investigate a prototypical agent-based model, the naming game, on two-dimensional random geometric networks. The naming game [Baronchelli, J. Stat. Mech.: Theory Exp. (2006) P06014] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the naming games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long-range communication links are added on top of the random geometric graph, resulting in a "small-world"-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case.

Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

NASA Astrophysics Data System (ADS)

Alshehhi, Rasha; Marpu, Prashanth Reddy

2017-04-01

Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

Biographer: web-based editing and rendering of SBGN compliant biochemical networks.

PubMed

Krause, Falko; Schulz, Marvin; Ripkens, Ben; Flöttmann, Max; Krantz, Marcus; Klipp, Edda; Handorf, Thomas

2013-06-01

The rapid accumulation of knowledge in the field of Systems Biology during the past years requires advanced, but simple-to-use, methods for the visualization of information in a structured and easily comprehensible manner. We have developed biographer, a web-based renderer and editor for reaction networks, which can be integrated as a library into tools dealing with network-related information. Our software enables visualizations based on the emerging standard Systems Biology Graphical Notation. It is able to import networks encoded in various formats such as SBML, SBGN-ML and jSBGN, a custom lightweight exchange format. The core package is implemented in HTML5, CSS and JavaScript and can be used within any kind of web-based project. It features interactive graph-editing tools and automatic graph layout algorithms. In addition, we provide a standalone graph editor and a web server, which contains enhanced features like web services for the import and export of models and visualizations in different formats. The biographer tool can be used at and downloaded from the web page http://biographer.biologie.hu-berlin.de/. The different software packages, including a server-independent version as well as a web server for Windows and Linux based systems, are available at http://code.google.com/p/biographer/ under the open-source license LGPL

G-Hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases.

PubMed

Wang, Xiaohong; Smalter, Aaron; Huan, Jun; Lushington, Gerald H

2009-01-01

Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important research topic. Graph similarity search has applications in a wide range of domains including cheminformatics, bioinformatics, sensor network management, social network management, and XML documents, among others.Most of the current graph indexing methods focus on subgraph query processing, i.e. determining the set of database graphs that contains the query graph and hence do not directly support similarity search. In data mining and machine learning, various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models for supervised learning, graph kernel functions have (i) high computational complexity and (ii) non-trivial difficulty to be indexed in a graph database.Our objective is to bridge graph kernel function and similarity search in graph databases by proposing (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. Our method of similarity measurement builds upon local features extracted from each node and their neighboring nodes in graphs. A hash table is utilized to support efficient storage and fast search of the extracted local features. Using the hash table, a graph kernel function is defined to capture the intrinsic similarity of graphs and for fast similarity query processing. We have implemented our method, which we have named G-hash, and have demonstrated its utility on large chemical graph databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Most importantly, the new similarity measurement and the index structure is scalable to large database with smaller indexing size, faster indexing construction time, and faster query processing time as compared to state-of-the-art indexing methods such as C-tree, gIndex, and GraphGrep.

Spectral analysis and slow spreading dynamics on complex networks.

PubMed

Odor, Géza

2013-09-01

The susceptible-infected-susceptible (SIS) model is one of the simplest memoryless systems for describing information or epidemic spreading phenomena with competing creation and spontaneous annihilation reactions. The effect of quenched disorder on the dynamical behavior has recently been compared to quenched mean-field (QMF) approximations in scale-free networks. QMF can take into account topological heterogeneity and clustering effects of the activity in the steady state by spectral decomposition analysis of the adjacency matrix. Therefore, it can provide predictions on possible rare-region effects, thus on the occurrence of slow dynamics. I compare QMF results of SIS with simulations on various large dimensional graphs. In particular, I show that for Erdős-Rényi graphs this method predicts correctly the occurrence of rare-region effects. It also provides a good estimate for the epidemic threshold in case of percolating graphs. Griffiths Phases emerge if the graph is fragmented or if we apply a strong, exponentially suppressing weighting scheme on the edges. The latter model describes the connection time distributions in the face-to-face experiments. In case of a generalized Barabási-Albert type of network with aging connections, strong rare-region effects and numerical evidence for Griffiths Phase dynamics are shown. The dynamical simulation results agree well with the predictions of the spectral analysis applied for the weighted adjacency matrices.

Using Networks To Understand Medical Data: The Case of Class III Malocclusions

PubMed Central

Scala, Antonio; Auconi, Pietro; Scazzocchio, Marco; Caldarelli, Guido; McNamara, James A.; Franchi, Lorenzo

2012-01-01

A system of elements that interact or regulate each other can be represented by a mathematical object called a network. While network analysis has been successfully applied to high-throughput biological systems, less has been done regarding their application in more applied fields of medicine; here we show an application based on standard medical diagnostic data. We apply network analysis to Class III malocclusion, one of the most difficult to understand and treat orofacial anomaly. We hypothesize that different interactions of the skeletal components can contribute to pathological disequilibrium; in order to test this hypothesis, we apply network analysis to 532 Class III young female patients. The topology of the Class III malocclusion obtained by network analysis shows a strong co-occurrence of abnormal skeletal features. The pattern of these occurrences influences the vertical and horizontal balance of disharmony in skeletal form and position. Patients with more unbalanced orthodontic phenotypes show preponderance of the pathological skeletal nodes and minor relevance of adaptive dentoalveolar equilibrating nodes. Furthermore, by applying Power Graphs analysis we identify some functional modules among orthodontic nodes. These modules correspond to groups of tightly inter-related features and presumably constitute the key regulators of plasticity and the sites of unbalance of the growing dentofacial Class III system. The data of the present study show that, in their most basic abstraction level, the orofacial characteristics can be represented as graphs using nodes to represent orthodontic characteristics, and edges to represent their various types of interactions. The applications of this mathematical model could improve the interpretation of the quantitative, patient-specific information, and help to better targeting therapy. Last but not least, the methodology we have applied in analyzing orthodontic features can be applied easily to other fields of the medical science. PMID:23028552

Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology

PubMed Central

Acencio, Marcio Luis; Bovolenta, Luiz Augusto; Camilo, Esther; Lemke, Ney

2013-01-01

Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype. PMID:24204854

Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy.

PubMed

Bernhardt, Boris C; Bonilha, Leonardo; Gross, Donald W

2015-09-01

Recent years have witnessed a paradigm shift in the study and conceptualization of epilepsy, which is increasingly understood as a network-level disorder. An emblematic case is temporal lobe epilepsy (TLE), the most common drug-resistant epilepsy that is electroclinically defined as a focal epilepsy and pathologically associated with hippocampal sclerosis. In this review, we will summarize histopathological, electrophysiological, and neuroimaging evidence supporting the concept that the substrate of TLE is not limited to the hippocampus alone, but rather is broadly distributed across multiple brain regions and interconnecting white matter pathways. We will introduce basic concepts of graph theory, a formalism to quantify topological properties of complex systems that has recently been widely applied to study networks derived from brain imaging and electrophysiology. We will discuss converging graph theoretical evidence indicating that networks in TLE show marked shifts in their overall topology, providing insight into the neurobiology of TLE as a network-level disorder. Our review will conclude by discussing methodological challenges and future clinical applications of this powerful analytical approach. Copyright © 2015 Elsevier Inc. All rights reserved.

An Xdata Architecture for Federated Graph Models and Multi-tier Asymmetric Computing

DTIC Science & Technology

2014-01-01

Wikipedia, a scale-free random graph (kron), Akamai trace route data, Bitcoin transaction data, and a Twitter follower network. We present results for...3x (SSSP on a random graph) and nearly 300x (Akamai and Bitcoin ) over the CPU performance of a well-known and widely deployed CPU-based graph...provided better throughput for smaller frontiers such as roadmaps or the Bitcoin data set. In our work, we have focused on two-phase kernels, but it

Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks

DOE PAGES

Baldwin, Nicole E.; Chesler, Elissa J.; Kirov, Stefan; ...

2005-01-01

Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively co-regulated genes and their annotation using gene ontology analysis and cis -regulatory element discovery. Themore » causal basis for co-regulation is detected through the use of quantitative trait locus mapping.« less

Diffusion Tensor Tractography Reveals Disrupted Structural Connectivity during Brain Aging

NASA Astrophysics Data System (ADS)

Lin, Lan; Tian, Miao; Wang, Qi; Wu, Shuicai

2017-10-01

Brain aging is one of the most crucial biological processes that entail many physical, biological, chemical, and psychological changes, and also a major risk factor for most common neurodegenerative diseases. To improve the quality of life for the elderly, it is important to understand how the brain is changed during the normal aging process. We compared diffusion tensor imaging (DTI)-based brain networks in a cohort of 75 healthy old subjects by using graph theory metrics to describe the anatomical networks and connectivity patterns, and network-based statistic (NBS) analysis was used to identify pairs of regions with altered structural connectivity. The NBS analysis revealed a significant network comprising nine distinct fiber bundles linking 10 different brain regions showed altered white matter structures in young-old group compare with middle-aged group (p < .05, family-wise error-corrected). Our results might guide future studies and help to gain a better understanding of brain aging.

EvoGraph: On-The-Fly Efficient Mining of Evolving Graphs on GPU

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sengupta, Dipanjan; Song, Shuaiwen

With the prevalence of the World Wide Web and social networks, there has been a growing interest in high performance analytics for constantly-evolving dynamic graphs. Modern GPUs provide massive AQ1 amount of parallelism for efficient graph processing, but the challenges remain due to their lack of support for the near real-time streaming nature of dynamic graphs. Specifically, due to the current high volume and velocity of graph data combined with the complexity of user queries, traditional processing methods by first storing the updates and then repeatedly running static graph analytics on a sequence of versions or snapshots are deemed undesirablemore » and computational infeasible on GPU. We present EvoGraph, a highly efficient and scalable GPU- based dynamic graph analytics framework.« less

Graph-based normalization and whitening for non-linear data analysis.

PubMed

Aaron, Catherine

2006-01-01

In this paper we construct a graph-based normalization algorithm for non-linear data analysis. The principle of this algorithm is to get a spherical average neighborhood with unit radius. First we present a class of global dispersion measures used for "global normalization"; we then adapt these measures using a weighted graph to build a local normalization called "graph-based" normalization. Then we give details of the graph-based normalization algorithm and illustrate some results. In the second part we present a graph-based whitening algorithm built by analogy between the "global" and the "local" problem.

Analysis of Social Network Measures with Respect to Structural Properties of Networks

DTIC Science & Technology

2012-03-01

there has been increased interest in degree based generators. The three generators that this thesis is interested in are the Erdos- Renyi (ER...these generators has their pros and cons. The ER graph generator was developed in 1960 by Erdos and Renyi in hopes of producing networks that...0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 P e rc e n ta ge Average Clustering Coefficient Erdös- Renyi BA (2 edge) BA (5 edge) BA (10 edge) PNDCG (α=2.35

Vulnerability of animal trade networks to the spread of infectious diseases: a methodological approach applied to evaluation and emergency control strategies in cattle, France, 2005.

PubMed

Rautureau, S; Dufour, B; Durand, B

2011-04-01

Besides farming, trade of livestock is a major component of agricultural economy. However, the networks generated by live animal movements are the major support for the propagation of infectious agents between farms, and their structure strongly affects how fast a disease may spread. Structural characteristics may thus be indicators of network vulnerability to the spread of infectious disease. The method proposed here is based upon the analysis of specific subnetworks: the giant strongly connected components (GSCs). Their existence, size and geographic extent are used to assess network vulnerability. Their disappearance when targeted nodes are removed allows studying how network vulnerability may be controlled under emergency conditions. The method was applied to the cattle trade network in France, 2005. Giant strongly connected components were present and widely spread all over the country in yearly, monthly and weekly networks. Among several tested approaches, the most efficient way to make GSCs disappear was based on the ranking of nodes by decreasing betweenness centrality (the proportion of shortest paths between nodes on which a specific node lies). Giant strongly connected components disappearance was obtained after removal of <1% of network nodes. Under emergency conditions, suspending animal trade activities in a small subset of holdings may thus allow to control the spread of an infectious disease through the animal trade network. Nodes representing markets and dealers were widely affected by these simulated control measures. This confirms their importance as 'hubs' for infectious diseases spread. Besides emergency conditions, specific sensitization and preventive measures should be dedicated to this population. © 2010 Blackwell Verlag GmbH.

Functional Brain Networks Develop from a “Local to Distributed” Organization

PubMed Central

Power, Jonathan D.; Dosenbach, Nico U. F.; Church, Jessica A.; Miezin, Francis M.; Schlaggar, Bradley L.; Petersen, Steven E.

2009-01-01

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways. PMID:19412534

Functional brain networks develop from a "local to distributed" organization.

PubMed

Fair, Damien A; Cohen, Alexander L; Power, Jonathan D; Dosenbach, Nico U F; Church, Jessica A; Miezin, Francis M; Schlaggar, Bradley L; Petersen, Steven E

2009-05-01

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength) between regions close in anatomical space and 'integration' (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.

Volatility behavior of visibility graph EMD financial time series from Ising interacting system

NASA Astrophysics Data System (ADS)

Zhang, Bo; Wang, Jun; Fang, Wen

2015-08-01

A financial market dynamics model is developed and investigated by stochastic Ising system, where the Ising model is the most popular ferromagnetic model in statistical physics systems. Applying two graph based analysis and multiscale entropy method, we investigate and compare the statistical volatility behavior of return time series and the corresponding IMF series derived from the empirical mode decomposition (EMD) method. And the real stock market indices are considered to be comparatively studied with the simulation data of the proposed model. Further, we find that the degree distribution of visibility graph for the simulation series has the power law tails, and the assortative network exhibits the mixing pattern property. All these features are in agreement with the real market data, the research confirms that the financial model established by the Ising system is reasonable.

Selective impairment of hippocampus and posterior hub areas in Alzheimer's disease: an MEG-based multiplex network study.

PubMed

Yu, Meichen; Engels, Marjolein M A; Hillebrand, Arjan; van Straaten, Elisabeth C W; Gouw, Alida A; Teunissen, Charlotte; van der Flier, Wiesje M; Scheltens, Philip; Stam, Cornelis J

2017-05-01

Although frequency-specific network analyses have shown that functional brain networks are altered in patients with Alzheimer's disease, the relationships between these frequency-specific network alterations remain largely unknown. Multiplex network analysis is a novel network approach to study complex systems consisting of subsystems with different types of connectivity patterns. In this study, we used magnetoencephalography to integrate five frequency-band specific brain networks in a multiplex framework. Previous structural and functional brain network studies have consistently shown that hub brain areas are selectively disrupted in Alzheimer's disease. Accordingly, we hypothesized that hub regions in the multiplex brain networks are selectively targeted in patients with Alzheimer's disease in comparison to healthy control subjects. Eyes-closed resting-state magnetoencephalography recordings from 27 patients with Alzheimer's disease (60.6 ± 5.4 years, 12 females) and 26 controls (61.8 ± 5.5 years, 14 females) were projected onto atlas-based regions of interest using beamforming. Subsequently, source-space time series for both 78 cortical and 12 subcortical regions were reconstructed in five frequency bands (delta, theta, alpha 1, alpha 2 and beta band). Multiplex brain networks were constructed by integrating frequency-specific magnetoencephalography networks. Functional connections between all pairs of regions of interests were quantified using a phase-based coupling metric, the phase lag index. Several multiplex hub and heterogeneity metrics were computed to capture both overall importance of each brain area and heterogeneity of the connectivity patterns across frequency-specific layers. Different nodal centrality metrics showed consistently that several hub regions, particularly left hippocampus, posterior parts of the default mode network and occipital regions, were vulnerable in patients with Alzheimer's disease compared to control subjects. Of note, these detected vulnerable hubs in Alzheimer's disease were absent in each individual frequency-specific network, thus showing the value of integrating the networks. The connectivity patterns of these vulnerable hub regions in the patients were heterogeneously distributed across layers. Perturbed cognitive function and abnormal cerebrospinal fluid amyloid-β42 levels correlated positively with the vulnerability of the hub regions in patients with Alzheimer's disease. Our analysis therefore demonstrates that the magnetoencephalography-based multiplex brain networks contain important information that cannot be revealed by frequency-specific brain networks. Furthermore, this indicates that functional networks obtained in different frequency bands do not act as independent entities. Overall, our multiplex network study provides an effective framework to integrate the frequency-specific networks with different frequency patterns and reveal neuropathological mechanism of hub disruption in Alzheimer's disease. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A framework for graph-based synthesis, analysis, and visualization of HPC cluster job data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayo, Jackson R.; Kegelmeyer, W. Philip, Jr.; Wong, Matthew H.

The monitoring and system analysis of high performance computing (HPC) clusters is of increasing importance to the HPC community. Analysis of HPC job data can be used to characterize system usage and diagnose and examine failure modes and their effects. This analysis is not straightforward, however, due to the complex relationships that exist between jobs. These relationships are based on a number of factors, including shared compute nodes between jobs, proximity of jobs in time, etc. Graph-based techniques represent an approach that is particularly well suited to this problem, and provide an effective technique for discovering important relationships in jobmore » queuing and execution data. The efficacy of these techniques is rooted in the use of a semantic graph as a knowledge representation tool. In a semantic graph job data, represented in a combination of numerical and textual forms, can be flexibly processed into edges, with corresponding weights, expressing relationships between jobs, nodes, users, and other relevant entities. This graph-based representation permits formal manipulation by a number of analysis algorithms. This report presents a methodology and software implementation that leverages semantic graph-based techniques for the system-level monitoring and analysis of HPC clusters based on job queuing and execution data. Ontology development and graph synthesis is discussed with respect to the domain of HPC job data. The framework developed automates the synthesis of graphs from a database of job information. It also provides a front end, enabling visualization of the synthesized graphs. Additionally, an analysis engine is incorporated that provides performance analysis, graph-based clustering, and failure prediction capabilities for HPC systems.« less

GraphStore: A Distributed Graph Storage System for Big Data Networks

ERIC Educational Resources Information Center

Martha, VenkataSwamy

2013-01-01

Networks, such as social networks, are a universal solution for modeling complex problems in real time, especially in the Big Data community. While previous studies have attempted to enhance network processing algorithms, none have paved a path for the development of a persistent storage system. The proposed solution, GraphStore, provides an…

Temporal Comparisons of Internet Topology

DTIC Science & Technology

2014-06-01

Number CAIDA Cooperative Association of Internet Data Analysis CDN Content Delivery Network CI Confidence Interval DoS denial of service GMT Greenwich...the CAIDA data. Our methods include analysis of graph theoretical measures as well as complex network and statistical measures that will quantify the...tool that probes the Internet for topology analysis and performance [26]. Scamper uses network diagnostic tools, such as traceroute and ping, to probe

GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harshaw, Chris R; Bridges, Robert A; Iannacone, Michael D

This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called \\textit{GraphPrints}. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets\\textemdash small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84\\% at the time-interval level, and 0.05\\% at the IP-level with 100\\% truemore » positive rates at both.« less

Small vulnerable sets determine large network cascades in power grids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Yang; Nishikawa, Takashi; Motter, Adilson E.

The understanding of cascading failures in complex systems has been hindered by the lack of realistic large-scale modeling and analysis that can account for variable system conditions. By using the North American power grid, we identified, quantified, and analyzed the set of network components that are vulnerable to cascading failures under any out of multiple conditions. We show that the vulnerable set consists of a small but topologically central portion of the network and that large cascades are disproportionately more likely to be triggered by initial failures close to this set. These results elucidate aspects of the origins and causesmore » of cascading failures relevant for grid design and operation and demonstrate vulnerability analysis methods that are applicable to a wider class of cascade-prone networks.« less

Small vulnerable sets determine large network cascades in power grids

DOE PAGES

Yang, Yang; Nishikawa, Takashi; Motter, Adilson E.

2017-11-17

The understanding of cascading failures in complex systems has been hindered by the lack of realistic large-scale modeling and analysis that can account for variable system conditions. By using the North American power grid, we identified, quantified, and analyzed the set of network components that are vulnerable to cascading failures under any out of multiple conditions. We show that the vulnerable set consists of a small but topologically central portion of the network and that large cascades are disproportionately more likely to be triggered by initial failures close to this set. These results elucidate aspects of the origins and causesmore » of cascading failures relevant for grid design and operation and demonstrate vulnerability analysis methods that are applicable to a wider class of cascade-prone networks.« less

Automated network analysis to measure brain effective connectivity estimated from EEG data of patients with alcoholism.

PubMed

Bae, Youngoh; Yoo, Byeong Wook; Lee, Jung Chan; Kim, Hee Chan

2017-05-01

Detection and diagnosis based on extracting features and classification using electroencephalography (EEG) signals are being studied vigorously. A network analysis of time series EEG signal data is one of many techniques that could help study brain functions. In this study, we analyze EEG to diagnose alcoholism. We propose a novel methodology to estimate the differences in the status of the brain based on EEG data of normal subjects and data from alcoholics by computing many parameters stemming from effective network using Granger causality. Among many parameters, only ten parameters were chosen as final candidates. By the combination of ten graph-based parameters, our results demonstrate predictable differences between alcoholics and normal subjects. A support vector machine classifier with best performance had 90% accuracy with sensitivity of 95.3%, and specificity of 82.4% for differentiating between the two groups.

Single-subject structural networks with closed-form rotation invariant matching mprove power in developmental studies of the cortex.

PubMed

Kandel, Benjamin M; Wang, Danny J J; Gee, James C; Avants, Brian B

2014-01-01

Although much attention has recently been focused on single-subject functional networks, using methods such as resting-state functional MRI, methods for constructing single-subject structural networks are in their infancy. Single-subject cortical networks aim to describe the self-similarity across the cortical structure, possibly signifying convergent developmental pathways. Previous methods for constructing single-subject cortical networks have used patch-based correlations and distance metrics based on curvature and thickness. We present here a method for constructing similarity-based cortical structural networks that utilizes a rotation-invariant representation of structure. The resulting graph metrics are closely linked to age and indicate an increasing degree of closeness throughout development in nearly all brain regions, perhaps corresponding to a more regular structure as the brain matures. The derived graph metrics demonstrate a four-fold increase in power for detecting age as compared to cortical thickness. This proof of concept study indicates that the proposed metric may be useful in identifying biologically relevant cortical patterns.

Simultaneous grouping pursuit and feature selection over an undirected graph*

PubMed Central

Zhu, Yunzhang; Shen, Xiaotong; Pan, Wei

2013-01-01

Summary In high-dimensional regression, grouping pursuit and feature selection have their own merits while complementing each other in battling the curse of dimensionality. To seek a parsimonious model, we perform simultaneous grouping pursuit and feature selection over an arbitrary undirected graph with each node corresponding to one predictor. When the corresponding nodes are reachable from each other over the graph, regression coefficients can be grouped, whose absolute values are the same or close. This is motivated from gene network analysis, where genes tend to work in groups according to their biological functionalities. Through a nonconvex penalty, we develop a computational strategy and analyze the proposed method. Theoretical analysis indicates that the proposed method reconstructs the oracle estimator, that is, the unbiased least squares estimator given the true grouping, leading to consistent reconstruction of grouping structures and informative features, as well as to optimal parameter estimation. Simulation studies suggest that the method combines the benefit of grouping pursuit with that of feature selection, and compares favorably against its competitors in selection accuracy and predictive performance. An application to eQTL data is used to illustrate the methodology, where a network is incorporated into analysis through an undirected graph. PMID:24098061

Constructing a Graph Database for Semantic Literature-Based Discovery.

PubMed

Hristovski, Dimitar; Kastrin, Andrej; Dinevski, Dejan; Rindflesch, Thomas C

2015-01-01

Literature-based discovery (LBD) generates discoveries, or hypotheses, by combining what is already known in the literature. Potential discoveries have the form of relations between biomedical concepts; for example, a drug may be determined to treat a disease other than the one for which it was intended. LBD views the knowledge in a domain as a network; a set of concepts along with the relations between them. As a starting point, we used SemMedDB, a database of semantic relations between biomedical concepts extracted with SemRep from Medline. SemMedDB is distributed as a MySQL relational database, which has some problems when dealing with network data. We transformed and uploaded SemMedDB into the Neo4j graph database, and implemented the basic LBD discovery algorithms with the Cypher query language. We conclude that storing the data needed for semantic LBD is more natural in a graph database. Also, implementing LBD discovery algorithms is conceptually simpler with a graph query language when compared with standard SQL.

Clinical correlates of graph theory findings in temporal lobe epilepsy.

PubMed

Haneef, Zulfi; Chiang, Sharon

2014-11-01

Temporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30-50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes. We performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE. Although different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed. Future studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility. Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Clinical correlates of graph theory findings in temporal lobe epilepsy

PubMed Central

Haneef, Zulfi; Chiang, Sharon

2014-01-01

Purpose Temporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30–50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes. Methods We performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE. Results Although different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed. Conclusions Future studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility. PMID:25127370

FUSE: a profit maximization approach for functional summarization of biological networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes; Yu, Hanry

2012-03-21

The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.

Visibility graph network analysis of natural gas price: The case of North American market

NASA Astrophysics Data System (ADS)

Sun, Mei; Wang, Yaqi; Gao, Cuixia

2016-11-01

Fluctuations in prices of natural gas significantly affect global economy. Therefore, the research on the characteristics of natural gas price fluctuations, turning points and its influencing cycle on the subsequent price series is of great significance. Global natural gas trade concentrates on three regional markets: the North American market, the European market and the Asia-Pacific market, with North America having the most developed natural gas financial market. In addition, perfect legal supervision and coordinated regulations make the North American market more open and more competitive. This paper focuses on the North American natural gas market specifically. The Henry Hub natural gas spot price time series is converted to a visibility graph network which provides a new direction for macro analysis of time series, and several indicators are investigated: degree and degree distribution, the average shortest path length and community structure. The internal mechanisms underlying price fluctuations are explored through the indicators. The results show that the natural gas prices visibility graph network (NGP-VGN) is of small-world and scale-free properties simultaneously. After random rearrangement of original price time series, the degree distribution of network becomes exponential distribution, different from the original ones. This means that, the original price time series is of long-range negative correlation fractal characteristic. In addition, nodes with large degree correspond to significant geopolitical or economic events. Communities correspond to time cycles in visibility graph network. The cycles of time series and the impact scope of hubs can be found by community structure partition.

diffuStats: an R package to compute diffusion-based scores on biological networks.

PubMed

Picart-Armada, Sergio; Thompson, Wesley K; Buil, Alfonso; Perera-Lluna, Alexandre

2018-02-01

Label propagation and diffusion over biological networks are a common mathematical formalism in computational biology for giving context to molecular entities and prioritizing novel candidates in the area of study. There are several choices in conceiving the diffusion process-involving the graph kernel, the score definitions and the presence of a posterior statistical normalization-which have an impact on the results. This manuscript describes diffuStats, an R package that provides a collection of graph kernels and diffusion scores, as well as a parallel permutation analysis for the normalized scores, that eases the computation of the scores and their benchmarking for an optimal choice. The R package diffuStats is publicly available in Bioconductor, https://bioconductor.org, under the GPL-3 license. sergi.picart@upc.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

The many faces of graph dynamics

NASA Astrophysics Data System (ADS)

Pignolet, Yvonne Anne; Roy, Matthieu; Schmid, Stefan; Tredan, Gilles

2017-06-01

The topological structure of complex networks has fascinated researchers for several decades, resulting in the discovery of many universal properties and reoccurring characteristics of different kinds of networks. However, much less is known today about the network dynamics: indeed, complex networks in reality are not static, but rather dynamically evolve over time. Our paper is motivated by the empirical observation that network evolution patterns seem far from random, but exhibit structure. Moreover, the specific patterns appear to depend on the network type, contradicting the existence of a ‘one fits it all’ model. However, we still lack observables to quantify these intuitions, as well as metrics to compare graph evolutions. Such observables and metrics are needed for extrapolating or predicting evolutions, as well as for interpolating graph evolutions. To explore the many faces of graph dynamics and to quantify temporal changes, this paper suggests to build upon the concept of centrality, a measure of node importance in a network. In particular, we introduce the notion of centrality distance, a natural similarity measure for two graphs which depends on a given centrality, characterizing the graph type. Intuitively, centrality distances reflect the extent to which (non-anonymous) node roles are different or, in case of dynamic graphs, have changed over time, between two graphs. We evaluate the centrality distance approach for five evolutionary models and seven real-world social and physical networks. Our results empirically show the usefulness of centrality distances for characterizing graph dynamics compared to a null-model of random evolution, and highlight the differences between the considered scenarios. Interestingly, our approach allows us to compare the dynamics of very different networks, in terms of scale and evolution speed.

Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning.

PubMed

Song, Youyi; Zhang, Ling; Chen, Siping; Ni, Dong; Lei, Baiying; Wang, Tianfu

2015-10-01

In this paper, a multiscale convolutional network (MSCN) and graph-partitioning-based method is proposed for accurate segmentation of cervical cytoplasm and nuclei. Specifically, deep learning via the MSCN is explored to extract scale invariant features, and then, segment regions centered at each pixel. The coarse segmentation is refined by an automated graph partitioning method based on the pretrained feature. The texture, shape, and contextual information of the target objects are learned to localize the appearance of distinctive boundary, which is also explored to generate markers to split the touching nuclei. For further refinement of the segmentation, a coarse-to-fine nucleus segmentation framework is developed. The computational complexity of the segmentation is reduced by using superpixel instead of raw pixels. Extensive experimental results demonstrate that the proposed cervical nucleus cell segmentation delivers promising results and outperforms existing methods.

Quantification of network structural dissimilarities.

PubMed

Schieber, Tiago A; Carpi, Laura; Díaz-Guilera, Albert; Pardalos, Panos M; Masoller, Cristina; Ravetti, Martín G

2017-01-09

Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.

PRIMAL: Page Rank-Based Indoor Mapping and Localization Using Gene-Sequenced Unlabeled WLAN Received Signal Strength

PubMed Central

Zhou, Mu; Zhang, Qiao; Xu, Kunjie; Tian, Zengshan; Wang, Yanmeng; He, Wei

2015-01-01

Due to the wide deployment of wireless local area networks (WLAN), received signal strength (RSS)-based indoor WLAN localization has attracted considerable attention in both academia and industry. In this paper, we propose a novel page rank-based indoor mapping and localization (PRIMAL) by using the gene-sequenced unlabeled WLAN RSS for simultaneous localization and mapping (SLAM). Specifically, first of all, based on the observation of the motion patterns of the people in the target environment, we use the Allen logic to construct the mobility graph to characterize the connectivity among different areas of interest. Second, the concept of gene sequencing is utilized to assemble the sporadically-collected RSS sequences into a signal graph based on the transition relations among different RSS sequences. Third, we apply the graph drawing approach to exhibit both the mobility graph and signal graph in a more readable manner. Finally, the page rank (PR) algorithm is proposed to construct the mapping from the signal graph into the mobility graph. The experimental results show that the proposed approach achieves satisfactory localization accuracy and meanwhile avoids the intensive time and labor cost involved in the conventional location fingerprinting-based indoor WLAN localization. PMID:26404274

A new graph-based method for pairwise global network alignment

PubMed Central

Klau, Gunnar W

2009-01-01

Background In addition to component-based comparative approaches, network alignments provide the means to study conserved network topology such as common pathways and more complex network motifs. Yet, unlike in classical sequence alignment, the comparison of networks becomes computationally more challenging, as most meaningful assumptions instantly lead to NP-hard problems. Most previous algorithmic work on network alignments is heuristic in nature. Results We introduce the graph-based maximum structural matching formulation for pairwise global network alignment. We relate the formulation to previous work and prove NP-hardness of the problem. Based on the new formulation we build upon recent results in computational structural biology and present a novel Lagrangian relaxation approach that, in combination with a branch-and-bound method, computes provably optimal network alignments. The Lagrangian algorithm alone is a powerful heuristic method, which produces solutions that are often near-optimal and – unlike those computed by pure heuristics – come with a quality guarantee. Conclusion Computational experiments on the alignment of protein-protein interaction networks and on the classification of metabolic subnetworks demonstrate that the new method is reasonably fast and has advantages over pure heuristics. Our software tool is freely available as part of the LISA library. PMID:19208162

Experimental Study of Quantum Graphs with Microwave Networks

NASA Astrophysics Data System (ADS)

Fu, Ziyuan; Koch, Trystan; Antonsen, Thomas; Ott, Edward; Anlage, Steven; Wave Chaos Team

An experimental setup consisting of microwave networks is used to simulate quantum graphs. The networks are constructed from coaxial cables connected by T junctions. The networks are built for operation both at room temperature and superconducting versions that operate at cryogenic temperatures. In the experiments, a phase shifter is connected to one of the network bonds to generate an ensemble of quantum graphs by varying the phase delay. The eigenvalue spectrum is found from S-parameter measurements on one-port graphs. With the experimental data, the nearest-neighbor spacing statistics and the impedance statistics of the graphs are examined. It is also demonstrated that time-reversal invariance for microwave propagation in the graphs can be broken without increasing dissipation significantly by making nodes with circulators. Random matrix theory (RMT) successfully describes universal statistical properties of the system. We acknowledge support under contract AFOSR COE Grant FA9550-15-1-0171.

Emergent spectral properties of river network topology: an optimal channel network approach.

PubMed

Abed-Elmdoust, Armaghan; Singh, Arvind; Yang, Zong-Liang

2017-09-13

Characterization of river drainage networks has been a subject of research for many years. However, most previous studies have been limited to quantities which are loosely connected to the topological properties of these networks. In this work, through a graph-theoretic formulation of drainage river networks, we investigate the eigenvalue spectra of their adjacency matrix. First, we introduce a graph theory model for river networks and explore the properties of the network through its adjacency matrix. Next, we show that the eigenvalue spectra of such complex networks follow distinct patterns and exhibit striking features including a spectral gap in which no eigenvalue exists as well as a finite number of zero eigenvalues. We show that such spectral features are closely related to the branching topology of the associated river networks. In this regard, we find an empirical relation for the spectral gap and nullity in terms of the energy dissipation exponent of the drainage networks. In addition, the eigenvalue distribution is found to follow a finite-width probability density function with certain skewness which is related to the drainage pattern. Our results are based on optimal channel network simulations and validated through examples obtained from physical experiments on landscape evolution. These results suggest the potential of the spectral graph techniques in characterizing and modeling river networks.

Visibility in the topology of complex networks

NASA Astrophysics Data System (ADS)

Tsiotas, Dimitrios; Charakopoulos, Avraam

2018-09-01

Taking its inspiration from the visibility algorithm, which was proposed by Lacasa et al. (2008) to convert a time-series into a complex network, this paper develops and proposes a novel expansion of this algorithm that allows generating a visibility graph from a complex network instead of a time-series that is currently applicable. The purpose of this approach is to apply the idea of visibility from the field of time-series to complex networks in order to interpret the network topology as a landscape. Visibility in complex networks is a multivariate property producing an associated visibility graph that maps the ability of a node "to see" other nodes in the network that lie beyond the range of its neighborhood, in terms of a control-attribute. Within this context, this paper examines the visibility topology produced by connectivity (degree) in comparison with the original (source) network, in order to detect what patterns or forces describe the mechanism under which a network is converted to a visibility graph. The overall analysis shows that visibility is a property that increases the connectivity in networks, it may contribute to pattern recognition (among which the detection of the scale-free topology) and it is worth to be applied to complex networks in order to reveal the potential of signal processing beyond the range of its neighborhood. Generally, this paper promotes interdisciplinary research in complex networks providing new insights to network science.

An efficient graph theory based method to identify every minimal reaction set in a metabolic network

PubMed Central

2014-01-01

Background Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels. Existing computational methods to identify minimal reaction sets in metabolic networks are computationally expensive. Further, they identify only one of the several possible minimal reaction sets. Results In this paper, we propose an efficient graph theory based recursive optimization approach to identify all minimal reaction sets. Graph theoretical insights offer systematic methods to not only reduce the number of variables in math programming and increase its computational efficiency, but also provide efficient ways to find multiple optimal solutions. The efficacy of the proposed approach is demonstrated using case studies from Escherichia coli and Saccharomyces cerevisiae. In case study 1, the proposed method identified three minimal reaction sets each containing 38 reactions in Escherichia coli central metabolic network with 77 reactions. Analysis of these three minimal reaction sets revealed that one of them is more suitable for developing minimal metabolism cell compared to other two due to practically achievable internal flux distribution. In case study 2, the proposed method identified 256 minimal reaction sets from the Saccharomyces cerevisiae genome scale metabolic network with 620 reactions. The proposed method required only 4.5 hours to identify all the 256 minimal reaction sets and has shown a significant reduction (approximately 80%) in the solution time when compared to the existing methods for finding minimal reaction set. Conclusions Identification of all minimal reactions sets in metabolic networks is essential since different minimal reaction sets have different properties that effect the bioprocess development. The proposed method correctly identified all minimal reaction sets in a both the case studies. The proposed method is computationally efficient compared to other methods for finding minimal reaction sets and useful to employ with genome-scale metabolic networks. PMID:24594118

Social Trust Prediction Using Heterogeneous Networks

PubMed Central

HUANG, JIN; NIE, FEIPING; HUANG, HENG; TU, YI-CHENG; LEI, YU

2014-01-01

Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method. PMID:24729776

Social Trust Prediction Using Heterogeneous Networks.

PubMed

Huang, Jin; Nie, Feiping; Huang, Heng; Tu, Yi-Cheng; Lei, Yu

2013-11-01

Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method.

Graph partitions and cluster synchronization in networks of oscillators

PubMed Central

Schaub, Michael T.; O’Clery, Neave; Billeh, Yazan N.; Delvenne, Jean-Charles; Lambiotte, Renaud; Barahona, Mauricio

2017-01-01

Synchronization over networks depends strongly on the structure of the coupling between the oscillators. When the coupling presents certain regularities, the dynamics can be coarse-grained into clusters by means of External Equitable Partitions of the network graph and their associated quotient graphs. We exploit this graph-theoretical concept to study the phenomenon of cluster synchronization, in which different groups of nodes converge to distinct behaviors. We derive conditions and properties of networks in which such clustered behavior emerges, and show that the ensuing dynamics is the result of the localization of the eigenvectors of the associated graph Laplacians linked to the existence of invariant subspaces. The framework is applied to both linear and non-linear models, first for the standard case of networks with positive edges, before being generalized to the case of signed networks with both positive and negative interactions. We illustrate our results with examples of both signed and unsigned graphs for consensus dynamics and for partial synchronization of oscillator networks under the master stability function as well as Kuramoto oscillators. PMID:27781454

Information Extraction for System-Software Safety Analysis: Calendar Year 2007 Year-End Report

NASA Technical Reports Server (NTRS)

Malin, Jane T.

2008-01-01

This annual report describes work to integrate a set of tools to support early model-based analysis of failures and hazards due to system-software interactions. The tools perform and assist analysts in the following tasks: 1) extract model parts from text for architecture and safety/hazard models; 2) combine the parts with library information to develop the models for visualization and analysis; 3) perform graph analysis on the models to identify possible paths from hazard sources to vulnerable entities and functions, in nominal and anomalous system-software configurations; 4) perform discrete-time-based simulation on the models to investigate scenarios where these paths may play a role in failures and mishaps; and 5) identify resulting candidate scenarios for software integration testing. This paper describes new challenges in a NASA abort system case, and enhancements made to develop the integrated tool set.

Key-Node-Separated Graph Clustering and Layouts for Human Relationship Graph Visualization.

PubMed

Itoh, Takayuki; Klein, Karsten

2015-01-01

Many graph-drawing methods apply node-clustering techniques based on the density of edges to find tightly connected subgraphs and then hierarchically visualize the clustered graphs. However, users may want to focus on important nodes and their connections to groups of other nodes for some applications. For this purpose, it is effective to separately visualize the key nodes detected based on adjacency and attributes of the nodes. This article presents a graph visualization technique for attribute-embedded graphs that applies a graph-clustering algorithm that accounts for the combination of connections and attributes. The graph clustering step divides the nodes according to the commonality of connected nodes and similarity of feature value vectors. It then calculates the distances between arbitrary pairs of clusters according to the number of connecting edges and the similarity of feature value vectors and finally places the clusters based on the distances. Consequently, the technique separates important nodes that have connections to multiple large clusters and improves the visibility of such nodes' connections. To test this technique, this article presents examples with human relationship graph datasets, including a coauthorship and Twitter communication network dataset.

Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity

PubMed Central

Bassett, Danielle S.; Khambhati, Ankit N.; Grafton, Scott T.

2018-01-01

Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain–machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer’s tool kit. PMID:28375650

Sparse cliques trump scale-free networks in coordination and competition

PubMed Central

Gianetto, David A.; Heydari, Babak

2016-01-01

Cooperative behavior, a natural, pervasive and yet puzzling phenomenon, can be significantly enhanced by networks. Many studies have shown how global network characteristics affect cooperation; however, it is difficult to understand how this occurs based on global factors alone, low-level network building blocks, or motifs are necessary. In this work, we systematically alter the structure of scale-free and clique networks and show, through a stochastic evolutionary game theory model, that cooperation on cliques increases linearly with community motif count. We further show that, for reactive stochastic strategies, network modularity improves cooperation in the anti-coordination Snowdrift game and the Prisoner’s Dilemma game but not in the Stag Hunt coordination game. We also confirm the negative effect of the scale-free graph on cooperation when effective payoffs are used. On the flip side, clique graphs are highly cooperative across social environments. Adding cycles to the acyclic scale-free graph increases cooperation when multiple games are considered; however, cycles have the opposite effect on how forgiving agents are when playing the Prisoner’s Dilemma game. PMID:26899456

Sparse cliques trump scale-free networks in coordination and competition

NASA Astrophysics Data System (ADS)

Gianetto, David A.; Heydari, Babak

2016-02-01

Cooperative behavior, a natural, pervasive and yet puzzling phenomenon, can be significantly enhanced by networks. Many studies have shown how global network characteristics affect cooperation; however, it is difficult to understand how this occurs based on global factors alone, low-level network building blocks, or motifs are necessary. In this work, we systematically alter the structure of scale-free and clique networks and show, through a stochastic evolutionary game theory model, that cooperation on cliques increases linearly with community motif count. We further show that, for reactive stochastic strategies, network modularity improves cooperation in the anti-coordination Snowdrift game and the Prisoner’s Dilemma game but not in the Stag Hunt coordination game. We also confirm the negative effect of the scale-free graph on cooperation when effective payoffs are used. On the flip side, clique graphs are highly cooperative across social environments. Adding cycles to the acyclic scale-free graph increases cooperation when multiple games are considered; however, cycles have the opposite effect on how forgiving agents are when playing the Prisoner’s Dilemma game.

Graph Coarsening for Path Finding in Cybersecurity Graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hogan, Emilie A.; Johnson, John R.; Halappanavar, Mahantesh

2013-01-01

n the pass-the-hash attack, hackers repeatedly steal password hashes and move through a computer network with the goal of reaching a computer with high level administrative privileges. In this paper we apply graph coarsening in network graphs for the purpose of detecting hackers using this attack or assessing the risk level of the network's current state. We repeatedly take graph minors, which preserve the existence of paths in the graph, and take powers of the adjacency matrix to count the paths. This allows us to detect the existence of paths as well as find paths that have high risk ofmore » being used by adversaries.« less

Brain Network Analysis from High-Resolution EEG Signals

NASA Astrophysics Data System (ADS)

de Vico Fallani, Fabrizio; Babiloni, Fabio

Over the last decade, there has been a growing interest in the detection of the functional connectivity in the brain from different neuroelectromagnetic and hemodynamic signals recorded by several neuro-imaging devices such as the functional Magnetic Resonance Imaging (fMRI) scanner, electroencephalography (EEG) and magnetoencephalography (MEG) apparatus. Many methods have been proposed and discussed in the literature with the aim of estimating the functional relationships among different cerebral structures. However, the necessity of an objective comprehension of the network composed by the functional links of different brain regions is assuming an essential role in the Neuroscience. Consequently, there is a wide interest in the development and validation of mathematical tools that are appropriate to spot significant features that could describe concisely the structure of the estimated cerebral networks. The extraction of salient characteristics from brain connectivity patterns is an open challenging topic, since often the estimated cerebral networks have a relative large size and complex structure. Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach seems relevant and useful as firstly demonstrated on a set of anatomical brain networks. In those studies, the authors have employed two characteristic measures, the average shortest path L and the clustering index C, to extract respectively the global and local properties of the network structure. They have found that anatomical brain networks exhibit many local connections (i.e. a high C) and few random long distance connections (i.e. a low L). These values identify a particular model that interpolate between a regular lattice and a random structure. Such a model has been designated as "small-world" network in analogy with the concept of the small-world phenomenon observed more than 30 years ago in social systems. In a similar way, many types of functional brain networks have been analyzed according to this mathematical approach. In particular, several studies based on different imaging techniques (fMRI, MEG and EEG) have found that the estimated functional networks showed small-world characteristics. In the functional brain connectivity context, these properties have been demonstrated to reflect an optimal architecture for the information processing and propagation among the involved cerebral structures. However, the performance of cognitive and motor tasks as well as the presence of neural diseases has been demonstrated to affect such a small-world topology, as revealed by the significant changes of L and C. Moreover, some functional brain networks have been mostly found to be very unlike the random graphs in their degree-distribution, which gives information about the allocation of the functional links within the connectivity pattern. It was demonstrated that the degree distributions of these networks follow a power-law trend. For this reason those networks are called "scale-free". They still exhibit the small-world phenomenon but tend to contain few nodes that act as highly connected "hubs". Scale-free networks are known to show resistance to failure, facility of synchronization and fast signal processing. Hence, it would be important to see whether the scaling properties of the functional brain networks are altered under various pathologies or experimental tasks. The present Chapter proposes a theoretical graph approach in order to evaluate the functional connectivity patterns obtained from high-resolution EEG signals. In this way, the "Brain Network Analysis" (in analogy with the Social Network Analysis that has emerged as a key technique in modern sociology) represents an effective methodology improving the comprehension of the complex interactions in the brain.

PuLP/XtraPuLP : Partitioning Tools for Extreme-Scale Graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Slota, George M; Rajamanickam, Sivasankaran; Madduri, Kamesh

2017-09-21

PuLP/XtraPulp is software for partitioning graphs from several real-world problems. Graphs occur in several places in real world from road networks, social networks and scientific simulations. For efficient parallel processing these graphs have to be partitioned (split) with respect to metrics such as computation and communication costs. Our software allows such partitioning for massive graphs.

Topological Analysis of Wireless Networks (TAWN)

DTIC Science & Technology

2016-05-31

transmissions from any other node. Definition 1. A wireless network vulnerability is its susceptibility to becoming disconnected when a single source of...19b. TELEPHONE NUMBER (Include area code) 31-05-2016 FINAL REPORT 12-02-2015 -- 31-05-2016 Topological Analysis of Wireless Networks (TAWN) Robinson...Release, Distribution Unlimited) N/A The goal of this project was to develop topological methods to detect and localize vulnerabilities of wireless

Finding Statistically Significant Communities in Networks

PubMed Central

Lancichinetti, Andrea; Radicchi, Filippo; Ramasco, José J.; Fortunato, Santo

2011-01-01

Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks. PMID:21559480

Vulnerability Analysis and Evaluation of Urban Road System in Tianjin

NASA Astrophysics Data System (ADS)

Liu, Y. Q.; Wu, X.

In recent years, with the development of economy, the road construction of our country has entered into a period of rapid growth. The road transportation network has been expanding and the risk of disasters is increasing. In this paper we study the vulnerability of urban road system in Tianjin. After analyzed many risk factors of the urban road system security, including road construction, road traffic and the natural environment, we proposed an evaluation index of vulnerability of urban road system and established the corresponding evaluation index system. Based on the results of analysis and comprehensive evaluation, appropriate improvement measures and suggestions which may reduce the vulnerability of the road system and improve the safety and reliability of the road system are proposed.

Robust Structural Analysis and Design of Distributed Control Systems to Prevent Zero Dynamics Attacks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weerakkody, Sean; Liu, Xiaofei; Sinopoli, Bruno

We consider the design and analysis of robust distributed control systems (DCSs) to ensure the detection of integrity attacks. DCSs are often managed by independent agents and are implemented using a diverse set of sensors and controllers. However, the heterogeneous nature of DCSs along with their scale leave such systems vulnerable to adversarial behavior. To mitigate this reality, we provide tools that allow operators to prevent zero dynamics attacks when as many as p agents and sensors are corrupted. Such a design ensures attack detectability in deterministic systems while removing the threat of a class of stealthy attacks in stochasticmore » systems. To achieve this goal, we use graph theory to obtain necessary and sufficient conditions for the presence of zero dynamics attacks in terms of the structural interactions between agents and sensors. We then formulate and solve optimization problems which minimize communication networks while also ensuring a resource limited adversary cannot perform a zero dynamics attacks. Polynomial time algorithms for design and analysis are provided.« less

A new network representation of the metabolism to detect chemical transformation modules.

PubMed

Sorokina, Maria; Medigue, Claudine; Vallenet, David

2015-11-14

Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS). RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation. This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.

Biographer: web-based editing and rendering of SBGN compliant biochemical networks

PubMed Central

Krause, Falko; Schulz, Marvin; Ripkens, Ben; Flöttmann, Max; Krantz, Marcus; Klipp, Edda; Handorf, Thomas

2013-01-01

Motivation: The rapid accumulation of knowledge in the field of Systems Biology during the past years requires advanced, but simple-to-use, methods for the visualization of information in a structured and easily comprehensible manner. Results: We have developed biographer, a web-based renderer and editor for reaction networks, which can be integrated as a library into tools dealing with network-related information. Our software enables visualizations based on the emerging standard Systems Biology Graphical Notation. It is able to import networks encoded in various formats such as SBML, SBGN-ML and jSBGN, a custom lightweight exchange format. The core package is implemented in HTML5, CSS and JavaScript and can be used within any kind of web-based project. It features interactive graph-editing tools and automatic graph layout algorithms. In addition, we provide a standalone graph editor and a web server, which contains enhanced features like web services for the import and export of models and visualizations in different formats. Availability: The biographer tool can be used at and downloaded from the web page http://biographer.biologie.hu-berlin.de/. The different software packages, including a server-indepenent version as well as a web server for Windows and Linux based systems, are available at http://code.google.com/p/biographer/ under the open-source license LGPL. Contact: edda.klipp@biologie.hu-berlin.de or handorf@physik.hu-berlin.de PMID:23574737

Network science and the human brain: Using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease.

PubMed

Mears, David; Pollard, Harvey B

2016-06-01

Over the past 15 years, the emerging field of network science has revealed the key features of brain networks, which include small-world topology, the presence of highly connected hubs, and hierarchical modularity. The value of network studies of the brain is underscored by the range of network alterations that have been identified in neurological and psychiatric disorders, including epilepsy, depression, Alzheimer's disease, schizophrenia, and many others. Here we briefly summarize the concepts of graph theory that are used to quantify network properties and describe common experimental approaches for analysis of brain networks of structural and functional connectivity. These range from tract tracing to functional magnetic resonance imaging, diffusion tensor imaging, electroencephalography, and magnetoencephalography. We then summarize the major findings from the application of graph theory to nervous systems ranging from Caenorhabditis elegans to more complex primate brains, including man. Focusing, then, on studies involving the amygdala, a brain region that has attracted intense interest as a center for emotional processing, fear, and motivation, we discuss the features of the amygdala in brain networks for fear conditioning and emotional perception. Finally, to highlight the utility of graph theory for studying dysfunction of the amygdala in mental illness, we review data with regard to changes in the hub properties of the amygdala in brain networks of patients with depression. We suggest that network studies of the human brain may serve to focus attention on regions and connections that act as principal drivers and controllers of brain function in health and disease. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children.

PubMed

Wen, Hongwei; Liu, Yue; Rekik, Islem; Wang, Shengpei; Zhang, Jishui; Zhang, Yue; Peng, Yun; He, Huiguang

2017-08-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. Although previous TS studies revealed structural abnormalities in distinct corticobasal ganglia circuits, the topological alterations of the whole-brain white matter (WM) structural networks remain poorly understood. Here, we used diffusion MRI probabilistic tractography and graph theoretical analysis to investigate the topological organization of WM networks in 44 drug-naive TS children and 41 age- and gender-matched healthy children. The WM networks were constructed by estimating inter-regional connectivity probability and the topological properties were characterized using graph theory. We found that both TS and control groups showed an efficient small-world organization in WM networks. However, compared to controls, TS children exhibited decreased global and local efficiency, increased shortest path length and small worldness, indicating a disrupted balance between local specialization and global integration in structural networks. Although both TS and control groups showed highly similar hub distributions, TS children exhibited significant decreased nodal efficiency, mainly distributed in the default mode, language, visual, and sensorimotor systems. Furthermore, two separate networks showing significantly decreased connectivity in TS group were identified using network-based statistical (NBS) analysis, primarily composed of the parieto-occipital cortex, precuneus, and paracentral lobule. Importantly, we combined support vector machine and multiple kernel learning frameworks to fuse multiple levels of network topological features for classification of individuals, achieving high accuracy of 86.47%. Together, our study revealed the disrupted topological organization of structural networks related to pathophysiology of TS, and the discriminative topological features for classification are potential quantitative neuroimaging biomarkers for clinical TS diagnosis. Hum Brain Mapp 38:3988-4008, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Incremental k-core decomposition: Algorithms and evaluation

DOE PAGES

Sariyuce, Ahmet Erdem; Gedik, Bugra; Jacques-SIlva, Gabriela; ...

2016-02-01

A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-hard problems on real networks efficiently, like maximal clique finding. In many real-world applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for dynamic graph data. In this paper, we propose a suite of incremental k-core decomposition algorithms for dynamic graph data. These algorithms locate a small subgraph that ismore » guaranteed to contain the list of vertices whose maximum k-core values have changed and efficiently process this subgraph to update the k-core decomposition. We present incremental algorithms for both insertion and deletion operations, and propose auxiliary vertex state maintenance techniques that can further accelerate these operations. Our results show a significant reduction in runtime compared to non-incremental alternatives. We illustrate the efficiency of our algorithms on different types of real and synthetic graphs, at varying scales. Furthermore, for a graph of 16 million vertices, we observe relative throughputs reaching a million times, relative to the non-incremental algorithms.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bradonjic, Milan; Hagberg, Aric; Hengartner, Nick

We analyze component evolution in general random intersection graphs (RIGs) and give conditions on existence and uniqueness of the giant component. Our techniques generalize the existing methods for analysis on component evolution in RIGs. That is, we analyze survival and extinction properties of a dependent, inhomogeneous Galton-Watson branching process on general RIGs. Our analysis relies on bounding the branching processes and inherits the fundamental concepts from the study on component evolution in Erdos-Renyi graphs. The main challenge becomes from the underlying structure of RIGs, when the number of offsprings follows a binomial distribution with a different number of nodes andmore » different rate at each step during the evolution. RIGs can be interpreted as a model for large randomly formed non-metric data sets. Besides the mathematical analysis on component evolution, which we provide in this work, we perceive RIGs as an important random structure which has already found applications in social networks, epidemic networks, blog readership, or wireless sensor networks.« less

Reticulate classification of mosaic microbial genomes using NeAT website.

PubMed

Lima-Mendez, Gipsi

2012-01-01

The tree of life is the classical representation of the evolutionary relationships between existent species. A tree is appropriate to display the divergence of species through mutation, i.e., by vertical descent. However, lateral gene transfer (LGT) is excluded from such representations. When LGT contribution to genome evolution cannot be neglected (e.g., for prokaryotes and mobile genetic elements), the tree becomes misleading. Networks appear as an intuitive way to represent both vertical and horizontal relationships, while overlapping groups within such graphs are more suitable for their classification. Here, we describe a method to represent both vertical and horizontal relationships. We start with a set of genomes whose coded proteins have been grouped into families based on sequence similarity. Next, all pairs of genomes are compared, counting the number of proteins classified into the same family. From this comparison, we derive a weighted graph where genomes with a significant number of similar proteins are linked. Finally, we apply a two-step clustering of this graph to produce a classification where nodes can be assigned to multiple clusters. The procedure can be performed using the Network Analysis Tools (NeAT) website.

Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations.

PubMed

Zhao, Jian; Glueck, Michael; Breslav, Simon; Chevalier, Fanny; Khan, Azam

2017-01-01

User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate insights between analysts. We present annotation graphs, a dynamic graph visualization that enables meta-analysis of data based on user-authored annotations. The annotation graph topology encodes annotation semantics, which describe the content of and relations between data selections, comments, and tags. We present a mixed-initiative approach to graph layout that integrates an analyst's manual manipulations with an automatic method based on similarity inferred from the annotation semantics. Various visual graph layout styles reveal different perspectives on the annotation semantics. Annotation graphs are implemented within C8, a system that supports authoring annotations during exploratory analysis of a dataset. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas.

Parallel Algorithms for Switching Edges in Heterogeneous Graphs☆

PubMed Central

Khan, Maleq; Chen, Jiangzhuo; Marathe, Madhav

2017-01-01

An edge switch is an operation on a graph (or network) where two edges are selected randomly and one of their end vertices are swapped with each other. Edge switch operations have important applications in graph theory and network analysis, such as in generating random networks with a given degree sequence, modeling and analyzing dynamic networks, and in studying various dynamic phenomena over a network. The recent growth of real-world networks motivates the need for efficient parallel algorithms. The dependencies among successive edge switch operations and the requirement to keep the graph simple (i.e., no self-loops or parallel edges) as the edges are switched lead to significant challenges in designing a parallel algorithm. Addressing these challenges requires complex synchronization and communication among the processors leading to difficulties in achieving a good speedup by parallelization. In this paper, we present distributed memory parallel algorithms for switching edges in massive networks. These algorithms provide good speedup and scale well to a large number of processors. A harmonic mean speedup of 73.25 is achieved on eight different networks with 1024 processors. One of the steps in our edge switch algorithms requires the computation of multinomial random variables in parallel. This paper presents the first non-trivial parallel algorithm for the problem, achieving a speedup of 925 using 1024 processors. PMID:28757680

U.S. Geological Survey DLG-3 and Bureau of the Census TIGER data. Development and GIS applications

USGS Publications Warehouse

Batten, Lawrence G.

1990-01-01

The U.S. Geological Survey has been actively developing digital cartographic and geographic data and standards since the early 1970's. One product is Digital Line Graph data, which offer a consistently accurate source of base category geographic information. The Bureau of the Census has combined their Dual Independent Map Encoding data with the Geological Survey's 1:100,000-scale Digital Line Graph data to prepare for the 1990 decennial census. The resulting Topologically Integrated Geographic Encoding and Referencing data offer a wealth of information. A major area of research using these data is in transportation analysis. The attributes associated with Digital Line Graphs can be used to determine the average travel times along each segment. Geographic information system functions can then be used to optimize routes through the network and to generate street name lists. Additional aspects of the subject are discussed.

Resting State Network Topology of the Ferret Brain

PubMed Central

Zhou, Zhe Charles; Salzwedel, Andrew P.; Radtke-Schuller, Susanne; Li, Yuhui; Sellers, Kristin K.; Gilmore, John H.; Shih, Yen-Yu Ian; Fröhlich, Flavio; Gao, Wei

2016-01-01

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4 tesla MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function. PMID:27596024

Structural network efficiency is associated with cognitive impairment in small-vessel disease.

PubMed

Lawrence, Andrew J; Chung, Ai Wern; Morris, Robin G; Markus, Hugh S; Barrick, Thomas R

2014-07-22

To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies. © 2014 American Academy of Neurology.

Structural network efficiency is associated with cognitive impairment in small-vessel disease

PubMed Central

Chung, Ai Wern; Morris, Robin G.; Markus, Hugh S.; Barrick, Thomas R.

2014-01-01

Objective: To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. Methods: A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. Results: Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. Conclusions: Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies. PMID:24951477

Topological Vulnerability Evaluation Model Based on Fractal Dimension of Complex Networks.

PubMed

Gou, Li; Wei, Bo; Sadiq, Rehan; Sadiq, Yong; Deng, Yong

2016-01-01

With an increasing emphasis on network security, much more attentions have been attracted to the vulnerability of complex networks. In this paper, the fractal dimension, which can reflect space-filling capacity of networks, is redefined as the origin moment of the edge betweenness to obtain a more reasonable evaluation of vulnerability. The proposed model combining multiple evaluation indexes not only overcomes the shortage of average edge betweenness's failing to evaluate vulnerability of some special networks, but also characterizes the topological structure and highlights the space-filling capacity of networks. The applications to six US airline networks illustrate the practicality and effectiveness of our proposed method, and the comparisons with three other commonly used methods further validate the superiority of our proposed method.

Functional network organization of the human brain

PubMed Central

Power, Jonathan D; Cohen, Alexander L; Nelson, Steven M; Wig, Gagan S; Barnes, Kelly Anne; Church, Jessica A; Vogel, Alecia C; Laumann, Timothy O; Miezin, Fran M; Schlaggar, Bradley L; Petersen, Steven E

2011-01-01

Summary Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a “processing” system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex. PMID:22099467

A Machine Learning Concept for DTN Routing

NASA Technical Reports Server (NTRS)

Dudukovich, Rachel; Hylton, Alan; Papachristou, Christos

2017-01-01

This paper discusses the concept and architecture of a machine learning based router for delay tolerant space networks. The techniques of reinforcement learning and Bayesian learning are used to supplement the routing decisions of the popular Contact Graph Routing algorithm. An introduction to the concepts of Contact Graph Routing, Q-routing and Naive Bayes classification are given. The development of an architecture for a cross-layer feedback framework for DTN (Delay-Tolerant Networking) protocols is discussed. Finally, initial simulation setup and results are given.

Influence of the time scale on the construction of financial networks.

PubMed

Emmert-Streib, Frank; Dehmer, Matthias

2010-09-30

In this paper we investigate the definition and formation of financial networks. Specifically, we study the influence of the time scale on their construction. For our analysis we use correlation-based networks obtained from the daily closing prices of stock market data. More precisely, we use the stocks that currently comprise the Dow Jones Industrial Average (DJIA) and estimate financial networks where nodes correspond to stocks and edges correspond to none vanishing correlation coefficients. That means only if a correlation coefficient is statistically significant different from zero, we include an edge in the network. This construction procedure results in unweighted, undirected networks. By separating the time series of stock prices in non-overlapping intervals, we obtain one network per interval. The length of these intervals corresponds to the time scale of the data, whose influence on the construction of the networks will be studied in this paper. Numerical analysis of four different measures in dependence on the time scale for the construction of networks allows us to gain insights about the intrinsic time scale of the stock market with respect to a meaningful graph-theoretical analysis.

HBNG: Graph theory based visualization of hydrogen bond networks in protein structures.

PubMed

Tiwari, Abhishek; Tiwari, Vivek

2007-07-09

HBNG is a graph theory based tool for visualization of hydrogen bond network in 2D. Digraphs generated by HBNG facilitate visualization of cooperativity and anticooperativity chains and rings in protein structures. HBNG takes hydrogen bonds list files (output from HBAT, HBEXPLORE, HBPLUS and STRIDE) as input and generates a DOT language script and constructs digraphs using freeware AT and T Graphviz tool. HBNG is useful in the enumeration of favorable topologies of hydrogen bond networks in protein structures and determining the effect of cooperativity and anticooperativity on protein stability and folding. HBNG can be applied to protein structure comparison and in the identification of secondary structural regions in protein structures. Program is available from the authors for non-commercial purposes.

ESB-based Sensor Web integration for the prediction of electric power supply system vulnerability.

PubMed

Stoimenov, Leonid; Bogdanovic, Milos; Bogdanovic-Dinic, Sanja

2013-08-15

Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB)-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application.