A model for evolution of overlapping community networks

NASA Astrophysics Data System (ADS)

Karan, Rituraj; Biswal, Bibhu

2017-05-01

A model is proposed for the evolution of network topology in social networks with overlapping community structure. Starting from an initial community structure that is defined in terms of group affiliations, the model postulates that the subsequent growth and loss of connections is similar to the Hebbian learning and unlearning in the brain and is governed by two dominant factors: the strength and frequency of interaction between the members, and the degree of overlap between different communities. The temporal evolution from an initial community structure to the current network topology can be described based on these two parameters. It is possible to quantify the growth occurred so far and predict the final stationary state to which the network is likely to evolve. Applications in epidemiology or the spread of email virus in a computer network as well as finding specific target nodes to control it are envisaged. While facing the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities one faces the most basic questions: how do communities evolve in time? This work aims to address this issue by developing a mathematical model for the evolution of community networks and studying it through computer simulation.

Spectrum-Based and Collaborative Network Topology Analysis and Visualization

ERIC Educational Resources Information Center

Hu, Xianlin

2013-01-01

Networks are of significant importance in many application domains, such as World Wide Web and social networks, which often embed rich topological information. Since network topology captures the organization of network nodes and links, studying network topology is very important to network analysis. In this dissertation, we study networks by…

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

PubMed

Mocanu, Decebal Constantin; Mocanu, Elena; Stone, Peter; Nguyen, Phuong H; Gibescu, Madeleine; Liotta, Antonio

2018-06-19

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős-Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible.

Assortativity and leadership emerge from anti-preferential attachment in heterogeneous networks.

PubMed

Sendiña-Nadal, I; Danziger, M M; Wang, Z; Havlin, S; Boccaletti, S

2016-02-18

Real-world networks have distinct topologies, with marked deviations from purely random networks. Many of them exhibit degree-assortativity, with nodes of similar degree more likely to link to one another. Though microscopic mechanisms have been suggested for the emergence of other topological features, assortativity has proven elusive. Assortativity can be artificially implanted in a network via degree-preserving link permutations, however this destroys the graph's hierarchical clustering and does not correspond to any microscopic mechanism. Here, we propose the first generative model which creates heterogeneous networks with scale-free-like properties in degree and clustering distributions and tunable realistic assortativity. Two distinct populations of nodes are incrementally added to an initial network by selecting a subgraph to connect to at random. One population (the followers) follows preferential attachment, while the other population (the potential leaders) connects via anti-preferential attachment: they link to lower degree nodes when added to the network. By selecting the lower degree nodes, the potential leader nodes maintain high visibility during the growth process, eventually growing into hubs. The evolution of links in Facebook empirically validates the connection between the initial anti-preferential attachment and long term high degree. In this way, our work sheds new light on the structure and evolution of social networks.

Assortativity and leadership emerge from anti-preferential attachment in heterogeneous networks

NASA Astrophysics Data System (ADS)

Sendiña-Nadal, I.; Danziger, M. M.; Wang, Z.; Havlin, S.; Boccaletti, S.

2016-02-01

Real-world networks have distinct topologies, with marked deviations from purely random networks. Many of them exhibit degree-assortativity, with nodes of similar degree more likely to link to one another. Though microscopic mechanisms have been suggested for the emergence of other topological features, assortativity has proven elusive. Assortativity can be artificially implanted in a network via degree-preserving link permutations, however this destroys the graph’s hierarchical clustering and does not correspond to any microscopic mechanism. Here, we propose the first generative model which creates heterogeneous networks with scale-free-like properties in degree and clustering distributions and tunable realistic assortativity. Two distinct populations of nodes are incrementally added to an initial network by selecting a subgraph to connect to at random. One population (the followers) follows preferential attachment, while the other population (the potential leaders) connects via anti-preferential attachment: they link to lower degree nodes when added to the network. By selecting the lower degree nodes, the potential leader nodes maintain high visibility during the growth process, eventually growing into hubs. The evolution of links in Facebook empirically validates the connection between the initial anti-preferential attachment and long term high degree. In this way, our work sheds new light on the structure and evolution of social networks.

Exploring the spiral of silence in adjustable social networks

NASA Astrophysics Data System (ADS)

Wu, Yue; Du, Ya-Jun; Li, Xian-Yong; Chen, Xiao-Liang

2015-03-01

This study extends the understanding of the spiral of silence theory by taking into account four factors, including the topology of networks, the time factor of information transmission, the node degree of individuals and the freedom of expression. Simulation experiments analyze the silencers, public opinion in steady state and relaxation time in small-world networks, scale-free networks and community-structured networks by adjusting the initial conditions. Results highlight that individuals are easier to keep silent in scale-free network, especially when the individual with big degree and minority opinion starts the discussion. Conversely, there are only a few individuals keep silent in the community-structured network when the two communities hold opposite opinions. Moreover, the number of silencers grows as the degree of coupling increases, and it decreases as the freedom of expression goes up. By analyzing the public opinion evolution, we also find some important conditions, such as the network topology, the potential public opinion distribution, and the status and sides of the first speaker, can drive the minority reversal.

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.

Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach

PubMed Central

2014-01-01

Background Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. Results We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation. Conclusions A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission. PMID:24507381

Evolutionary dynamics on networks of selectively neutral genotypes: effects of topology and sequence stability.

PubMed

Aguirre, Jacobo; Buldú, Javier M; Manrubia, Susanna C

2009-12-01

Networks of selectively neutral genotypes underlie the evolution of populations of replicators in constant environments. Previous theoretical analysis predicted that such populations will evolve toward highly connected regions of the genome space. We first study the evolution of populations of replicators on simple networks and quantify how the transient time to equilibrium depends on the initial distribution of sequences on the neutral network, on the topological properties of the latter, and on the mutation rate. Second, network neutrality is broken through the introduction of an energy for each sequence. This allows to study the competition between two features (neutrality and energetic stability) relevant for survival and subjected to different selective pressures. In cases where the two features are negatively correlated, the population experiences sudden migrations in the genome space for values of the relevant parameters that we calculate. The numerical study of larger networks indicates that the qualitative behavior to be expected in more realistic cases is already seen in representative examples of small networks.

Evolutionary dynamics on networks of selectively neutral genotypes: Effects of topology and sequence stability

NASA Astrophysics Data System (ADS)

Aguirre, Jacobo; Buldú, Javier M.; Manrubia, Susanna C.

2009-12-01

Networks of selectively neutral genotypes underlie the evolution of populations of replicators in constant environments. Previous theoretical analysis predicted that such populations will evolve toward highly connected regions of the genome space. We first study the evolution of populations of replicators on simple networks and quantify how the transient time to equilibrium depends on the initial distribution of sequences on the neutral network, on the topological properties of the latter, and on the mutation rate. Second, network neutrality is broken through the introduction of an energy for each sequence. This allows to study the competition between two features (neutrality and energetic stability) relevant for survival and subjected to different selective pressures. In cases where the two features are negatively correlated, the population experiences sudden migrations in the genome space for values of the relevant parameters that we calculate. The numerical study of larger networks indicates that the qualitative behavior to be expected in more realistic cases is already seen in representative examples of small networks.

Ring correlations in random networks.

PubMed

Sadjadi, Mahdi; Thorpe, M F

2016-12-01

We examine the correlations between rings in random network glasses in two dimensions as a function of their separation. Initially, we use the topological separation (measured by the number of intervening rings), but this leads to pseudo-long-range correlations due to a lack of topological charge neutrality in the shells surrounding a central ring. This effect is associated with the noncircular nature of the shells. It is, therefore, necessary to use the geometrical distance between ring centers. Hence we find a generalization of the Aboav-Weaire law out to larger distances, with the correlations between rings decaying away when two rings are more than about three rings apart.

Network Metamodeling: The Effect of Correlation Metric Choice on Phylogenomic and Transcriptomic Network Topology

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

Weighill, Deborah A; Jacobson, Daniel A

We explore the use of a network meta-modeling approach to compare the effects of similarity metrics used to construct biological networks on the topology of the resulting networks. This work reviews various similarity metrics for the construction of networks and various topology measures for the characterization of resulting network topology, demonstrating the use of these metrics in the construction and comparison of phylogenomic and transcriptomic networks.

The architecture of dynamic reservoir in the echo state network

NASA Astrophysics Data System (ADS)

Cui, Hongyan; Liu, Xiang; Li, Lixiang

2012-09-01

Echo state network (ESN) has recently attracted increasing interests because of its superior capability in modeling nonlinear dynamic systems. In the conventional echo state network model, its dynamic reservoir (DR) has a random and sparse topology, which is far from the real biological neural networks from both structural and functional perspectives. We hereby propose three novel types of echo state networks with new dynamic reservoir topologies based on complex network theory, i.e., with a small-world topology, a scale-free topology, and a mixture of small-world and scale-free topologies, respectively. We then analyze the relationship between the dynamic reservoir structure and its prediction capability. We utilize two commonly used time series to evaluate the prediction performance of the three proposed echo state networks and compare them to the conventional model. We also use independent and identically distributed time series to analyze the short-term memory and prediction precision of these echo state networks. Furthermore, we study the ratio of scale-free topology and the small-world topology in the mixed-topology network, and examine its influence on the performance of the echo state networks. Our simulation results show that the proposed echo state network models have better prediction capabilities, a wider spectral radius, but retain almost the same short-term memory capacity as compared to the conventional echo state network model. We also find that the smaller the ratio of the scale-free topology over the small-world topology, the better the memory capacities.

A topology visualization early warning distribution algorithm for large-scale network security incidents.

PubMed

He, Hui; Fan, Guotao; Ye, Jianwei; Zhang, Weizhe

2013-01-01

It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system's emergency response capabilities, alleviate the cyber attacks' damage, and strengthen the system's counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system's plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks' topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

Revealing the Effect of Irradiation on Cement Hydrates: Evidence of a Topological Self-Organization.

PubMed

Krishnan, N M Anoop; Wang, Bu; Sant, Gaurav; Phillips, James C; Bauchy, Mathieu

2017-09-20

Despite the crucial role of concrete in the construction of nuclear power plants, the effects of radiation exposure (i.e., in the form of neutrons) on the calcium-silicate-hydrate (C-S-H, i.e., the glue of concrete) remain largely unknown. Using molecular dynamics simulations, we systematically investigate the effects of irradiation on the structure of C-S-H across a range of compositions. Expectedly, although C-S-H is more resistant to irradiation than typical crystalline silicates, such as quartz, we observe that radiation exposure affects C-S-H's structural order, silicate mean chain length, and the amount of molecular water that is present in the atomic network. By topological analysis, we show that these "structural effects" arise from a self-organization of the atomic network of C-S-H upon irradiation. This topological self-organization is driven by the (initial) presence of atomic eigenstress in the C-S-H network and is facilitated by the presence of water in the network. Overall, we show that C-S-H exhibits an optimal resistance to radiation damage when its atomic network is isostatic (at Ca/Si = 1.5). Such an improved understanding of the response of C-S-H to irradiation can pave the way to the design of durable concrete for radiation applications.

Topology Control in Aerial Multi-Beam Directional Networks

DTIC Science & Technology

2017-04-24

underlying challenges to topology control in multi -beam direction networks. Two topology control algorithms are developed: a centralized algorithm...main beam, the gain is negligible. Thus, for topology control in a multi -beam system, two nodes that are being simultaneously transmitted to or...the network. As the network size is larger than the communication range, even the original network will require some multi -hop traffic. The second two

Inferring topologies via driving-based generalized synchronization of two-layer networks

NASA Astrophysics Data System (ADS)

Wang, Yingfei; Wu, Xiaoqun; Feng, Hui; Lu, Jun-an; Xu, Yuhua

2016-05-01

The interaction topology among the constituents of a complex network plays a crucial role in the network’s evolutionary mechanisms and functional behaviors. However, some network topologies are usually unknown or uncertain. Meanwhile, coupling delays are ubiquitous in various man-made and natural networks. Hence, it is necessary to gain knowledge of the whole or partial topology of a complex dynamical network by taking into consideration communication delay. In this paper, topology identification of complex dynamical networks is investigated via generalized synchronization of a two-layer network. Particularly, based on the LaSalle-type invariance principle of stochastic differential delay equations, an adaptive control technique is proposed by constructing an auxiliary layer and designing proper control input and updating laws so that the unknown topology can be recovered upon successful generalized synchronization. Numerical simulations are provided to illustrate the effectiveness of the proposed method. The technique provides a certain theoretical basis for topology inference of complex networks. In particular, when the considered network is composed of systems with high-dimension or complicated dynamics, a simpler response layer can be constructed, which is conducive to circuit design. Moreover, it is practical to take into consideration perturbations caused by control input. Finally, the method is applicable to infer topology of a subnetwork embedded within a complex system and locate hidden sources. We hope the results can provide basic insight into further research endeavors on understanding practical and economical topology inference of networks.

Analysis of topological relationships and network properties in the interactions of human beings

PubMed Central

Chen, Xuebo; Sun, Qiubai; Huang, Tianyun

2017-01-01

In the animal world, various kinds of collective motions have been found and proven to be efficient ways of carrying out some activities such as searching for food and avoiding predators. Many scholars research the interactions of collective behaviors of human beings according to the rules of collective behaviors of animals. Based on the Lennard-Jones potential function and a self-organization process, our paper proposes a topological communication model to simulate the collective behaviors of human beings. In the results of simulations, we find various types of collective behavior and fission behavior and discover the threshold for the emergence of collective behavior, which is the range five to seven for the number of topology K. According to the analysis of network properties of the model, the in-degree of individuals is always equal to the number of topology. In the stable state, the out-degrees of individuals distribute around the value of the number of topology K, except that the out-degree of a single individual is approximately double the out-degrees of the other individuals. In addition, under different initial conditions, some features of different kinds of networks emerge from the model. We also find the leader and herd mentality effects in the characteristics of the behaviors of human beings in our model. Thus, this work could be used to discover how to promote the emergence of beneficial group behaviors and prevent the emergence of harmful behaviors. PMID:28832629

Optimal network alignment with graphlet degree vectors.

PubMed

Milenković, Tijana; Ng, Weng Leong; Hayes, Wayne; Przulj, Natasa

2010-06-30

Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology.

Research on social communication network evolution based on topology potential distribution

NASA Astrophysics Data System (ADS)

Zhao, Dongjie; Jiang, Jian; Li, Deyi; Zhang, Haisu; Chen, Guisheng

2011-12-01

Aiming at the problem of social communication network evolution, first, topology potential is introduced to measure the local influence among nodes in networks. Second, from the perspective of topology potential distribution the method of network evolution description based on topology potential distribution is presented, which takes the artificial intelligence with uncertainty as basic theory and local influence among nodes as essentiality. Then, a social communication network is constructed by enron email dataset, the method presented is used to analyze the characteristic of the social communication network evolution and some useful conclusions are got, implying that the method is effective, which shows that topology potential distribution can effectively describe the characteristic of sociology and detect the local changes in social communication network.

Machine learning topological states

NASA Astrophysics Data System (ADS)

Deng, Dong-Ling; Li, Xiaopeng; Das Sarma, S.

2017-11-01

Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology. Here, we use artificial neural networks to study an intriguing phenomenon in quantum physics—the topological phases of matter. We find that certain topological states, either symmetry-protected or with intrinsic topological order, can be represented with classical artificial neural networks. This is demonstrated by using three concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D and 3D toric code states with intrinsic topological orders. For all three cases, we show rigorously that the topological ground states can be represented by short-range neural networks in an exact and efficient fashion—the required number of hidden neurons is as small as the number of physical spins and the number of parameters scales only linearly with the system size. For the 2D toric-code model, we find that the proposed short-range neural networks can describe the excited states with Abelian anyons and their nontrivial mutual statistics as well. In addition, by using reinforcement learning we show that neural networks are capable of finding the topological ground states of nonintegrable Hamiltonians with strong interactions and studying their topological phase transitions. Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases in generic lattice models.

Energy efficiency evaluation of tree-topology 10 gigabit ethernet passive optical network and ring-topology time- and wavelength-division-multiplexed passive optical network

NASA Astrophysics Data System (ADS)

Song, Jingjing; Yang, Chuanchuan; Zhang, Qingxiang; Ma, Zhuang; Huang, Xingang; Geng, Dan; Wang, Ziyu

2015-09-01

Higher capacity and larger scales have always been the top targets for the evolution of optical access networks, driven by the ever-increasing demand from the end users. One thing that started to attract wide attention not long ago, but with at least equal importance as capacity and scale, is energy efficiency, a metric essential nowadays as human beings are confronted with severe environmental issues like global warming, air pollution, and so on. Here, different from the conventional energy consumption analysis of tree-topology networks, we propose an effective energy consumption calculation method to compare the energy efficiency of the tree-topology 10 gigabit ethernet passive optical network (10G-EPON) and ring-topology time- and wavelength-division-multiplexed passive optical network (TWDM-PON), two experimental networks deployed in China. Numerical results show that the ring-topology TWDM-PON networks with 2, 4, 8, and 16 wavelengths are more energy efficient than the tree-topology 10G-EPON, although 10G-EPON consumes less energy. Also, TWDM-PON with four wavelengths is the most energy-efficient network candidate and saves 58.7% more energy than 10G-EPON when fully loaded.

Extension algorithm for generic low-voltage networks

NASA Astrophysics Data System (ADS)

Marwitz, S.; Olk, C.

2018-02-01

Distributed energy resources (DERs) are increasingly penetrating the energy system which is driven by climate and sustainability goals. These technologies are mostly connected to low- voltage electrical networks and change the demand and supply situation in these networks. This can cause critical network states. Network topologies vary significantly and depend on several conditions including geography, historical development, network design or number of network connections. In the past, only some of these aspects were taken into account when estimating the network investment needs for Germany on the low-voltage level. Typically, fixed network topologies are examined or a Monte Carlo approach is used to quantify the investment needs at this voltage level. Recent research has revealed that DERs differ substantially between rural, suburban and urban regions. The low-voltage network topologies have different design concepts in these regions, so that different network topologies have to be considered when assessing the need for network extensions and investments due to DERs. An extension algorithm is needed to calculate network extensions and investment needs for the different typologies of generic low-voltage networks. We therefore present a new algorithm, which is capable of calculating the extension for generic low-voltage networks of any given topology based on voltage range deviations and thermal overloads. The algorithm requires information about line and cable lengths, their topology and the network state only. We test the algorithm on a radial, a loop, and a heavily meshed network. Here we show that the algorithm functions for electrical networks with these topologies. We found that the algorithm is able to extend different networks efficiently by placing cables between network nodes. The main value of the algorithm is that it does not require any information about routes for additional cables or positions for additional substations when it comes to estimating network extension needs.

Emergence of small-world structure in networks of spiking neurons through STDP plasticity.

PubMed

Basalyga, Gleb; Gleiser, Pablo M; Wennekers, Thomas

2011-01-01

In this work, we use a complex network approach to investigate how a neural network structure changes under synaptic plasticity. In particular, we consider a network of conductance-based, single-compartment integrate-and-fire excitatory and inhibitory neurons. Initially the neurons are connected randomly with uniformly distributed synaptic weights. The weights of excitatory connections can be strengthened or weakened during spiking activity by the mechanism known as spike-timing-dependent plasticity (STDP). We extract a binary directed connection matrix by thresholding the weights of the excitatory connections at every simulation step and calculate its major topological characteristics such as the network clustering coefficient, characteristic path length and small-world index. We numerically demonstrate that, under certain conditions, a nontrivial small-world structure can emerge from a random initial network subject to STDP learning.

The application of improved NeuroEvolution of Augmenting Topologies neural network in Marcellus Shale lithofacies prediction

NASA Astrophysics Data System (ADS)

Wang, Guochang; Cheng, Guojian; Carr, Timothy R.

2013-04-01

The organic-rich Marcellus Shale was deposited in a foreland basin during Middle Devonian. In terms of mineral composition and organic matter richness, we define seven mudrock lithofacies: three organic-rich lithofacies and four organic-poor lithofacies. The 3D lithofacies model is very helpful to determine geologic and engineering sweet spots, and consequently useful for designing horizontal well trajectories and stimulation strategies. The NeuroEvolution of Augmenting Topologies (NEAT) is relatively new idea in the design of neural networks, and shed light on classification (i.e., Marcellus Shale lithofacies prediction). We have successfully enhanced the capability and efficiency of NEAT in three aspects. First, we introduced two new attributes of node gene, the node location and recurrent connection (RCC), to increase the calculation efficiency. Second, we evolved the population size from an initial small value to big, instead of using the constant value, which saves time and computer memory, especially for complex learning tasks. Third, in multiclass pattern recognition problems, we combined feature selection of input variables and modular neural network to automatically select input variables and optimize network topology for each binary classifier. These improvements were tested and verified by true if an odd number of its arguments are true and false otherwise (XOR) experiments, and were powerful for classification.

Topological entropy of catalytic sets: Hypercycles revisited

NASA Astrophysics Data System (ADS)

Sardanyés, Josep; Duarte, Jorge; Januário, Cristina; Martins, Nuno

2012-02-01

The dynamics of catalytic networks have been widely studied over the last decades because of their implications in several fields like prebiotic evolution, virology, neural networks, immunology or ecology. One of the most studied mathematical bodies for catalytic networks was initially formulated in the context of prebiotic evolution, by means of the hypercycle theory. The hypercycle is a set of self-replicating species able to catalyze other replicator species within a cyclic architecture. Hypercyclic organization might arise from a quasispecies as a way to increase the informational containt surpassing the so-called error threshold. The catalytic coupling between replicators makes all the species to behave like a single and coherent evolutionary multimolecular unit. The inherent nonlinearities of catalytic interactions are responsible for the emergence of several types of dynamics, among them, chaos. In this article we begin with a brief review of the hypercycle theory focusing on its evolutionary implications as well as on different dynamics associated to different types of small catalytic networks. Then we study the properties of chaotic hypercycles with error-prone replication with symbolic dynamics theory, characterizing, by means of the theory of topological Markov chains, the topological entropy and the periods of the orbits of unimodal-like iterated maps obtained from the strange attractor. We will focus our study on some key parameters responsible for the structure of the catalytic network: mutation rates, autocatalytic and cross-catalytic interactions.

Robust quantum network architectures and topologies for entanglement distribution

NASA Astrophysics Data System (ADS)

Das, Siddhartha; Khatri, Sumeet; Dowling, Jonathan P.

2018-01-01

Entanglement distribution is a prerequisite for several important quantum information processing and computing tasks, such as quantum teleportation, quantum key distribution, and distributed quantum computing. In this work, we focus on two-dimensional quantum networks based on optical quantum technologies using dual-rail photonic qubits for the building of a fail-safe quantum internet. We lay out a quantum network architecture for entanglement distribution between distant parties using a Bravais lattice topology, with the technological constraint that quantum repeaters equipped with quantum memories are not easily accessible. We provide a robust protocol for simultaneous entanglement distribution between two distant groups of parties on this network. We also discuss a memory-based quantum network architecture that can be implemented on networks with an arbitrary topology. We examine networks with bow-tie lattice and Archimedean lattice topologies and use percolation theory to quantify the robustness of the networks. In particular, we provide figures of merit on the loss parameter of the optical medium that depend only on the topology of the network and quantify the robustness of the network against intermittent photon loss and intermittent failure of nodes. These figures of merit can be used to compare the robustness of different network topologies in order to determine the best topology in a given real-world scenario, which is critical in the realization of the quantum internet.

Entraining the topology and the dynamics of a network of phase oscillators

NASA Astrophysics Data System (ADS)

Sendiña-Nadal, I.; Leyva, I.; Buldú, J. M.; Almendral, J. A.; Boccaletti, S.

2009-04-01

We show that the topology and dynamics of a network of unsynchronized Kuramoto oscillators can be simultaneously controlled by means of a forcing mechanism which yields a phase locking of the oscillators to that of an external pacemaker in connection with the reshaping of the network’s degree distribution. The entrainment mechanism is based on the addition, at regular time intervals, of unidirectional links from oscillators that follow the dynamics of a pacemaker to oscillators in the pristine graph whose phases hold a prescribed phase relationship. Such a dynamically based rule in the attachment process leads to the emergence of a power-law shape in the final degree distribution of the graph whenever the network is entrained to the dynamics of the pacemaker. We show that the arousal of a scale-free distribution in connection with the success of the entrainment process is a robust feature, characterizing different networks’ initial configurations and parameters.

A Novel Topology Link-Controlling Approach for Active Defense of a Node in a Network.

PubMed

Li, Jun; Hu, HanPing; Ke, Qiao; Xiong, Naixue

2017-03-09

With the rapid development of virtual machine technology and cloud computing, distributed denial of service (DDoS) attacks, or some peak traffic, poses a great threat to the security of the network. In this paper, a novel topology link control technique and mitigation attacks in real-time environments is proposed. Firstly, a non-invasive method of deploying virtual sensors in the nodes is built, which uses the resource manager of each monitored node as a sensor. Secondly, a general topology-controlling approach of resisting the tolerant invasion is proposed. In the proposed approach, a prediction model is constructed by using copula functions for predicting the peak of a resource through another resource. The result of prediction determines whether or not to initiate the active defense. Finally, a minority game with incomplete strategy is employed to suppress attack flows and improve the permeability of the normal flows. The simulation results show that the proposed approach is very effective in protecting nodes.

A Novel Topology Link-Controlling Approach for Active Defense of Nodes in Networks

PubMed Central

Li, Jun; Hu, HanPing; Ke, Qiao; Xiong, Naixue

2017-01-01

With the rapid development of virtual machine technology and cloud computing, distributed denial of service (DDoS) attacks, or some peak traffic, poses a great threat to the security of the network. In this paper, a novel topology link control technique and mitigation attacks in real-time environments is proposed. Firstly, a non-invasive method of deploying virtual sensors in the nodes is built, which uses the resource manager of each monitored node as a sensor. Secondly, a general topology-controlling approach of resisting the tolerant invasion is proposed. In the proposed approach, a prediction model is constructed by using copula functions for predicting the peak of a resource through another resource. The result of prediction determines whether or not to initiate the active defense. Finally, a minority game with incomplete strategy is employed to suppress attack flows and improve the permeability of the normal flows. The simulation results show that the proposed approach is very effective in protecting nodes. PMID:28282962

Photo-responsive surface topology in chiral nematic media

NASA Astrophysics Data System (ADS)

Liu, Danqing; Bastiaansen, Cees W. M.; Toonder, Jaap. M. J.; Broer, Dirk J.

2012-03-01

We report on the design and fabrication of 'smart surfaces' that exhibit dynamic changes in their surface topology in response to exposure to light. The principle is based on anisotropic geometric changes of a liquid crystal network upon a change of the molecular order parameter. The photomechanical property of the coating is induced by incorporating an azobenzene moiety into the liquid crystal network. The responsive surface topology consists of regions with two different types of molecular order: planar chiral-nematic areas and homeotropic. Under flood exposure with 365 nm light the surfaces deform from flat to one with a surface relief. The height of the relief structures is of the order of 1 um corresponding to strain difference of around 20%. Furthermore, we demonstrate surface reliefs can form either convex or concave structures upon exposure to UV light corresponding to the decrease or increase molecular order parameter, respectively, related to the isomeric state of the azobenzene crosslinker. The reversible deformation to the initial flat state occurs rapidly after removing the light source.

Fluctuation Dynamics Analysis of gp120 Envelope Protein Reveals a Topologically Based Communication Network

PubMed Central

Shrivastava, Indira; LaLonde, Judith M.

2012-01-01

HIV infection is initiated by binding of the viral glycoprotein gp120, to the cellular receptor CD4. Upon CD4 binding, gp120 undergoes conformational change, permitting binding to the chemokine receptor. Crystal structures of gp120 ternary complex reveal the CD4 bound conformation of gp120. We report here the application of Gaussian Network Model (GNM) to the crystal structures of gp120 bound to CD4 or CD4 mimic and 17b, to study the collective motions of the gp120 core and determine the communication propensities of the residue network. The GNM fluctuation profiles identify residues in the inner domain and outer domain that may facilitate conformational change or stability, respectively. Communication propensities delineate a residue network that is topologically suited for signal propagation from the Phe43 cavity throughout the gp120 outer domain. . These results provide a new context for interpreting gp120 core envelope structure-function relationships. PMID:20718047

Recovery time after localized perturbations in complex dynamical networks

NASA Astrophysics Data System (ADS)

Mitra, Chiranjit; Kittel, Tim; Choudhary, Anshul; Kurths, Jürgen; Donner, Reik V.

2017-10-01

Maintaining the synchronous motion of dynamical systems interacting on complex networks is often critical to their functionality. However, real-world networked dynamical systems operating synchronously are prone to random perturbations driving the system to arbitrary states within the corresponding basin of attraction, thereby leading to epochs of desynchronized dynamics with a priori unknown durations. Thus, it is highly relevant to have an estimate of the duration of such transient phases before the system returns to synchrony, following a random perturbation to the dynamical state of any particular node of the network. We address this issue here by proposing the framework of single-node recovery time (SNRT) which provides an estimate of the relative time scales underlying the transient dynamics of the nodes of a network during its restoration to synchrony. We utilize this in differentiating the particularly slow nodes of the network from the relatively fast nodes, thus identifying the critical nodes which when perturbed lead to significantly enlarged recovery time of the system before resuming synchronized operation. Further, we reveal explicit relationships between the SNRT values of a network, and its global relaxation time when starting all the nodes from random initial conditions. Earlier work on relaxation time generally focused on investigating its dependence on macroscopic topological properties of the respective network. However, we employ the proposed concept for deducing microscopic relationships between topological features of nodes and their respective SNRT values. The framework of SNRT is further extended to a measure of resilience of the different nodes of a networked dynamical system. We demonstrate the potential of SNRT in networks of Rössler oscillators on paradigmatic topologies and a model of the power grid of the United Kingdom with second-order Kuramoto-type nodal dynamics illustrating the conceivable practical applicability of the proposed concept.

Decentralised fixed modes of networked MIMO systems

NASA Astrophysics Data System (ADS)

Hao, Yuqing; Duan, Zhisheng; Chen, Guanrong

2018-04-01

In this paper, decentralised fixed modes (DFMs) of a networked system are studied. The network topology is directed and weighted and the nodes are higher-dimensional linear time-invariant (LTI) dynamical systems. The effects of the network topology, the node-system dynamics, the external control inputs, and the inner interactions on the existence of DFMs for the whole networked system are investigated. A necessary and sufficient condition for networked multi-input/multi-output (MIMO) systems in a general topology to possess no DFMs is derived. For networked single-input/single-output (SISO) LTI systems in general as well as some typical topologies, some specific conditions for having no DFMs are established. It is shown that the existence of DFMs is an integrated result of the aforementioned relevant factors which cannot be decoupled into individual DFMs of the node-systems and the properties solely determined by the network topology.

Political opinion formation: Initial opinion distribution and individual heterogeneity of tolerance

NASA Astrophysics Data System (ADS)

Jin, Cheng; Li, Yifu; Jin, Xiaogang

2017-02-01

Opinion dynamics on networks have received serious attention for its profound prospects in social behaviours and self-organized systems. However, political opinion formation, as one typical and significant case, remains lacking in discussion. Previous agent-based simulations propose various models that are based on different mechanisms like the coevolution between network topology and status transition. Nonetheless, even under the same network topology and with the same simple mechanism, forming opinions can still be uncertain. In this work, we propose two features, the initial distribution of opinions and the individual heterogeneity of tolerances on opinion changing, in political opinion formation. These two features are imbedded in the network construction phase of a classical model. By comparing multi simple-party systems, along with a detailed analysis on the two-party system, we capture the critical phenomenon of fragmentation, polarization and consensus both in the persistent stable stage and in-process. We further introduce the average ratio of nearest neighbours to characterize the stage of opinion formation. The results show that the initial distribution of opinions leads to different evolution results on similar random networks. In addition, the existence of stubborn nodes plays a special role: only nodes that are extremely stubborn can cause the change of final opinion distribution while in other cases they only delay the time to reach stability. If stubborn nodes are small in number, their effects are confined within a small range. This theoretical work goes deeper on an existing model, it is an early exploration on qualitative and quantitative simulation of party competition.

Observations and analysis of self-similar branching topology in glacier networks

USGS Publications Warehouse

Bahr, D.B.; Peckham, S.D.

1996-01-01

Glaciers, like rivers, have a branching structure which can be characterized by topological trees or networks. Probability distributions of various topological quantities in the networks are shown to satisfy the criterion for self-similarity, a symmetry structure which might be used to simplify future models of glacier dynamics. Two analytical methods of describing river networks, Shreve's random topology model and deterministic self-similar trees, are applied to the six glaciers of south central Alaska studied in this analysis. Self-similar trees capture the topological behavior observed for all of the glaciers, and most of the networks are also reasonably approximated by Shreve's theory. Copyright 1996 by the American Geophysical Union.

Inferring Network Controls from Topology Using the Chomp Database

DTIC Science & Technology

2015-12-03

AFRL-AFOSR-VA-TR-2016-0033 INFERRING NETWORK CONTROLS FROM TOPOLOGY USING THE CHOMP DATABASE John Harer DUKE UNIVERSITY Final Report 12/03/2015...INFERRING NETWORK CONTROLS FROM TOPOLOGY USING THE CHOMP DATABASE 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-10-1-0436 5c. PROGRAM ELEMENT NUMBER 6...area of Topological Data Analysis (TDA) and it’s application to dynamical systems. The role of this work in the Complex Networks program is based on

Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder

PubMed Central

Chen, Jian-Huai; Yao, Zhi-Jian; Qin, Jiao-Long; Yan, Rui; Hua, Ling-Ling; Lu, Qing

2016-01-01

Background: Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods: The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results: Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions: All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network. PMID:26960371

Machine Learning Topological Invariants with Neural Networks

NASA Astrophysics Data System (ADS)

Zhang, Pengfei; Shen, Huitao; Zhai, Hui

2018-02-01

In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

A New Cross-By-Pass-Torus Architecture Based on CBP-Mesh and Torus Interconnection for On-Chip Communication.

PubMed

Gulzari, Usman Ali; Sajid, Muhammad; Anjum, Sheraz; Agha, Shahrukh; Torres, Frank Sill

2016-01-01

A Mesh topology is one of the most promising architecture due to its regular and simple structure for on-chip communication. Performance of mesh topology degraded greatly by increasing the network size due to small bisection width and large network diameter. In order to overcome this limitation, many researchers presented modified Mesh design by adding some extra links to improve its performance in terms of network latency and power consumption. The Cross-By-Pass-Mesh was presented by us as an improved version of Mesh topology by intelligent addition of extra links. This paper presents an efficient topology named Cross-By-Pass-Torus for further increase in the performance of the Cross-By-Pass-Mesh topology. The proposed design merges the best features of the Cross-By-Pass-Mesh and Torus, to reduce the network diameter, minimize the average number of hops between nodes, increase the bisection width and to enhance the overall performance of the network. In this paper, the architectural design of the topology is presented and analyzed against similar kind of 2D topologies in terms of average latency, throughput and power consumption. In order to certify the actual behavior of proposed topology, the synthetic traffic trace and five different real embedded application workloads are applied to the proposed as well as other competitor network topologies. The simulation results indicate that Cross-By-Pass-Torus is an efficient candidate among its predecessor's and competitor topologies due to its less average latency and increased throughput at a slight cost in network power and energy for on-chip communication.

Topological and Historical Considerations for Infectious Disease Transmission among Injecting Drug Users in Bushwick, Brooklyn (USA)

PubMed Central

Dombrowski, Kirk; Curtis, Richard; Friedman, Samuel; Khan, Bilal

2014-01-01

Recent interest by physicists in social networks and disease transmission factors has prompted debate over the topology of degree distributions in sexual networks. Social network researchers have been critical of “scale-free” Barabasi-Albert approaches, and largely rejected the preferential attachment, “rich-get-richer” assumptions that underlie that model. Instead, research on sexual networks has pointed to the importance of homophily and local sexual norms in dictating degree distributions, and thus disease transmission thresholds. Injecting Drug User (IDU) network topologies may differ from the emerging models of sexual networks, however. Degree distribution analysis of a Brooklyn, NY, IDU network indicates a different topology than the spanning tree configurations discussed for sexual networks, instead featuring comparatively short cycles and high concurrency. Our findings suggest that IDU networks do in some ways conform to a “scale-free” topology, and thus may represent “reservoirs” of potential infection despite seemingly low transmission thresholds. PMID:24672745

Method and system for mesh network embedded devices

NASA Technical Reports Server (NTRS)

Wang, Ray (Inventor)

2009-01-01

A method and system for managing mesh network devices. A mesh network device with integrated features creates an N-way mesh network with a full mesh network topology or a partial mesh network topology.

Combining Topological Hardware and Topological Software: Color-Code Quantum Computing with Topological Superconductor Networks

NASA Astrophysics Data System (ADS)

Litinski, Daniel; Kesselring, Markus S.; Eisert, Jens; von Oppen, Felix

2017-07-01

We present a scalable architecture for fault-tolerant topological quantum computation using networks of voltage-controlled Majorana Cooper pair boxes and topological color codes for error correction. Color codes have a set of transversal gates which coincides with the set of topologically protected gates in Majorana-based systems, namely, the Clifford gates. In this way, we establish color codes as providing a natural setting in which advantages offered by topological hardware can be combined with those arising from topological error-correcting software for full-fledged fault-tolerant quantum computing. We provide a complete description of our architecture, including the underlying physical ingredients. We start by showing that in topological superconductor networks, hexagonal cells can be employed to serve as physical qubits for universal quantum computation, and we present protocols for realizing topologically protected Clifford gates. These hexagonal-cell qubits allow for a direct implementation of open-boundary color codes with ancilla-free syndrome read-out and logical T gates via magic-state distillation. For concreteness, we describe how the necessary operations can be implemented using networks of Majorana Cooper pair boxes, and we give a feasibility estimate for error correction in this architecture. Our approach is motivated by nanowire-based networks of topological superconductors, but it could also be realized in alternative settings such as quantum-Hall-superconductor hybrids.

The Effect of Angle Restriction on the Topological Characteristics of Minicircle Networks

NASA Astrophysics Data System (ADS)

Arsuaga, J.; Diao, Y.; Hinson, K.

2012-01-01

Networks of topologically linked minicircle polymers are found in diverse natural systems and are a subject of intense research in nanotechonology. In a recent report the authors introduced a new theoretical model to study the effects of polymer density on the formation and on the topological properties of minicircle networks. Three key topological characteristics were identified in the formation and characterization of a network: the critical percolation density, the average saturation density and the mean valence of the network. In this work we report how these characteristics change when an orientation bias is imposed on the minicircles forming the network. We observe that such restrictions have significant effects on the key topological characteristics of the network. In particular while the effects of restriction of the tilting angle can be predicted we find that those of the azimuthal angle can have somewhat unexpected results.

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

Chen, Dong; Heidelberger, Philip; Sugawara, Yutaka

An apparatus and method for extending the scalability and improving the partitionability of networks that contain all-to-all links for transporting packet traffic from a source endpoint to a destination endpoint with low per-endpoint (per-server) cost and a small number of hops. An all-to-all wiring in the baseline topology is decomposed into smaller all-to-all components in which each smaller all-to-all connection is replaced with star topology by using global switches. Stacking multiple copies of the star topology baseline network creates a multi-planed switching topology for transporting packet traffic. Point-to-point unified stacking method using global switch wiring methods connects multiple planes ofmore » a baseline topology by using the global switches to create a large network size with a low number of hops, i.e., low network latency. Grouped unified stacking method increases the scalability (network size) of a stacked topology.« less

A random spatial network model based on elementary postulates

USGS Publications Warehouse

Karlinger, Michael R.; Troutman, Brent M.

1989-01-01

A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity

NASA Astrophysics Data System (ADS)

Zhang, Bijun; Vogt, Martin; Maggiora, Gerald M.; Bajorath, Jürgen

2015-07-01

Chemical space networks (CSNs) have recently been introduced as a conceptual alternative to coordinate-based representations of chemical space. CSNs were initially designed as threshold networks using the Tanimoto coefficient as a continuous similarity measure. The analysis of CSNs generated from sets of bioactive compounds revealed that many statistical properties were strongly dependent on their edge density. While it was difficult to compare CSNs at pre-defined similarity threshold values, CSNs with constant edge density were directly comparable. In the current study, alternative CSN representations were constructed by applying the matched molecular pair (MMP) formalism as a substructure-based similarity criterion. For more than 150 compound activity classes, MMP-based CSNs (MMP-CSNs) were compared to corresponding threshold CSNs (THR-CSNs) at a constant edge density by applying different parameters from network science, measures of community structure distributions, and indicators of structure-activity relationship (SAR) information content. MMP-CSNs were found to be an attractive alternative to THR-CSNs, yielding low edge densities and well-resolved topologies. MMP-CSNs and corresponding THR-CSNs often had similar topology and closely corresponding community structures, although there was only limited overlap in similarity relationships. The homophily principle from network science was shown to affect MMP-CSNs and THR-CSNs in different ways, despite the presence of conserved topological features. Moreover, activity cliff distributions in alternative CSN designs markedly differed, which has important implications for SAR analysis.

Highly designable phenotypes and mutational buffers emerge from a systematic mapping between network topology and dynamic output.

PubMed

Nochomovitz, Yigal D; Li, Hao

2006-03-14

Deciphering the design principles for regulatory networks is fundamental to an understanding of biological systems. We have explored the mapping from the space of network topologies to the space of dynamical phenotypes for small networks. Using exhaustive enumeration of a simple model of three- and four-node networks, we demonstrate that certain dynamical phenotypes can be generated by an atypically broad spectrum of network topologies. Such dynamical outputs are highly designable, much like certain protein structures can be designed by an unusually broad spectrum of sequences. The network topologies that encode a highly designable dynamical phenotype possess two classes of connections: a fully conserved core of dedicated connections that encodes the stable dynamical phenotype and a partially conserved set of variable connections that controls the transient dynamical flow. By comparing the topologies and dynamics of the three- and four-node network ensembles, we observe a large number of instances of the phenomenon of "mutational buffering," whereby addition of a fourth node suppresses phenotypic variation amongst a set of three-node networks.

The spreading of opposite opinions on online social networks with authoritative nodes

NASA Astrophysics Data System (ADS)

Yan, Shu; Tang, Shaoting; Pei, Sen; Jiang, Shijin; Zhang, Xiao; Ding, Wenrui; Zheng, Zhiming

2013-09-01

The study of opinion dynamics, such as spreading and controlling of rumors, has become an important issue on social networks. Numerous models have been devised to describe this process, including epidemic models and spin models, which mainly focus on how opinions spread and interact with each other, respectively. In this paper, we propose a model that combines the spreading stage and the interaction stage for opinions to illustrate the process of dispelling a rumor. Moreover, we set up authoritative nodes, which disseminate positive opinion to counterbalance the negative opinion prevailing on online social networking sites. With analysis of the relationship among positive opinion proportion, opinion strength and the density of authoritative nodes in networks with different topologies, we demonstrate that the positive opinion proportion grows with the density of authoritative nodes until the positive opinion prevails in the entire network. In particular, the relationship is linear in homogeneous topologies. Besides, it is also noteworthy that initial locations of the negative opinion source and authoritative nodes do not influence positive opinion proportion in homogeneous networks but have a significant impact on heterogeneous networks. The results are verified by numerical simulations and are helpful to understand the mechanism of two different opinions interacting with each other on online social networking sites.

From network structure to network reorganization: implications for adult neurogenesis

NASA Astrophysics Data System (ADS)

Schneider-Mizell, Casey M.; Parent, Jack M.; Ben-Jacob, Eshel; Zochowski, Michal R.; Sander, Leonard M.

2010-12-01

Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells.

Complexity of generic biochemical circuits: topology versus strength of interactions.

PubMed

Tikhonov, Mikhail; Bialek, William

2016-12-06

The historical focus on network topology as a determinant of biological function is still largely maintained today, illustrated by the rise of structure-only approaches to network analysis. However, biochemical circuits and genetic regulatory networks are defined both by their topology and by a multitude of continuously adjustable parameters, such as the strength of interactions between nodes, also recognized as important. Here we present a class of simple perceptron-based Boolean models within which comparing the relative importance of topology versus interaction strengths becomes a quantitatively well-posed problem. We quantify the intuition that for generic networks, optimization of interaction strengths is a crucial ingredient of achieving high complexity, defined here as the number of fixed points the network can accommodate. We propose a new methodology for characterizing the relative role of parameter optimization for topologies of a given class.

Experiments with arbitrary networks in time-multiplexed delay systems

NASA Astrophysics Data System (ADS)

Hart, Joseph D.; Schmadel, Don C.; Murphy, Thomas E.; Roy, Rajarshi

2017-12-01

We report a new experimental approach using an optoelectronic feedback loop to investigate the dynamics of oscillators coupled on large complex networks with arbitrary topology. Our implementation is based on a single optoelectronic feedback loop with time delays. We use the space-time interpretation of systems with time delay to create large networks of coupled maps. Others have performed similar experiments using high-pass filters to implement the coupling; this restricts the network topology to the coupling of only a few nearest neighbors. In our experiment, the time delays and coupling are implemented on a field-programmable gate array, allowing the creation of networks with arbitrary coupling topology. This system has many advantages: the network nodes are truly identical, the network is easily reconfigurable, and the network dynamics occur at high speeds. We use this system to study cluster synchronization and chimera states in both small and large networks of different topologies.

Default cascades in complex networks: topology and systemic risk.

PubMed

Roukny, Tarik; Bersini, Hugues; Pirotte, Hugues; Caldarelli, Guido; Battiston, Stefano

2013-09-26

The recent crisis has brought to the fore a crucial question that remains still open: what would be the optimal architecture of financial systems? We investigate the stability of several benchmark topologies in a simple default cascading dynamics in bank networks. We analyze the interplay of several crucial drivers, i.e., network topology, banks' capital ratios, market illiquidity, and random vs targeted shocks. We find that, in general, topology matters only--but substantially--when the market is illiquid. No single topology is always superior to others. In particular, scale-free networks can be both more robust and more fragile than homogeneous architectures. This finding has important policy implications. We also apply our methodology to a comprehensive dataset of an interbank market from 1999 to 2011.

Modes of Interaction between Individuals Dominate the Topologies of Real World Networks

PubMed Central

Lee, Insuk; Kim, Eiru; Marcotte, Edward M.

2015-01-01

We find that the topologies of real world networks, such as those formed within human societies, by the Internet, or among cellular proteins, are dominated by the mode of the interactions considered among the individuals. Specifically, a major dichotomy in previously studied networks arises from modeling networks in terms of pairwise versus group tasks. The former often intrinsically give rise to scale-free, disassortative, hierarchical networks, whereas the latter often give rise to single- or broad-scale, assortative, nonhierarchical networks. These dependencies explain contrasting observations among previous topological analyses of real world complex systems. We also observe this trend in systems with natural hierarchies, in which alternate representations of the same networks, but which capture different levels of the hierarchy, manifest these signature topological differences. For example, in both the Internet and cellular proteomes, networks of lower-level system components (routers within domains or proteins within biological processes) are assortative and nonhierarchical, whereas networks of upper-level system components (internet domains or biological processes) are disassortative and hierarchical. Our results demonstrate that network topologies of complex systems must be interpreted in light of their hierarchical natures and interaction types. PMID:25793969

Scheduling and Topology Design in Networks with Directional Antennas

DTIC Science & Technology

2017-05-19

emergency response networks was recently studied in [14] and [15]. This work examines the topology control problem in group - based wireless networks that...Broadcast Fig. 7: Max-min throughput ⇢ versus number of nodes for non -uniform edge capacities [14] T. Suzuki, et al. “Directional Antenna Control based...Scheduling and Topology Design in Networks with Directional Antennas Thomas Stahlbuhk, Nathaniel M. Jones, Brooke Shrader Lincoln Laboratory

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.

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.

High-efficient Extraction of Drainage Networks from Digital Elevation Model Data Constrained by Enhanced Flow Enforcement from Known River Map

NASA Astrophysics Data System (ADS)

Wu, T.; Li, T.; Li, J.; Wang, G.

2017-12-01

Improved drainage network extraction can be achieved by flow enforcement whereby information of known river maps is imposed to the flow-path modeling process. However, the common elevation-based stream burning method can sometimes cause unintended topological errors and misinterpret the overall drainage pattern. We presented an enhanced flow enforcement method to facilitate accurate and efficient process of drainage network extraction. Both the topology of the mapped hydrography and the initial landscape of the DEM are well preserved and fully utilized in the proposed method. An improved stream rasterization is achieved here, yielding continuous, unambiguous and stream-collision-free raster equivalent of stream vectors for flow enforcement. By imposing priority-based enforcement with a complementary flow direction enhancement procedure, the drainage patterns of the mapped hydrography are fully represented in the derived results. The proposed method was tested over the Rogue River Basin, using DEMs with various resolutions. As indicated by the visual and statistical analyses, the proposed method has three major advantages: (1) it significantly reduces the occurrences of topological errors, yielding very accurate watershed partition and channel delineation, (2) it ensures scale-consistent performance at DEMs of various resolutions, and (3) the entire extraction process is well-designed to achieve great computational efficiency.

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

Dorier, Matthieu; Mubarak, Misbah; Ross, Rob

Two-tiered direct network topologies such as Dragonflies have been proposed for future post-petascale and exascale machines, since they provide a high-radix, low-diameter, fast interconnection network. Such topologies call for redesigning MPI collective communication algorithms in order to attain the best performance. Yet as increasingly more applications share a machine, it is not clear how these topology-aware algorithms will react to interference with concurrent jobs accessing the same network. In this paper, we study three topology-aware broadcast algorithms, including one designed by ourselves. We evaluate their performance through event-driven simulation for small- and large-sized broadcasts (in terms of both data sizemore » and number of processes). We study the effect of different routing mechanisms on the topology-aware collective algorithms, as well as their sensitivity to network contention with other jobs. Our results show that while topology-aware algorithms dramatically reduce link utilization, their advantage in terms of latency is more limited.« less

Hyperswitch Network For Hypercube Computer

NASA Technical Reports Server (NTRS)

Chow, Edward; Madan, Herbert; Peterson, John

1989-01-01

Data-driven dynamic switching enables high speed data transfer. Proposed hyperswitch network based on mixed static and dynamic topologies. Routing header modified in response to congestion or faults encountered as path established. Static topology meets requirement if nodes have switching elements that perform necessary routing header revisions dynamically. Hypercube topology now being implemented with switching element in each computer node aimed at designing very-richly-interconnected multicomputer system. Interconnection network connects great number of small computer nodes, using fixed hypercube topology, characterized by point-to-point links between nodes.

Topological properties of robust biological and computational networks

PubMed Central

Navlakha, Saket; He, Xin; Faloutsos, Christos; Bar-Joseph, Ziv

2014-01-01

Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields. PMID:24789562

Structure, Function, and Propagation of Information across Living Two, Four, and Eight Node Degree Topologies.

PubMed

Alagapan, Sankaraleengam; Franca, Eric; Pan, Liangbin; Leondopulos, Stathis; Wheeler, Bruce C; DeMarse, Thomas B

2016-01-01

In this study, we created four network topologies composed of living cortical neurons and compared resultant structural-functional dynamics including the nature and quality of information transmission. Each living network was composed of living cortical neurons and were created using microstamping of adhesion promoting molecules and each was "designed" with different levels of convergence embedded within each structure. Networks were cultured over a grid of electrodes that permitted detailed measurements of neural activity at each node in the network. Of the topologies we tested, the "Random" networks in which neurons connect based on their own intrinsic properties transmitted information embedded within their spike trains with higher fidelity relative to any other topology we tested. Within our patterned topologies in which we explicitly manipulated structure, the effect of convergence on fidelity was dependent on both topology and time-scale (rate vs. temporal coding). A more detailed examination using tools from network analysis revealed that these changes in fidelity were also associated with a number of other structural properties including a node's degree, degree-degree correlations, path length, and clustering coefficients. Whereas information transmission was apparent among nodes with few connections, the greatest transmission fidelity was achieved among the few nodes possessing the highest number of connections (high degree nodes or putative hubs). These results provide a unique view into the relationship between structure and its affect on transmission fidelity, at least within these small neural populations with defined network topology. They also highlight the potential role of tools such as microstamp printing and microelectrode array recordings to construct and record from arbitrary network topologies to provide a new direction in which to advance the study of structure-function relationships.

Assessment of the instantaneous unit hydrograph derived from the theory of topologically random networks

USGS Publications Warehouse

Karlinger, M.R.; Troutman, B.M.

1985-01-01

An instantaneous unit hydrograph (iuh) based on the theory of topologically random networks (topological iuh) is evaluated in terms of sets of basin characteristics and hydraulic parameters. Hydrographs were computed using two linear routing methods for each of two drainage basins in the southeastern United States and are the basis of comparison for the topological iuh's. Elements in the sets of basin characteristics for the topological iuh's are the number of first-order streams only, (N), or the nuber of sources together with the number of channel links in the topological diameter (N, D); the hydraulic parameters are values of the celerity and diffusivity constant. Sensitivity analyses indicate that the mean celerity of the internal links in the network is the critical hydraulic parameter for determining the shape of the topological iuh, while the diffusivity constant has minimal effect on the topological iuh. Asymptotic results (source-only) indicate the number of sources need not be large to approximate the topological iuh with the Weibull probability density function.

Sensitivity and network topology in chemical reaction systems

NASA Astrophysics Data System (ADS)

Okada, Takashi; Mochizuki, Atsushi

2017-08-01

In living cells, biochemical reactions are catalyzed by specific enzymes and connect to one another by sharing substrates and products, forming complex networks. In our previous studies, we established a framework determining the responses to enzyme perturbations only from network topology, and then proved a theorem, called the law of localization, explaining response patterns in terms of network topology. In this paper, we generalize these results to reaction networks with conserved concentrations, which allows us to study any reaction system. We also propose network characteristics quantifying robustness. We compare E. coli metabolic network with randomly rewired networks, and find that the robustness of the E. coli network is significantly higher than that of the random networks.

Virtual network embedding in cross-domain network based on topology and resource attributes

NASA Astrophysics Data System (ADS)

Zhu, Lei; Zhang, Zhizhong; Feng, Linlin; Liu, Lilan

2018-03-01

Aiming at the network architecture ossification and the diversity of access technologies issues, this paper researches the cross-domain virtual network embedding algorithm. By analysing the topological attribute from the local and global perspective of nodes in the virtual network and the physical network, combined with the local network resource property, we rank the embedding priority of the nodes with PCA and TOPSIS methods. Besides, the link load distribution is considered. Above all, We proposed an cross-domain virtual network embedding algorithm based on topology and resource attributes. The simulation results depicts that our algorithm increases the acceptance rate of multi-domain virtual network requests, compared with the existing virtual network embedding algorithm.

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.

Reconstruction of networks from one-step data by matching positions

NASA Astrophysics Data System (ADS)

Wu, Jianshe; Dang, Ni; Jiao, Yang

2018-05-01

It is a challenge in estimating the topology of a network from short time series data. In this paper, matching positions is developed to reconstruct the topology of a network from only one-step data. We consider a general network model of coupled agents, in which the phase transformation of each node is determined by its neighbors. From the phase transformation information from one step to the next, the connections of the tail vertices are reconstructed firstly by the matching positions. Removing the already reconstructed vertices, and repeatedly reconstructing the connections of tail vertices, the topology of the entire network is reconstructed. For sparse scale-free networks with more than ten thousands nodes, we almost obtain the actual topology using only the one-step data in simulations.

Computing Tutte polynomials of contact networks in classrooms

NASA Astrophysics Data System (ADS)

Hincapié, Doracelly; Ospina, Juan

2013-05-01

Objective: The topological complexity of contact networks in classrooms and the potential transmission of an infectious disease were analyzed by sex and age. Methods: The Tutte polynomials, some topological properties and the number of spanning trees were used to algebraically compute the topological complexity. Computations were made with the Maple package GraphTheory. Published data of mutually reported social contacts within a classroom taken from primary school, consisting of children in the age ranges of 4-5, 7-8 and 10-11, were used. Results: The algebraic complexity of the Tutte polynomial and the probability of disease transmission increases with age. The contact networks are not bipartite graphs, gender segregation was observed especially in younger children. Conclusion: Tutte polynomials are tools to understand the topology of the contact networks and to derive numerical indexes of such topologies. It is possible to establish relationships between the Tutte polynomial of a given contact network and the potential transmission of an infectious disease within such network

Default Cascades in Complex Networks: Topology and Systemic Risk

PubMed Central

Roukny, Tarik; Bersini, Hugues; Pirotte, Hugues; Caldarelli, Guido; Battiston, Stefano

2013-01-01

The recent crisis has brought to the fore a crucial question that remains still open: what would be the optimal architecture of financial systems? We investigate the stability of several benchmark topologies in a simple default cascading dynamics in bank networks. We analyze the interplay of several crucial drivers, i.e., network topology, banks' capital ratios, market illiquidity, and random vs targeted shocks. We find that, in general, topology matters only – but substantially – when the market is illiquid. No single topology is always superior to others. In particular, scale-free networks can be both more robust and more fragile than homogeneous architectures. This finding has important policy implications. We also apply our methodology to a comprehensive dataset of an interbank market from 1999 to 2011. PMID:24067913

Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks

NASA Technical Reports Server (NTRS)

Fathpour, Nanaz; Hadaegh, Fred Y.; Mesbahi, Mehran; Rahmani, Amirreza

2011-01-01

Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation-flying estimation algorithms can only be supported via highly centralized, all-to-all, static relative sensing. New algorithms are proposed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms rely on a local information exchange network, relaxing the assumptions on existing algorithms. Distributed space systems rely on a signal transmission network among multiple spacecraft for their operation. Control and coordination among multiple spacecraft in a formation is facilitated via a network of relative sensing and interspacecraft communications. Guidance, navigation, and control rely on the sensing network. This network becomes more complex the more spacecraft are added, or as mission requirements become more complex. The observability of a formation state was observed by a set of local observations from a particular node in the formation. Formation observability can be parameterized in terms of the matrices appearing in the formation dynamics and observation matrices. An agreement protocol was used as a mechanism for observing formation states from local measurements. An agreement protocol is essentially an unforced dynamic system whose trajectory is governed by the interconnection geometry and initial condition of each node, with a goal of reaching a common value of interest. The observability of the interconnected system depends on the geometry of the network, as well as the position of the observer relative to the topology. For the first time, critical GN&C (guidance, navigation, and control estimation) subsystems are synthesized by bringing the contribution of the spacecraft information-exchange network to the forefront of algorithmic analysis and design. The result is a formation estimation algorithm that is modular and robust to variations in the topology and link properties of the underlying formation network.

Topological Phenotypes Constitute a New Dimension in the Phenotypic Space of Leaf Venation Networks

PubMed Central

Ronellenfitsch, Henrik; Lasser, Jana; Daly, Douglas C.; Katifori, Eleni

2015-01-01

The leaves of angiosperms contain highly complex venation networks consisting of recursively nested, hierarchically organized loops. We describe a new phenotypic trait of reticulate vascular networks based on the topology of the nested loops. This phenotypic trait encodes information orthogonal to widely used geometric phenotypic traits, and thus constitutes a new dimension in the leaf venation phenotypic space. We apply our metric to a database of 186 leaves and leaflets representing 137 species, predominantly from the Burseraceae family, revealing diverse topological network traits even within this single family. We show that topological information significantly improves identification of leaves from fragments by calculating a “leaf venation fingerprint” from topology and geometry. Further, we present a phenomenological model suggesting that the topological traits can be explained by noise effects unique to specimen during development of each leaf which leave their imprint on the final network. This work opens the path to new quantitative identification techniques for leaves which go beyond simple geometric traits such as vein density and is directly applicable to other planar or sub-planar networks such as blood vessels in the brain. PMID:26700471

Topological Gyroscopic Metamaterials

NASA Astrophysics Data System (ADS)

Nash, Lisa Michelle

Topological materials are generally insulating in their bulk, with protected conducting states on their boundaries that are robust against disorder and perturbation of material property. The existence of these conducting edge states is characterized by an integer topological invariant. Though the phenomenon was first discovered in electronic systems, recent years have shown that topological states exist in classical systems as well. In this thesis we are primarily concerned with the topological properties of gyroscopic materials, which are created by coupling networks of fast-spinning objects. Through a series of simulations, numerical calculations, and experiments, we show that these materials can support topological edge states. We find that edge states in these gyroscopic metamaterials bear the hallmarks of topology related to broken time reversal symmetry: they transmit excitations unidirectionally and are extremely robust against experimental disorder. We also explore requirements for topology by studying several lattice configurations and find that topology emerges naturally in gyroscopic systems.A simple prescription can be used to create many gyroscopic lattices. Though many of our gyroscopic networks are periodic, we explore amorphous point-sets and find that topology also emerges in these networks.

Do motifs reflect evolved function?--No convergent evolution of genetic regulatory network subgraph topologies.

PubMed

Knabe, Johannes F; Nehaniv, Chrystopher L; Schilstra, Maria J

2008-01-01

Methods that analyse the topological structure of networks have recently become quite popular. Whether motifs (subgraph patterns that occur more often than in randomized networks) have specific functions as elementary computational circuits has been cause for debate. As the question is difficult to resolve with currently available biological data, we approach the issue using networks that abstractly model natural genetic regulatory networks (GRNs) which are evolved to show dynamical behaviors. Specifically one group of networks was evolved to be capable of exhibiting two different behaviors ("differentiation") in contrast to a group with a single target behavior. In both groups we find motif distribution differences within the groups to be larger than differences between them, indicating that evolutionary niches (target functions) do not necessarily mold network structure uniquely. These results show that variability operators can have a stronger influence on network topologies than selection pressures, especially when many topologies can create similar dynamics. Moreover, analysis of motif functional relevance by lesioning did not suggest that motifs were of greater importance to the functioning of the network than arbitrary subgraph patterns. Only when drastically restricting network size, so that one motif corresponds to a whole functionally evolved network, was preference for particular connection patterns found. This suggests that in non-restricted, bigger networks, entanglement with the rest of the network hinders topological subgraph analysis.

Network topology analysis approach on China's QFII stock investment behavior

NASA Astrophysics Data System (ADS)

Zhang, Yongjie; Cao, Xing; He, Feng; Zhang, Wei

2017-05-01

In this paper, the investment behavior of QFII in China stock market from 2004 to 2015 is studied with the network topology method. Based on the nodes topological characteristics, stock holding fluctuations correlation is studied from the micro network level. We conclude that the QFII mutual stock holding network have both scale free and small world properties, which presented mainly small world characteristics from 2005 to 2011, and scale free characteristics from 2012 to 2015. Moreover, fluctuations correlation is different with different nodes topological characteristics. In different economic periods, QFII represented different connection patterns and they reacted to the market crash spontaneously. Thus, this paper provides the first evidence of complex network research on QFII' investment behavior in China as an emerging market.

Extending Topological Approaches to Microseismic-Derived 3D Fracture Networks

NASA Astrophysics Data System (ADS)

Urbancic, T.; Bosman, K.; Baig, A.; Ardakani, E. P.

2017-12-01

Fracture topology is important for determining the fluid-flow characteristics of a fracture network. In most unconventional petroleum applications, flow through subsurface fracture networks is the primary source of production, as matrix permeability is often in the nanodarcy range. Typical models of reservoir discrete fracture networks (DFNs) are constructed using fracture orientation and average spacing, without consideration of how the connectivity of the fracture network aids the percolation of hydrocarbons back to the wellbore. Topological approaches to DFN characterization have been developed and extensively used in analysis of outcrop data and aerial photography. Such study of the surface expression of fracture networks is straight-forward, and the physical form of the observed fractures is directly reflected in the parameters used to describe the topology. However, this analysis largely ignores the three-dimensional nature of natural fracture networks, which is difficult to define accurately in geological studies. SMTI analysis of microseismic event distributions can produce DFNs, where each event is represented by a penny-shaped crack with radius and orientation determined from the frequency content of the waveforms and assessment of the slip instability of the potential fracture planes, respectively. Analysis of the geometric relationships between a set of fractures can provide details of intersections between fractures, and thus the topological characteristics of the fracture network. Extension of existing 2D topology approaches to 3D fracture networks is non-trivial. In the 2D case, a fracture intersection is a single point (node), and branches connect adjacent nodes along fractures. For the 3D case, intersection "nodes" become lines, and connecting nodes to find branches becomes more complicated. There are several parameters defined in 2D topology to quantify the connectivity of the fracture network. Equivalent quantities must be defined and calibrated for the 3D case to provide a meaningful measurement of fracture network connectivity. We have developed an approach to analyze the topology of 3D fracture networks derived from microseismic moment tensors. We illustrate the utility of the approach with applications to example datasets from hydraulic fracturing completions.

Development of structural correlations and synchronization from adaptive rewiring in networks of Kuramoto oscillators

NASA Astrophysics Data System (ADS)

Papadopoulos, Lia; Kim, Jason Z.; Kurths, Jürgen; Bassett, Danielle S.

2017-07-01

Synchronization of non-identical oscillators coupled through complex networks is an important example of collective behavior, and it is interesting to ask how the structural organization of network interactions influences this process. Several studies have explored and uncovered optimal topologies for synchronization by making purposeful alterations to a network. On the other hand, the connectivity patterns of many natural systems are often not static, but are rather modulated over time according to their dynamics. However, this co-evolution and the extent to which the dynamics of the individual units can shape the organization of the network itself are less well understood. Here, we study initially randomly connected but locally adaptive networks of Kuramoto oscillators. In particular, the system employs a co-evolutionary rewiring strategy that depends only on the instantaneous, pairwise phase differences of neighboring oscillators, and that conserves the total number of edges, allowing the effects of local reorganization to be isolated. We find that a simple rule—which preserves connections between more out-of-phase oscillators while rewiring connections between more in-phase oscillators—can cause initially disordered networks to organize into more structured topologies that support enhanced synchronization dynamics. We examine how this process unfolds over time, finding a dependence on the intrinsic frequencies of the oscillators, the global coupling, and the network density, in terms of how the adaptive mechanism reorganizes the network and influences the dynamics. Importantly, for large enough coupling and after sufficient adaptation, the resulting networks exhibit interesting characteristics, including degree-frequency and frequency-neighbor frequency correlations. These properties have previously been associated with optimal synchronization or explosive transitions in which the networks were constructed using global information. On the contrary, by considering a time-dependent interplay between structure and dynamics, this work offers a mechanism through which emergent phenomena and organization can arise in complex systems utilizing local rules.

The Anatomical Distance of Functional Connections Predicts Brain Network Topology in Health and Schizophrenia

PubMed Central

Vértes, Petra E.; Stidd, Reva; Lalonde, François; Clasen, Liv; Rapoport, Judith; Giedd, Jay; Bullmore, Edward T.; Gogtay, Nitin

2013-01-01

The human brain is a topologically complex network embedded in anatomical space. Here, we systematically explored relationships between functional connectivity, complex network topology, and anatomical (Euclidean) distance between connected brain regions, in the resting-state functional magnetic resonance imaging brain networks of 20 healthy volunteers and 19 patients with childhood-onset schizophrenia (COS). Normal between-subject differences in average distance of connected edges in brain graphs were strongly associated with variation in topological properties of functional networks. In addition, a club or subset of connector hubs was identified, in lateral temporal, parietal, dorsal prefrontal, and medial prefrontal/cingulate cortical regions. In COS, there was reduced strength of functional connectivity over short distances especially, and therefore, global mean connection distance of thresholded graphs was significantly greater than normal. As predicted from relationships between spatial and topological properties of normal networks, this disorder-related proportional increase in connection distance was associated with reduced clustering and modularity and increased global efficiency of COS networks. Between-group differences in connection distance were localized specifically to connector hubs of multimodal association cortex. In relation to the neurodevelopmental pathogenesis of schizophrenia, we argue that the data are consistent with the interpretation that spatial and topological disturbances of functional network organization could arise from excessive “pruning” of short-distance functional connections in schizophrenia. PMID:22275481

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.

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

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

Potok, Thomas E; Schuman, Catherine D; Young, Steven R

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determinemore » network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.« less

Signalling Network Construction for Modelling Plant Defence Response

PubMed Central

Miljkovic, Dragana; Stare, Tjaša; Mozetič, Igor; Podpečan, Vid; Petek, Marko; Witek, Kamil; Dermastia, Marina; Lavrač, Nada; Gruden, Kristina

2012-01-01

Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2) triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be utilised for modelling other biological systems, given that an adequate vocabulary is provided. PMID:23272172

Real-space mapping of topological invariants using artificial neural networks

NASA Astrophysics Data System (ADS)

Carvalho, D.; García-Martínez, N. A.; Lado, J. L.; Fernández-Rossier, J.

2018-03-01

Topological invariants allow one to characterize Hamiltonians, predicting the existence of topologically protected in-gap modes. Those invariants can be computed by tracing the evolution of the occupied wave functions under twisted boundary conditions. However, those procedures do not allow one to calculate a topological invariant by evaluating the system locally, and thus require information about the wave functions in the whole system. Here we show that artificial neural networks can be trained to identify the topological order by evaluating a local projection of the density matrix. We demonstrate this for two different models, a one-dimensional topological superconductor and a two-dimensional quantum anomalous Hall state, both with spatially modulated parameters. Our neural network correctly identifies the different topological domains in real space, predicting the location of in-gap states. By combining a neural network with a calculation of the electronic states that uses the kernel polynomial method, we show that the local evaluation of the invariant can be carried out by evaluating a local quantity, in particular for systems without translational symmetry consisting of tens of thousands of atoms. Our results show that supervised learning is an efficient methodology to characterize the local topology of a system.

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.

Expected Number of Fixed Points in Boolean Networks with Arbitrary Topology.

PubMed

Mori, Fumito; Mochizuki, Atsushi

2017-07-14

Boolean network models describe genetic, neural, and social dynamics in complex networks, where the dynamics depend generally on network topology. Fixed points in a genetic regulatory network are typically considered to correspond to cell types in an organism. We prove that the expected number of fixed points in a Boolean network, with Boolean functions drawn from probability distributions that are not required to be uniform or identical, is one, and is independent of network topology if only a feedback arc set satisfies a stochastic neutrality condition. We also demonstrate that the expected number is increased by the predominance of positive feedback in a cycle.

Altered Cerebral Blood Flow Covariance Network in Schizophrenia.

PubMed

Liu, Feng; Zhuo, Chuanjun; Yu, Chunshui

2016-01-01

Many studies have shown abnormal cerebral blood flow (CBF) in schizophrenia; however, it remains unclear how topological properties of CBF network are altered in this disorder. Here, arterial spin labeling (ASL) MRI was employed to measure resting-state CBF in 96 schizophrenia patients and 91 healthy controls. CBF covariance network of each group was constructed by calculating across-subject CBF covariance between 90 brain regions. Graph theory was used to compare intergroup differences in global and nodal topological measures of the network. Both schizophrenia patients and healthy controls had small-world topology in CBF covariance networks, implying an optimal balance between functional segregation and integration. Compared with healthy controls, schizophrenia patients showed reduced small-worldness, normalized clustering coefficient and local efficiency of the network, suggesting a shift toward randomized network topology in schizophrenia. Furthermore, schizophrenia patients exhibited altered nodal centrality in the perceptual-, affective-, language-, and spatial-related regions, indicating functional disturbance of these systems in schizophrenia. This study demonstrated for the first time that schizophrenia patients have disrupted topological properties in CBF covariance network, which provides a new perspective (efficiency of blood flow distribution between brain regions) for understanding neural mechanisms of schizophrenia.

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

PubMed Central

2017-01-01

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

Nonlinear conduction via solitons in a topological mechanical insulator.

PubMed

Chen, Bryan Gin-ge; Upadhyaya, Nitin; Vitelli, Vincenzo

2014-09-09

Networks of rigid bars connected by joints, termed linkages, provide a minimal framework to design robotic arms and mechanical metamaterials built of folding components. Here, we investigate a chain-like linkage that, according to linear elasticity, behaves like a topological mechanical insulator whose zero-energy modes are localized at the edge. Simple experiments we performed using prototypes of the chain vividly illustrate how the soft motion, initially localized at the edge, can in fact propagate unobstructed all of the way to the opposite end. Using real prototypes, simulations, and analytical models, we demonstrate that the chain is a mechanical conductor, whose carriers are nonlinear solitary waves, not captured within linear elasticity. Indeed, the linkage prototype can be regarded as the simplest example of a topological metamaterial whose protected mechanical excitations are solitons, moving domain walls between distinct topological mechanical phases. More practically, we have built a topologically protected mechanism that can perform basic tasks such as transporting a mechanical state from one location to another. Our work paves the way toward adopting the principle of topological robustness in the design of robots assembled from activated linkages as well as in the fabrication of complex molecular nanostructures.

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.

Age structure and cooperation in coevolutionary games on dynamic network

NASA Astrophysics Data System (ADS)

Qin, Zilong; Hu, Zhenhua; Zhou, Xiaoping; Yi, Jingzhang

2015-04-01

Our proposed model imitates the growth of a population and describes the age structure and the level of cooperation in games on dynamic network with continuous changes of structure and topology. The removal of nodes and links caused by age-dependent attack, together with the nodes addition standing for the newborns of population, badly ruins Matthew effect in this coevolutionary process. Though the network is generated by growth and preferential attachment, it degenerates into random network and it is no longer heterogeneous. When the removal of nodes and links is equal to the addition of nodes and links, the size of dynamic network is maintained in steady-state, so is the low level of cooperation. Severe structure variation, homogeneous topology and continuous invasion of new defection jointly make dynamic network unsuitable for the survival of cooperator even when the probability with which the newborn players initially adopt the strategy cooperation is high, while things change slightly when the connections of newborn players are restricted. Fortunately, moderate interactions in a generation trigger an optimal recovering process to encourage cooperation. The model developed in this paper outlines an explanation of the cohesion changes in the development process of an organization. Some suggestions for cooperative behavior improvement are given in the end.

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

Synchronized state of coupled dynamics on time-varying networks.

PubMed

Amritkar, R E; Hu, Chin-Kun

2006-03-01

We consider synchronization properties of coupled dynamics on time-varying networks and the corresponding time-average network. We find that if the different Laplacians corresponding to the time-varying networks commute with each other then the stability of the synchronized state for both the time-varying and the time-average topologies are approximately the same. On the other hand for noncommuting Laplacians the stability of the synchronized state for the time-varying topology is in general better than the time-average topology.

Organization of excitable dynamics in hierarchical biological networks.

PubMed

Müller-Linow, Mark; Hilgetag, Claus C; Hütt, Marc-Thorsten

2008-09-26

This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

Structure and Topology Dynamics of Hyper-Frequency Networks during Rest and Auditory Oddball Performance.

PubMed

Müller, Viktor; Perdikis, Dionysios; von Oertzen, Timo; Sleimen-Malkoun, Rita; Jirsa, Viktor; Lindenberger, Ulman

2016-01-01

Resting-state and task-related recordings are characterized by oscillatory brain activity and widely distributed networks of synchronized oscillatory circuits. Electroencephalographic recordings (EEG) were used to assess network structure and network dynamics during resting state with eyes open and closed, and auditory oddball performance through phase synchronization between EEG channels. For this assessment, we constructed a hyper-frequency network (HFN) based on within- and cross-frequency coupling (WFC and CFC, respectively) at 10 oscillation frequencies ranging between 2 and 20 Hz. We found that CFC generally differentiates between task conditions better than WFC. CFC was the highest during resting state with eyes open. Using a graph-theoretical approach (GTA), we found that HFNs possess small-world network (SWN) topology with a slight tendency to random network characteristics. Moreover, analysis of the temporal fluctuations of HFNs revealed specific network topology dynamics (NTD), i.e., temporal changes of different graph-theoretical measures such as strength, clustering coefficient, characteristic path length (CPL), local, and global efficiency determined for HFNs at different time windows. The different topology metrics showed significant differences between conditions in the mean and standard deviation of these metrics both across time and nodes. In addition, using an artificial neural network approach, we found stimulus-related dynamics that varied across the different network topology metrics. We conclude that functional connectivity dynamics (FCD), or NTD, which was found using the HFN approach during rest and stimulus processing, reflects temporal and topological changes in the functional organization and reorganization of neuronal cell assemblies.

Structure and Topology Dynamics of Hyper-Frequency Networks during Rest and Auditory Oddball Performance

PubMed Central

Müller, Viktor; Perdikis, Dionysios; von Oertzen, Timo; Sleimen-Malkoun, Rita; Jirsa, Viktor; Lindenberger, Ulman

2016-01-01

Resting-state and task-related recordings are characterized by oscillatory brain activity and widely distributed networks of synchronized oscillatory circuits. Electroencephalographic recordings (EEG) were used to assess network structure and network dynamics during resting state with eyes open and closed, and auditory oddball performance through phase synchronization between EEG channels. For this assessment, we constructed a hyper-frequency network (HFN) based on within- and cross-frequency coupling (WFC and CFC, respectively) at 10 oscillation frequencies ranging between 2 and 20 Hz. We found that CFC generally differentiates between task conditions better than WFC. CFC was the highest during resting state with eyes open. Using a graph-theoretical approach (GTA), we found that HFNs possess small-world network (SWN) topology with a slight tendency to random network characteristics. Moreover, analysis of the temporal fluctuations of HFNs revealed specific network topology dynamics (NTD), i.e., temporal changes of different graph-theoretical measures such as strength, clustering coefficient, characteristic path length (CPL), local, and global efficiency determined for HFNs at different time windows. The different topology metrics showed significant differences between conditions in the mean and standard deviation of these metrics both across time and nodes. In addition, using an artificial neural network approach, we found stimulus-related dynamics that varied across the different network topology metrics. We conclude that functional connectivity dynamics (FCD), or NTD, which was found using the HFN approach during rest and stimulus processing, reflects temporal and topological changes in the functional organization and reorganization of neuronal cell assemblies. PMID:27799906

Multi-attribute integrated measurement of node importance in complex networks.

PubMed

Wang, Shibo; Zhao, Jinlou

2015-11-01

The measure of node importance in complex networks is very important to the research of networks stability and robustness; it also can ensure the security of the whole network. Most researchers have used a single indicator to measure the networks node importance, so that the obtained measurement results only reflect certain aspects of the networks with a loss of information. Meanwhile, because of the difference of networks topology, the nodes' importance should be described by combining the character of the networks topology. Most of the existing evaluation algorithms cannot completely reflect the circumstances of complex networks, so this paper takes into account the degree of centrality, the relative closeness centrality, clustering coefficient, and topology potential and raises an integrated measuring method to measure the nodes' importance. This method can reflect nodes' internal and outside attributes and eliminate the influence of network structure on the node importance. The experiments of karate network and dolphin network show that networks topology structure integrated measure has smaller range of metrical result than a single indicator and more universal. Experiments show that attacking the North American power grid and the Internet network with the method has a faster convergence speed than other methods.

Inferring Centrality from Network Snapshots

PubMed Central

Shao, Haibin; Mesbahi, Mehran; Li, Dewei; Xi, Yugeng

2017-01-01

The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagation rate of influence exerted on consensus networks and the Kirchhoff index of the underlying graph. Moreover, the tempo centrality also encodes the disturbance rejection of nodes in a consensus network. Our findings provide an approach to infer the performance of a consensus network from its temporal data. PMID:28098166

Inferring Centrality from Network Snapshots

NASA Astrophysics Data System (ADS)

Shao, Haibin; Mesbahi, Mehran; Li, Dewei; Xi, Yugeng

2017-01-01

The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagation rate of influence exerted on consensus networks and the Kirchhoff index of the underlying graph. Moreover, the tempo centrality also encodes the disturbance rejection of nodes in a consensus network. Our findings provide an approach to infer the performance of a consensus network from its temporal data.

Interaction Control to Synchronize Non-synchronizable Networks.

PubMed

Schröder, Malte; Chakraborty, Sagar; Witthaut, Dirk; Nagler, Jan; Timme, Marc

2016-11-17

Synchronization constitutes one of the most fundamental collective dynamics across networked systems and often underlies their function. Whether a system may synchronize depends on the internal unit dynamics as well as the topology and strength of their interactions. For chaotic units with certain interaction topologies synchronization might be impossible across all interaction strengths, meaning that these networks are non-synchronizable. Here we propose the concept of interaction control, generalizing transient uncoupling, to induce desired collective dynamics in complex networks and apply it to synchronize even such non-synchronizable systems. After highlighting that non-synchronizability prevails for a wide range of networks of arbitrary size, we explain how a simple binary control may localize interactions in state space and thereby synchronize networks. Intriguingly, localizing interactions by a fixed control scheme enables stable synchronization across all connected networks regardless of topological constraints. Interaction control may thus ease the design of desired collective dynamics even without knowledge of the networks' exact interaction topology and consequently have implications for biological and self-organizing technical systems.

Chaotic, informational and synchronous behaviour of multiplex networks

NASA Astrophysics Data System (ADS)

Baptista, M. S.; Szmoski, R. M.; Pereira, R. F.; Pinto, S. E. De Souza

2016-03-01

The understanding of the relationship between topology and behaviour in interconnected networks would allow to charac- terise and predict behaviour in many real complex networks since both are usually not simultaneously known. Most previous studies have focused on the relationship between topology and synchronisation. In this work, we provide analytical formulas that shows how topology drives complex behaviour: chaos, information, and weak or strong synchronisation; in multiplex net- works with constant Jacobian. We also study this relationship numerically in multiplex networks of Hindmarsh-Rose neurons. Whereas behaviour in the analytically tractable network is a direct but not trivial consequence of the spectra of eigenvalues of the Laplacian matrix, where behaviour may strongly depend on the break of symmetry in the topology of interconnections, in Hindmarsh-Rose neural networks the nonlinear nature of the chemical synapses breaks the elegant mathematical connec- tion between the spectra of eigenvalues of the Laplacian matrix and the behaviour of the network, creating networks whose behaviour strongly depends on the nature (chemical or electrical) of the inter synapses.

Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks.

PubMed

Jovanović, Stojan; Rotter, Stefan

2016-06-01

The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs) are responsible for their emergence. Comparing two different models of network topology-random networks of Erdős-Rényi type and networks with highly interconnected hubs-we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations.

Modifier constraint in alkali borophosphate glasses using topological constraint theory

NASA Astrophysics Data System (ADS)

Li, Xiang; Zeng, Huidan; Jiang, Qi; Zhao, Donghui; Chen, Guorong; Wang, Zhaofeng; Sun, Luyi; Chen, Jianding

2016-12-01

In recent years, composition-dependent properties of glasses have been successfully predicted using the topological constraint theory. The constraints of the glass network are derived from two main parts: network formers and network modifiers. The constraints of the network formers can be calculated on the basis of the topological structure of the glass. However, the latter cannot be accurately calculated in this way, because of the existing of ionic bonds. In this paper, the constraints of the modifier ions in phosphate glasses were thoroughly investigated using the topological constraint theory. The results show that the constraints of the modifier ions are gradually increased with the addition of alkali oxides. Furthermore, an improved topological constraint theory for borophosphate glasses is proposed by taking the composition-dependent constraints of the network modifiers into consideration. The proposed theory is subsequently evaluated by analyzing the composition dependence of the glass transition temperature in alkali borophosphate glasses. This method is supposed to be extended to other similar glass systems containing alkali ions.

Computational models of location-invariant orthographic processing

NASA Astrophysics Data System (ADS)

Dandurand, Frédéric; Hannagan, Thomas; Grainger, Jonathan

2013-03-01

We trained three topologies of backpropagation neural networks to discriminate 2000 words (lexical representations) presented at different positions of a horizontal letter array. The first topology (zero-deck) contains no hidden layer, the second (one-deck) has a single hidden layer, and for the last topology (two-deck), the task is divided in two subtasks implemented as two stacked neural networks, with explicit word-centred letters as intermediate representations. All topologies successfully simulated two key benchmark phenomena observed in skilled human reading: transposed-letter priming and relative-position priming. However, the two-deck topology most accurately simulated the ability to discriminate words from nonwords, while containing the fewest connection weights. We analysed the internal representations after training. Zero-deck networks implement a letter-based scheme with a position bias to differentiate anagrams. One-deck networks implement a holographic overlap coding in which representations are essentially letter-based and words are linear combinations of letters. Two-deck networks also implement holographic-coding.

Spectral Analysis of Rich Network Topology in Social Networks

ERIC Educational Resources Information Center

Wu, Leting

2013-01-01

Social networks have received much attention these days. Researchers have developed different methods to study the structure and characteristics of the network topology. Our focus is on spectral analysis of the adjacency matrix of the underlying network. Recent work showed good properties in the adjacency spectral space but there are few…

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow.

PubMed

Zubek, Julian; Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz

2017-01-01

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems.

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow

PubMed Central

Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz

2017-01-01

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems. PMID:28809957

Effects of substrate network topologies on competition dynamics

NASA Astrophysics Data System (ADS)

Lee, Sang Hoon; Jeong, Hawoong

2006-08-01

We study a competition dynamics, based on the minority game, endowed with various substrate network structures. We observe the effects of the network topologies by investigating the volatility of the system and the structure of follower networks. The topology of substrate structures significantly influences the system efficiency represented by the volatility and such substrate networks are shown to amplify the herding effect and cause inefficiency in most cases. The follower networks emerging from the leadership structure show a power-law incoming degree distribution. This study shows the emergence of scale-free structures of leadership in the minority game and the effects of the interaction among players on the networked version of the game.

A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

PubMed

Kentzoglanakis, Kyriakos; Poole, Matthew

2012-01-01

In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

Positron Emission Tomography Reveals Abnormal Topological Organization in Functional Brain Network in Diabetic Patients.

PubMed

Qiu, Xiangzhe; Zhang, Yanjun; Feng, Hongbo; Jiang, Donglang

2016-01-01

Recent studies have demonstrated alterations in the topological organization of structural brain networks in diabetes mellitus (DM). However, the DM-related changes in the topological properties in functional brain networks are unexplored so far. We therefore used fluoro-D-glucose positron emission tomography (FDG-PET) data to construct functional brain networks of 73 DM patients and 91 sex- and age-matched normal controls (NCs), followed by a graph theoretical analysis. We found that both DM patients and NCs had a small-world topology in functional brain network. In comparison to the NC group, the DM group was found to have significantly lower small-world index, lower normalized clustering coefficients and higher normalized characteristic path length. Moreover, for diabetic patients, the nodal centrality was significantly reduced in the right rectus, the right cuneus, the left middle occipital gyrus, and the left postcentral gyrus, and it was significantly increased in the orbitofrontal region of the left middle frontal gyrus, the left olfactory region, and the right paracentral lobule. Our results demonstrated that the diabetic brain was associated with disrupted topological organization in the functional PET network, thus providing functional evidence for the abnormalities of brain networks in DM.

Homeostatic structural plasticity can account for topology changes following deafferentation and focal stroke.

PubMed

Butz, Markus; Steenbuck, Ines D; van Ooyen, Arjen

2014-01-01

After brain lesions caused by tumors or stroke, or after lasting loss of input (deafferentation), inter- and intra-regional brain networks respond with complex changes in topology. Not only areas directly affected by the lesion but also regions remote from the lesion may alter their connectivity-a phenomenon known as diaschisis. Changes in network topology after brain lesions can lead to cognitive decline and increasing functional disability. However, the principles governing changes in network topology are poorly understood. Here, we investigated whether homeostatic structural plasticity can account for changes in network topology after deafferentation and brain lesions. Homeostatic structural plasticity postulates that neurons aim to maintain a desired level of electrical activity by deleting synapses when neuronal activity is too high and by providing new synaptic contacts when activity is too low. Using our Model of Structural Plasticity, we explored how local changes in connectivity induced by a focal loss of input affected global network topology. In accordance with experimental and clinical data, we found that after partial deafferentation, the network as a whole became more random, although it maintained its small-world topology, while deafferentated neurons increased their betweenness centrality as they rewired and returned to the homeostatic range of activity. Furthermore, deafferentated neurons increased their global but decreased their local efficiency and got longer tailed degree distributions, indicating the emergence of hub neurons. Together, our results suggest that homeostatic structural plasticity may be an important driving force for lesion-induced network reorganization and that the increase in betweenness centrality of deafferentated areas may hold as a biomarker for brain repair.

Effects in the network topology due to node aggregation: Empirical evidence from the domestic maritime transportation in Greece

NASA Astrophysics Data System (ADS)

Tsiotas, Dimitrios; Polyzos, Serafeim

2018-02-01

This article studies the topological consistency of spatial networks due to node aggregation, examining the changes captured between different network representations that result from nodes' grouping and they refer to the same socioeconomic system. The main purpose of this study is to evaluate what kind of topological information remains unalterable due to node aggregation and, further, to develop a framework for linking the data of an empirical network with data of its socioeconomic environment, when the latter are available for hierarchically higher levels of aggregation, in an effort to promote the interdisciplinary research in the field of complex network analysis. The research question is empirically tested on topological and socioeconomic data extracted from the Greek Maritime Network (GMN) that is modeled as a non-directed multilayer (bilayer) graph consisting of a port-layer, where nodes represent ports, and a prefecture-layer, where nodes represent coastal and insular prefectural groups of ports. The analysis highlights that the connectivity (degree) of the GMN is the most consistent aspect of this multilayer network, which preserves both the topological and the socioeconomic information through node aggregation. In terms of spatial analysis and regional science, such effects illustrate the effectiveness of the prefectural administrative division for the functionality of the Greek maritime transportation system. Overall, this approach proposes a methodological framework that can enjoy further applications about the grouping effects induced on the network topology, providing physical, technical, socioeconomic, strategic or political insights.

Mapping to Irregular Torus Topologies and Other Techniques for Petascale Biomolecular Simulation

PubMed Central

Phillips, James C.; Sun, Yanhua; Jain, Nikhil; Bohm, Eric J.; Kalé, Laxmikant V.

2014-01-01

Currently deployed petascale supercomputers typically use toroidal network topologies in three or more dimensions. While these networks perform well for topology-agnostic codes on a few thousand nodes, leadership machines with 20,000 nodes require topology awareness to avoid network contention for communication-intensive codes. Topology adaptation is complicated by irregular node allocation shapes and holes due to dedicated input/output nodes or hardware failure. In the context of the popular molecular dynamics program NAMD, we present methods for mapping a periodic 3-D grid of fixed-size spatial decomposition domains to 3-D Cray Gemini and 5-D IBM Blue Gene/Q toroidal networks to enable hundred-million atom full machine simulations, and to similarly partition node allocations into compact domains for smaller simulations using multiple-copy algorithms. Additional enabling techniques are discussed and performance is reported for NCSA Blue Waters, ORNL Titan, ANL Mira, TACC Stampede, and NERSC Edison. PMID:25594075

A Network Topology Control and Identity Authentication Protocol with Support for Movable Sensor Nodes

PubMed Central

Zhang, Ying; Chen, Wei; Liang, Jixing; Zheng, Bingxin; Jiang, Shengming

2015-01-01

It is expected that in the near future wireless sensor network (WSNs) will be more widely used in the mobile environment, in applications such as Autonomous Underwater Vehicles (AUVs) for marine monitoring and mobile robots for environmental investigation. The sensor nodes’ mobility can easily cause changes to the structure of a network topology, and lead to the decline in the amount of transmitted data, excessive energy consumption, and lack of security. To solve these problems, a kind of efficient Topology Control algorithm for node Mobility (TCM) is proposed. In the topology construction stage, an efficient clustering algorithm is adopted, which supports sensor node movement. It can ensure the balance of clustering, and reduce the energy consumption. In the topology maintenance stage, the digital signature authentication based on Error Correction Code (ECC) and the communication mechanism of soft handover are adopted. After verifying the legal identity of the mobile nodes, secure communications can be established, and this can increase the amount of data transmitted. Compared to some existing schemes, the proposed scheme has significant advantages regarding network topology stability, amounts of data transferred, lifetime and safety performance of the network. PMID:26633405

A Network Topology Control and Identity Authentication Protocol with Support for Movable Sensor Nodes.

PubMed

Zhang, Ying; Chen, Wei; Liang, Jixing; Zheng, Bingxin; Jiang, Shengming

2015-12-01

It is expected that in the near future wireless sensor network (WSNs) will be more widely used in the mobile environment, in applications such as Autonomous Underwater Vehicles (AUVs) for marine monitoring and mobile robots for environmental investigation. The sensor nodes' mobility can easily cause changes to the structure of a network topology, and lead to the decline in the amount of transmitted data, excessive energy consumption, and lack of security. To solve these problems, a kind of efficient Topology Control algorithm for node Mobility (TCM) is proposed. In the topology construction stage, an efficient clustering algorithm is adopted, which supports sensor node movement. It can ensure the balance of clustering, and reduce the energy consumption. In the topology maintenance stage, the digital signature authentication based on Error Correction Code (ECC) and the communication mechanism of soft handover are adopted. After verifying the legal identity of the mobile nodes, secure communications can be established, and this can increase the amount of data transmitted. Compared to some existing schemes, the proposed scheme has significant advantages regarding network topology stability, amounts of data transferred, lifetime and safety performance of the network.

Structural complexity, movement bias, and metapopulation extinction risk in dendritic ecological networks

USGS Publications Warehouse

Campbell Grant, Evan H.

2011-01-01

Spatial complexity in metacommunities can be separated into 3 main components: size (i.e., number of habitat patches), spatial arrangement of habitat patches (network topology), and diversity of habitat patch types. Much attention has been paid to lattice-type networks, such as patch-based metapopulations, but interest in understanding ecological networks of alternative geometries is building. Dendritic ecological networks (DENs) include some increasingly threatened ecological systems, such as caves and streams. The restrictive architecture of dendritic ecological networks might have overriding implications for species persistence. I used a modeling approach to investigate how number and spatial arrangement of habitat patches influence metapopulation extinction risk in 2 DENs of different size and topology. Metapopulation persistence was higher in larger networks, but this relationship was mediated by network topology and the dispersal pathways used to navigate the network. Larger networks, especially those with greater topological complexity, generally had lower extinction risk than smaller and less-complex networks, but dispersal bias and magnitude affected the shape of this relationship. Applying these general results to real systems will require empirical data on the movement behavior of organisms and will improve our understanding of the implications of network complexity on population and community patterns and processes.

Disrupted Structural Brain Network in AD and aMCI: A Finding of Long Fiber Degeneration.

PubMed

Fang, Rong; Yan, Xiao-Xiao; Wu, Zhi-Yuan; Sun, Yu; Yin, Qi-Hua; Wang, Ying; Tang, Hui-Dong; Sun, Jun-Feng; Miao, Fei; Chen, Sheng-Di

2015-01-01

Although recent evidence has emerged that Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) patients show both regional brain abnormalities and topological degeneration in brain networks, our understanding of the effects of white matter fiber aberrations on brain network topology in AD and aMCI is still rudimentary. In this study, we investigated the regional volumetric aberrations and the global topological abnormalities in AD and aMCI patients. The results showed a widely distributed atrophy in both gray and white matters in the AD and aMCI groups. In particular, AD patients had weaker connectivity with long fiber length than aMCI and normal control (NC) groups, as assessed by fractional anisotropy (FA). Furthermore, the brain networks of all three groups exhibited prominent economical small-world properties. Interestingly, the topological characteristics estimated from binary brain networks showed no significant group effect, indicating a tendency of preserving an optimal topological architecture in AD and aMCI during degeneration. However, significantly longer characteristic path length was observed in the FA weighted brain networks of AD and aMCI patients, suggesting dysfunctional global integration. Moreover, the abnormality of the characteristic path length was negatively correlated with the clinical ratings of cognitive impairment. Thus, the results therefore suggested that the topological alterations in weighted brain networks of AD are induced by the loss of connectivity with long fiber lengths. Our findings provide new insights into the alterations of the brain network in AD and may indicate the predictive value of the network metrics as biomarkers of disease development.

Topological Principles of Control in Dynamical Networks

NASA Astrophysics Data System (ADS)

Kim, Jason; Pasqualetti, Fabio; Bassett, Danielle

Networked biological systems, such as the brain, feature complex patterns of interactions. To predict and correct the dynamic behavior of such systems, it is imperative to understand how the underlying topological structure affects and limits the function of the system. Here, we use network control theory to extract topological features that favor or prevent network controllability, and to understand the network-wide effect of external stimuli on large-scale brain systems. Specifically, we treat each brain region as a dynamic entity with real-valued state, and model the time evolution of all interconnected regions using linear, time-invariant dynamics. We propose a simplified feed-forward scheme where the effect of upstream regions (drivers) on the connected downstream regions (non-drivers) is characterized in closed-form. Leveraging this characterization of the simplified model, we derive topological features that predict the controllability properties of non-simplified networks. We show analytically and numerically that these predictors are accurate across a large range of parameters. Among other contributions, our analysis shows that heterogeneity in the network weights facilitate controllability, and allows us to implement targeted interventions that profoundly improve controllability. By assuming an underlying dynamical mechanism, we are able to understand the complex topology of networked biological systems in a functionally meaningful way.

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 .

Quantifying climatic controls on river network topology across scales

NASA Astrophysics Data System (ADS)

Ranjbar Moshfeghi, S.; Hooshyar, M.; Wang, D.; Singh, A.

2017-12-01

Branching structure of river networks is an important topologic and geomorphologic feature that depends on several factors (e.g. climate, tectonic). However, mechanisms that cause these drainage patterns in river networks are poorly understood. In this study, we investigate the effects of varying climatic forcing on river network topology and geomorphology. For this, we select 20 catchments across the United States with different long-term climatic conditions quantified by climate aridity index (AI), defined here as the ratio of mean annual potential evaporation (Ep) to precipitation (P), capturing variation in runoff and vegetation cover. The river networks of these catchments are extracted, using a curvature-based method, from high-resolution (1 m) digital elevation models and several metrics such as drainage density, branching angle, and width functions are computed. We also use a multiscale-entropy-based approach to quantify the topologic irregularity and structural richness of these river networks. Our results reveal systematic impacts of climate forcing on the structure of river networks.

Interaction Control to Synchronize Non-synchronizable Networks

PubMed Central

Schröder, Malte; Chakraborty, Sagar; Witthaut, Dirk; Nagler, Jan; Timme, Marc

2016-01-01

Synchronization constitutes one of the most fundamental collective dynamics across networked systems and often underlies their function. Whether a system may synchronize depends on the internal unit dynamics as well as the topology and strength of their interactions. For chaotic units with certain interaction topologies synchronization might be impossible across all interaction strengths, meaning that these networks are non-synchronizable. Here we propose the concept of interaction control, generalizing transient uncoupling, to induce desired collective dynamics in complex networks and apply it to synchronize even such non-synchronizable systems. After highlighting that non-synchronizability prevails for a wide range of networks of arbitrary size, we explain how a simple binary control may localize interactions in state space and thereby synchronize networks. Intriguingly, localizing interactions by a fixed control scheme enables stable synchronization across all connected networks regardless of topological constraints. Interaction control may thus ease the design of desired collective dynamics even without knowledge of the networks’ exact interaction topology and consequently have implications for biological and self-organizing technical systems. PMID:27853266

Topology for efficient information dissemination in ad-hoc networking

NASA Technical Reports Server (NTRS)

Jennings, E.; Okino, C. M.

2002-01-01

In this paper, we explore the information dissemination problem in ad-hoc wirless networks. First, we analyze the probability of successful broadcast, assuming: the nodes are uniformly distributed, the available area has a lower bould relative to the total number of nodes, and there is zero knowledge of the overall topology of the network. By showing that the probability of such events is small, we are motivated to extract good graph topologies to minimize the overall transmissions. Three algorithms are used to generate topologies of the network with guaranteed connectivity. These are the minimum radius graph, the relative neighborhood graph and the minimum spanning tree. Our simulation shows that the relative neighborhood graph has certain good graph properties, which makes it suitable for efficient information dissemination.

Intermittent and sustained periodic windows in networked chaotic Rössler oscillators

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

He, Zhiwei; Sun, Yong; University of the Chinese Academy of Sciences, Beijing 100049

Route to chaos (or periodicity) in dynamical systems is one of fundamental problems. Here, dynamical behaviors of coupled chaotic Rössler oscillators on complex networks are investigated and two different types of periodic windows with the variation of coupling strength are found. Under a moderate coupling, the periodic window is intermittent, and the attractors within the window extremely sensitively depend on the initial conditions, coupling parameter, and topology of the network. Therefore, after adding or removing one edge of network, the periodic attractor can be destroyed and substituted by a chaotic one, or vice versa. In contrast, under an extremely weakmore » coupling, another type of periodic window appears, which insensitively depends on the initial conditions, coupling parameter, and network. It is sustained and unchanged for different types of network structure. It is also found that the phase differences of the oscillators are almost discrete and randomly distributed except that directly linked oscillators more likely have different phases. These dynamical behaviors have also been generally observed in other networked chaotic oscillators.« less

Synchronization Dynamics of Coupled Chemical Oscillators

NASA Astrophysics Data System (ADS)

Tompkins, Nathan

The synchronization dynamics of complex networks have been extensively studied over the past few decades due to their ubiquity in the natural world. Prominent examples include cardiac rhythms, circadian rhythms, the flashing of fireflies, predator/prey population dynamics, mammalian gait, human applause, pendulum clocks, the electrical grid, and of the course the brain. Detailed experiments have been done to map the topology of many of these systems and significant advances have been made to describe the mathematics of these networks. Compared to these bodies of work relatively little has been done to directly test the role of topology in the synchronization dynamics of coupled oscillators. This Dissertation develops technology to examine the dynamics due to topology within networks of discrete oscillatory components. The oscillatory system used here consists of the photo-inhibitable Belousov-Zhabotinsky (BZ) reaction water-in-oil emulsion where the oscillatory drops are diffusively coupled to one another and the topology is defined by the geometry of the diffusive connections. Ring networks are created from a close-packed 2D array of drops using the Programmable Illumination Microscope (PIM) in order to test Turing's theory of morphogenesis directly. Further technology is developed to create custom planar networks of BZ drops in more complicated topologies which can be individually perturbed using illumination from the PIM. The work presented here establishes the validity of using the BZ emulsion system with a PIM to study the topology induced effects on the synchronization dynamics of coupled chemical oscillators, tests the successes and limitations of Turing's theory of morphogenesis, and develops new technology to further probe the effects of network topology on a system of coupled oscillators. Finally, this Dissertation concludes by describing ongoing experiments which utilize this new technology to examine topology induced transitions of synchronization dynamics of diffusively coupled chemical oscillators.

HubAlign: an accurate and efficient method for global alignment of protein-protein interaction networks.

PubMed

Hashemifar, Somaye; Xu, Jinbo

2014-09-01

High-throughput experimental techniques have produced a large amount of protein-protein interaction (PPI) data. The study of PPI networks, such as comparative analysis, shall benefit the understanding of life process and diseases at the molecular level. One way of comparative analysis is to align PPI networks to identify conserved or species-specific subnetwork motifs. A few methods have been developed for global PPI network alignment, but it still remains challenging in terms of both accuracy and efficiency. This paper presents a novel global network alignment algorithm, denoted as HubAlign, that makes use of both network topology and sequence homology information, based upon the observation that topologically important proteins in a PPI network usually are much more conserved and thus, more likely to be aligned. HubAlign uses a minimum-degree heuristic algorithm to estimate the topological and functional importance of a protein from the global network topology information. Then HubAlign aligns topologically important proteins first and gradually extends the alignment to the whole network. Extensive tests indicate that HubAlign greatly outperforms several popular methods in terms of both accuracy and efficiency, especially in detecting functionally similar proteins. HubAlign is available freely for non-commercial purposes at http://ttic.uchicago.edu/∼hashemifar/software/HubAlign.zip. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics

PubMed Central

Prescott, Aaron M.; McCollough, Forest W.; Eldreth, Bryan L.; Binder, Brad M.; Abel, Steven M.

2016-01-01

Most models for ethylene signaling involve a linear pathway. However, measurements of seedling growth kinetics when ethylene is applied and removed have resulted in more complex network models that include coherent feedforward, negative feedback, and positive feedback motifs. The dynamical responses of the proposed networks have not been explored in a quantitative manner. Here, we explore (i) whether any of the proposed models are capable of producing growth-response behaviors consistent with experimental observations and (ii) what mechanistic roles various parts of the network topologies play in ethylene signaling. To address this, we used computational methods to explore two general network topologies: The first contains a coherent feedforward loop that inhibits growth and a negative feedback from growth onto itself (CFF/NFB). In the second, ethylene promotes the cleavage of EIN2, with the product of the cleavage inhibiting growth and promoting the production of EIN2 through a positive feedback loop (PFB). Since few network parameters for ethylene signaling are known in detail, we used an evolutionary algorithm to explore sets of parameters that produce behaviors similar to experimental growth response kinetics of both wildtype and mutant seedlings. We generated a library of parameter sets by independently running the evolutionary algorithm many times. Both network topologies produce behavior consistent with experimental observations, and analysis of the parameter sets allows us to identify important network interactions and parameter constraints. We additionally screened these parameter sets for growth recovery in the presence of sub-saturating ethylene doses, which is an experimentally-observed property that emerges in some of the evolved parameter sets. Finally, we probed simplified networks maintaining key features of the CFF/NFB and PFB topologies. From this, we verified observations drawn from the larger networks about mechanisms underlying ethylene signaling. Analysis of each network topology results in predictions about changes that occur in network components that can be experimentally tested to give insights into which, if either, network underlies ethylene responses. PMID:27625669

Symmetric Topological Phases and Tensor Network States

NASA Astrophysics Data System (ADS)

Jiang, Shenghan

Classification and simulation of quantum phases are one of main themes in condensed matter physics. Quantum phases can be distinguished by their symmetrical and topological properties. The interplay between symmetry and topology in condensed matter physics often leads to exotic quantum phases and rich phase diagrams. Famous examples include quantum Hall phases, spin liquids and topological insulators. In this thesis, I present our works toward a more systematically understanding of symmetric topological quantum phases in bosonic systems. In the absence of global symmetries, gapped quantum phases are characterized by topological orders. Topological orders in 2+1D are well studied, while a systematically understanding of topological orders in 3+1D is still lacking. By studying a family of exact solvable models, we find at least some topological orders in 3+1D can be distinguished by braiding phases of loop excitations. In the presence of both global symmetries and topological orders, the interplay between them leads to new phases termed as symmetry enriched topological (SET) phases. We develop a framework to classify a large class of SET phases using tensor networks. For each tensor class, we can write down generic variational wavefunctions. We apply our method to study gapped spin liquids on the kagome lattice, which can be viewed as SET phases of on-site symmetries as well as lattice symmetries. In the absence of topological order, symmetry could protect different topological phases, which are often referred to as symmetry protected topological (SPT) phases. We present systematic constructions of tensor network wavefunctions for bosonic symmetry protected topological (SPT) phases respecting both onsite and spatial symmetries.

Empirical study of the role of the topology in spreading on communication networks

NASA Astrophysics Data System (ADS)

Medvedev, Alexey; Kertesz, Janos

2017-03-01

Topological aspects, like community structure, and temporal activity patterns, like burstiness, have been shown to severely influence the speed of spreading in temporal networks. We study the influence of the topology on the susceptible-infected (SI) spreading on time stamped communication networks, as obtained from a dataset of mobile phone records. We consider city level networks with intra- and inter-city connections. The networks using only intra-city links are usually sparse, where the spreading depends mainly on the average degree. The inter-city links serve as bridges in spreading, speeding up considerably the process. We demonstrate the effect also on model simulations.

Topological Distances Between Brain Networks

PubMed Central

Lee, Hyekyoung; Solo, Victor; Davidson, Richard J.; Pollak, Seth D.

2018-01-01

Many existing brain network distances are based on matrix norms. The element-wise differences may fail to capture underlying topological differences. Further, matrix norms are sensitive to outliers. A few extreme edge weights may severely affect the distance. Thus it is necessary to develop network distances that recognize topology. In this paper, we introduce Gromov-Hausdorff (GH) and Kolmogorov-Smirnov (KS) distances. GH-distance is often used in persistent homology based brain network models. The superior performance of KS-distance is contrasted against matrix norms and GH-distance in random network simulations with the ground truths. The KS-distance is then applied in characterizing the multimodal MRI and DTI study of maltreated children.

Evolution versus "intelligent design": comparing the topology of protein-protein interaction networks to the Internet.

PubMed

Yang, Q; Siganos, G; Faloutsos, M; Lonardi, S

2006-01-01

Recent research efforts have made available genome-wide, high-throughput protein-protein interaction (PPI) maps for several model organisms. This has enabled the systematic analysis of PPI networks, which has become one of the primary challenges for the system biology community. In this study, we attempt to understand better the topological structure of PPI networks by comparing them against man-made communication networks, and more specifically, the Internet. Our comparative study is based on a comprehensive set of graph metrics. Our results exhibit an interesting dichotomy. On the one hand, both networks share several macroscopic properties such as scale-free and small-world properties. On the other hand, the two networks exhibit significant topological differences, such as the cliqueishness of the highest degree nodes. We attribute these differences to the distinct design principles and constraints that both networks are assumed to satisfy. We speculate that the evolutionary constraints that favor the survivability and diversification are behind the building process of PPI networks, whereas the leading force in shaping the Internet topology is a decentralized optimization process geared towards efficient node communication.

Collective relaxation dynamics of small-world networks

NASA Astrophysics Data System (ADS)

Grabow, Carsten; Grosskinsky, Stefan; Kurths, Jürgen; Timme, Marc

2015-05-01

Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian, or a similar linear operator. The structure of networks with regular, small-world, and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N , average degree k , and topological randomness q . We present simplified analytic predictions for the second-largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small, and moderate topological randomness q , including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory, thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.

Collective relaxation dynamics of small-world networks.

PubMed

Grabow, Carsten; Grosskinsky, Stefan; Kurths, Jürgen; Timme, Marc

2015-05-01

Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian, or a similar linear operator. The structure of networks with regular, small-world, and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N, average degree k, and topological randomness q. We present simplified analytic predictions for the second-largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small, and moderate topological randomness q, including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory, thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.

Spatial-temporal modeling of malware propagation in networks.

PubMed

Chen, Zesheng; Ji, Chuanyi

2005-09-01

Network security is an important task of network management. One threat to network security is malware (malicious software) propagation. One type of malware is called topological scanning that spreads based on topology information. The focus of this work is on modeling the spread of topological malwares, which is important for understanding their potential damages, and for developing countermeasures to protect the network infrastructure. Our model is motivated by probabilistic graphs, which have been widely investigated in machine learning. We first use a graphical representation to abstract the propagation of malwares that employ different scanning methods. We then use a spatial-temporal random process to describe the statistical dependence of malware propagation in arbitrary topologies. As the spatial dependence is particularly difficult to characterize, the problem becomes how to use simple (i.e., biased) models to approximate the spatially dependent process. In particular, we propose the independent model and the Markov model as simple approximations. We conduct both theoretical analysis and extensive simulations on large networks using both real measurements and synthesized topologies to test the performance of the proposed models. Our results show that the independent model can capture temporal dependence and detailed topology information and, thus, outperforms the previous models, whereas the Markov model incorporates a certain spatial dependence and, thus, achieves a greater accuracy in characterizing both transient and equilibrium behaviors of malware propagation.

Using Hybrid Angle/Distance Information for Distributed Topology Control in Vehicular Sensor Networks

PubMed Central

Huang, Chao-Chi; Chiu, Yang-Hung; Wen, Chih-Yu

2014-01-01

In a vehicular sensor network (VSN), the key design issue is how to organize vehicles effectively, such that the local network topology can be stabilized quickly. In this work, each vehicle with on-board sensors can be considered as a local controller associated with a group of communication members. In order to balance the load among the nodes and govern the local topology change, a group formation scheme using localized criteria is implemented. The proposed distributed topology control method focuses on reducing the rate of group member change and avoiding the unnecessary information exchange. Two major phases are sequentially applied to choose the group members of each vehicle using hybrid angle/distance information. The operation of Phase I is based on the concept of the cone-based method, which can select the desired vehicles quickly. Afterwards, the proposed time-slot method is further applied to stabilize the network topology. Given the network structure in Phase I, a routing scheme is presented in Phase II. The network behaviors are explored through simulation and analysis in a variety of scenarios. The results show that the proposed mechanism is a scalable and effective control framework for VSNs. PMID:25350506

Gene network interconnectedness and the generalized topological overlap measure

PubMed Central

Yip, Andy M; Horvath, Steve

2007-01-01

Background Network methods are increasingly used to represent the interactions of genes and/or proteins. Genes or proteins that are directly linked may have a similar biological function or may be part of the same biological pathway. Since the information on the connection (adjacency) between 2 nodes may be noisy or incomplete, it can be desirable to consider alternative measures of pairwise interconnectedness. Here we study a class of measures that are proportional to the number of neighbors that a pair of nodes share in common. For example, the topological overlap measure by Ravasz et al. [1] can be interpreted as a measure of agreement between the m = 1 step neighborhoods of 2 nodes. Several studies have shown that two proteins having a higher topological overlap are more likely to belong to the same functional class than proteins having a lower topological overlap. Here we address the question whether a measure of topological overlap based on higher-order neighborhoods could give rise to a more robust and sensitive measure of interconnectedness. Results We generalize the topological overlap measure from m = 1 step neighborhoods to m ≥ 2 step neighborhoods. This allows us to define the m-th order generalized topological overlap measure (GTOM) by (i) counting the number of m-step neighbors that a pair of nodes share and (ii) normalizing it to take a value between 0 and 1. Using theoretical arguments, a yeast co-expression network application, and a fly protein network application, we illustrate the usefulness of the proposed measure for module detection and gene neighborhood analysis. Conclusion Topological overlap can serve as an important filter to counter the effects of spurious or missing connections between network nodes. The m-th order topological overlap measure allows one to trade-off sensitivity versus specificity when it comes to defining pairwise interconnectedness and network modules. PMID:17250769

Topological relationships between brain and social networks.

PubMed

Sakata, Shuzo; Yamamori, Tetsuo

2007-01-01

Brains are complex networks. Previously, we revealed that specific connected structures are either significantly abundant or rare in cortical networks. However, it remains unknown whether systems from other disciplines have similar architectures to brains. By applying network-theoretical methods, here we show topological similarities between brain and social networks. We found that the statistical relevance of specific tied structures differs between social "friendship" and "disliking" networks, suggesting relation-type-specific topology of social networks. Surprisingly, overrepresented connected structures in brain networks are more similar to those in the friendship networks than to those in other networks. We found that balanced and imbalanced reciprocal connections between nodes are significantly abundant and rare, respectively, whereas these results are unpredictable by simply counting mutual connections. We interpret these results as evidence of positive selection of balanced mutuality between nodes. These results also imply the existence of underlying common principles behind the organization of brain and social networks.

Robust spatial memory maps in flickering neuronal networks: a topological model

NASA Astrophysics Data System (ADS)

Dabaghian, Yuri; Babichev, Andrey; Memoli, Facundo; Chowdhury, Samir; Rice University Collaboration; Ohio State University Collaboration

It is widely accepted that the hippocampal place cells provide a substrate of the neuronal representation of the environment--the ``cognitive map''. However, hippocampal network, as any other network in the brain is transient: thousands of hippocampal neurons die every day and the connections formed by these cells constantly change due to various forms of synaptic plasticity. What then explains the remarkable reliability of our spatial memories? We propose a computational approach to answering this question based on a couple of insights. First, we propose that the hippocampal cognitive map is fundamentally topological, and hence it is amenable to analysis by topological methods. We then apply several novel methods from homology theory, to understand how dynamic connections between cells influences the speed and reliability of spatial learning. We simulate the rat's exploratory movements through different environments and study how topological invariants of these environments arise in a network of simulated neurons with ``flickering'' connectivity. We find that despite transient connectivity the network of place cells produces a stable representation of the topology of the environment.

Self-organization in multilayer network with adaptation mechanisms based on competition

NASA Astrophysics Data System (ADS)

Pitsik, Elena N.; Makarov, Vladimir V.; Nedaivozov, Vladimir O.; Kirsanov, Daniil V.; Goremyko, Mikhail V.

2018-04-01

The paper considers the phenomena of competition in multiplex network whose structure evolves corresponding to dynamics of it's elements, forming closed loop of self-learning with the aim to reach the optimal topology. Numerical analysis of proposed model shows that it is possible to obtain scale-invariant structures for corresponding parameters as well as the structures with homogeneous distribution of connections in the layers. Revealed phenomena emerges as the consequence of the self-organization processes related to structure-dynamical selflearning based on homeostasis and homophily, as well as the result of the competition between the network's layers for optimal topology. It was shown that in the mode of partial and cluster synchronization the network reaches scale-free topology of complex nature that is different from layer to layer. However, in the mode of global synchronization the homogeneous topologies on all layer of the network are observed. This phenomenon is tightly connected with the competitive processes that represent themselves as the natural mechanism of reaching the optimal topology of the links in variety of real-world systems.

Co-location and Self-Similar Topologies of Urban Infrastructure Networks

NASA Astrophysics Data System (ADS)

Klinkhamer, Christopher; Zhan, Xianyuan; Ukkusuri, Satish; Elisabeth, Krueger; Paik, Kyungrock; Rao, Suresh

2016-04-01

The co-location of urban infrastructure is too obvious to be easily ignored. For reasons of practicality, reliability, and eminent domain, the spatial locations of many urban infrastructure networks, including drainage, sanitary sewers, and road networks, are well correlated. However, important questions dealing with correlations in the network topologies of differing infrastructure types remain unanswered. Here, we have extracted randomly distributed, nested subnets from the urban drainage, sanitary sewer, and road networks in two distinctly different cities: Amman, Jordan; and Indianapolis, USA. Network analyses were performed for each randomly chosen subnet (location and size), using a dual-mapping approach (Hierarchical Intersection Continuity Negotiation). Topological metrics for each infrastructure type were calculated and compared for all subnets in a given city. Despite large differences in the climate, governance, and populace of the two cities, and functional properties of the different infrastructure types, these infrastructure networks are shown to be highly spatially homogenous. Furthermore, strong correlations are found between topological metrics of differing types of surface and subsurface infrastructure networks. Also, the network topologies of each infrastructure type for both cities are shown to exhibit self-similar characteristics (i.e., power law node-degree distributions, [p(k) = ak-γ]. These findings can be used to assist city planners and engineers either expanding or retrofitting existing infrastructure, or in the case of developing countries, building new cities from the ground up. In addition, the self-similar nature of these infrastructure networks holds significant implications for the vulnerability of these critical infrastructure networks to external hazards and ways in which network resilience can be improved.

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.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Consensus of Multi-Agent Systems with Prestissimo Scale-Free Networks

NASA Astrophysics Data System (ADS)

Yang, Hong-Yong; Lu, Lan; Cao, Ke-Cai; Zhang, Si-Ying

2010-04-01

In this paper, the relations of the network topology and the moving consensus of multi-agent systems are studied. A consensus-prestissimo scale-free network model with the static preferential-consensus attachment is presented on the rewired link of the regular network. The effects of the static preferential-consensus BA network on the algebraic connectivity of the topology graph are compared with the regular network. The robustness gain to delay is analyzed for variable network topology with the same scale. The time to reach the consensus is studied for the dynamic network with and without communication delays. By applying the computer simulations, it is validated that the speed of the convergence of multi-agent systems can be greatly improved in the preferential-consensus BA network model with different configuration.

Different alterations in brain functional networks according to direct and indirect topological connections in patients with schizophrenia.

PubMed

Park, Chang-Hyun; Lee, Seungyup; Kim, Taewon; Won, Wang Yeon; Lee, Kyoung-Uk

2017-10-01

Schizophrenia displays connectivity deficits in the brain, but the literature has shown inconsistent findings about alterations in global efficiency of brain functional networks. We supposed that such inconsistency at the whole brain level may be due to a mixture of different portions of global efficiency at sub-brain levels. Accordingly, we considered measuring portions of global efficiency in two aspects: spatial portions by considering sub-brain networks and topological portions by considering contributions to global efficiency according to direct and indirect topological connections. We proposed adjacency and indirect adjacency as new network parameters attributable to direct and indirect topological connections, respectively, and applied them to graph-theoretical analysis of brain functional networks constructed from resting state fMRI data of 22 patients with schizophrenia and 22 healthy controls. Group differences in the network parameters were observed not for whole brain and hemispheric networks, but for regional networks. Alterations in adjacency and indirect adjacency were in opposite directions, such that adjacency increased, but indirect adjacency decreased in patients with schizophrenia. Furthermore, over connections in frontal and parietal regions, increased adjacency was associated with more severe negative symptoms, while decreased adjacency was associated with more severe positive symptoms of schizophrenia. This finding indicates that connectivity deficits associated with positive and negative symptoms of schizophrenia may involve topologically different paths in the brain. In patients with schizophrenia, although changes in global efficiency may not be clearly shown, different alterations in brain functional networks according to direct and indirect topological connections could be revealed at the regional level. Copyright © 2017 Elsevier B.V. All rights reserved.

Is the Internet gaming-addicted brain close to be in a pathological state?

PubMed

Park, Chang-Hyun; Chun, Ji-Won; Cho, Huyn; Jung, Young-Chul; Choi, Jihye; Kim, Dai Jin

2017-01-01

Internet gaming addiction (IGA) is becoming a common and widespread mental health concern. Although IGA induces a variety of negative psychosocial consequences, it is yet ambiguous whether the brain addicted to Internet gaming is considered to be in a pathological state. We investigated IGA-induced abnormalities of the brain specifically from the network perspective and qualitatively assessed whether the Internet gaming-addicted brain is in a state similar to the pathological brain. Topological properties of brain functional networks were examined by applying a graph-theoretical approach to analyzing functional magnetic resonance imaging data acquired during a resting state in 19 IGA adolescents and 20 age-matched healthy controls. We compared functional distance-based measures, global and local efficiency of resting state brain functional networks between the two groups to assess how the IGA subjects' brain was topologically altered from the controls' brain. The IGA subjects had severer impulsiveness and their brain functional networks showed higher global efficiency and lower local efficiency relative to the controls. These topological differences suggest that IGA induced brain functional networks to shift toward the random topological architecture, as exhibited in other pathological states. Furthermore, for the IGA subjects, the topological alterations were specifically attributable to interregional connections incident on the frontal region, and the degree of impulsiveness was associated with the topological alterations over the frontolimbic connections. The current findings lend support to the proposition that the Internet gaming-addicted brain could be in the state similar to pathological states in terms of topological characteristics of brain functional networks. © 2015 Society for the Study of Addiction.

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

A Geographic and Functional Network Flow Analysis Tool

DTIC Science & Technology

2014-06-01

a context for the games to be run. By definition, a dystopia is “a place where bad things happen”—a fitting name for a place where the scenarios are...design identified by Topology Zoo and the BRITE Topology generator (Byers et al. 2014; Bowden 2013). Both projects aim to accurately map the network...Knight, Simon, Nguyen, Hung, Falkner, Nickolas, and Matthew Roughan. 2014. “The Internet Topology Zoo .” The Internet Topology Zoo . Accessed March

Topology design and performance analysis of an integrated communication network

NASA Technical Reports Server (NTRS)

Li, V. O. K.; Lam, Y. F.; Hou, T. C.; Yuen, J. H.

1985-01-01

A research study on the topology design and performance analysis for the Space Station Information System (SSIS) network is conducted. It is begun with a survey of existing research efforts in network topology design. Then a new approach for topology design is presented. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. The algorithm for generating subsets is described in detail, and various aspects of the overall design procedure are discussed. Two more efficient versions of this algorithm (applicable in specific situations) are also given. Next, two important aspects of network performance analysis: network reliability and message delays are discussed. A new model is introduced to study the reliability of a network with dependent failures. For message delays, a collection of formulas from existing research results is given to compute or estimate the delays of messages in a communication network without making the independence assumption. The design algorithm coded in PASCAL is included as an appendix.

From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks

PubMed Central

Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

2013-01-01

Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial. PMID:23563395

From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

PubMed

Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

2013-01-01

Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.

Geometry of complex networks and topological centrality

NASA Astrophysics Data System (ADS)

Ranjan, Gyan; Zhang, Zhi-Li

2013-09-01

We explore the geometry of complex networks in terms of an n-dimensional Euclidean embedding represented by the Moore-Penrose pseudo-inverse of the graph Laplacian (L). The squared distance of a node i to the origin in this n-dimensional space (lii+), yields a topological centrality index, defined as C∗(i)=1/lii+. In turn, the sum of reciprocals of individual node centralities, ∑i1/C∗(i)=∑ilii+, or the trace of L, yields the well-known Kirchhoff index (K), an overall structural descriptor for the network. To put into context this geometric definition of centrality, we provide alternative interpretations of the proposed indices that connect them to meaningful topological characteristics - first, as forced detour overheads and frequency of recurrences in random walks that has an interesting analogy to voltage distributions in the equivalent electrical network; and then as the average connectedness of i in all the bi-partitions of the graph. These interpretations respectively help establish the topological centrality (C∗(i)) of node i as a measure of its overall position as well as its overall connectedness in the network; thus reflecting the robustness of i to random multiple edge failures. Through empirical evaluations using synthetic and real world networks, we demonstrate how the topological centrality is better able to distinguish nodes in terms of their structural roles in the network and, along with Kirchhoff index, is appropriately sensitive to perturbations/re-wirings in the network.

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.

Reconfigurable routing protocol for free space optical sensor networks.

PubMed

Xie, Rong; Yang, Won-Hyuk; Kim, Young-Chon

2012-01-01

Recently, free space optical sensor networks (FSOSNs), which are based on free space optics (FSO) instead of radio frequency (RF), have gained increasing visibility over traditional wireless sensor networks (WSNs) due to their advantages such as larger capacity, higher security, and lower cost. However, the performance of FSOSNs is restricted to the requirement of a direct line-of-sight (LOS) path between a sender and a receiver pair. Once a node dies of energy depletion, the network would probably suffer from a dramatic decrease of connectivity, resulting in a huge loss of data packets. Thus, this paper proposes a reconfigurable routing protocol (RRP) to overcome this problem by dynamically reconfiguring the network virtual topology. The RRP works in three phases: (1) virtual topology construction, (2) routing establishment, and (3) reconfigurable routing. When data transmission begins, the data packets are first routed through the shortest hop paths. Then a reconfiguration is initiated by the node whose residual energy falls below a threshold. Nodes affected by this dying node are classified into two types, namely maintenance nodes and adjustment nodes, and they are reconfigured according to the types. An energy model is designed to evaluate the performance of RRP through OPNET simulation. Our simulation results indicate that the RRP achieves better performance compared with the simple-link protocol and a direct reconfiguration scheme in terms of connectivity, network lifetime, packet delivery ratio and the number of living nodes.

Control of polymer network topology in semi-batch systems

NASA Astrophysics Data System (ADS)

Wang, Rui; Olsen, Bradley; Johnson, Jeremiah

Polymer networks invariably possess topological defects: loops of different orders. Since small loops (primary loops and secondary loops) both lower the modulus of network and lead to stress concentration that causes material failure at low deformation, it is desirable to greatly reduce the loop fraction. We have shown that achieving loop fraction close to zero is extremely difficult in the batch process due to the slow decay of loop fraction with the polymer concentration and chain length. Here, we develop a modified kinetic graph theory that can model network formation reactions in semi-batch systems. We demonstrate that the loop fraction is not sensitive to the feeding policy if the reaction volume maintains constant during the network formation. However, if we initially put concentrated solution of small junction molecules in the reactor and continuously adding polymer solutions, the fractions of both primary loop and higher-order loops will be significantly reduced. There is a limiting value (nonzero) of loop fraction that can be achieved in the semi-batch system in condition of extremely slow feeding rate. This minimum loop fraction only depends on a single dimensionless variable, the product of concentration and with single chain pervaded volume, and defines an operating zone in which the loop fraction of polymer networks can be controlled through adjusting the feeding rate of the semi-batch process.

Weak signal transmission in complex networks and its application in detecting connectivity.

PubMed

Liang, Xiaoming; Liu, Zonghua; Li, Baowen

2009-10-01

We present a network model of coupled oscillators to study how a weak signal is transmitted in complex networks. Through both theoretical analysis and numerical simulations, we find that the response of other nodes to the weak signal decays exponentially with their topological distance to the signal source and the coupling strength between two neighboring nodes can be figured out by the responses. This finding can be conveniently used to detect the topology of unknown network, such as the degree distribution, clustering coefficient and community structure, etc., by repeatedly choosing different nodes as the signal source. Through four typical networks, i.e., the regular one dimensional, small world, random, and scale-free networks, we show that the features of network can be approximately given by investigating many fewer nodes than the network size, thus our approach to detect the topology of unknown network may be efficient in practical situations with large network size.

Topological Edge Floppy Modes in Disordered Fiber Networks

NASA Astrophysics Data System (ADS)

Zhou, Di; Zhang, Leyou; Mao, Xiaoming

2018-02-01

Disordered fiber networks are ubiquitous in a broad range of natural (e.g., cytoskeleton) and manmade (e.g., aerogels) materials. In this Letter, we discuss the emergence of topological floppy edge modes in two-dimensional fiber networks as a result of deformation or active driving. It is known that a network of straight fibers exhibits bulk floppy modes which only bend the fibers without stretching them. We find that, interestingly, with a perturbation in geometry, these bulk modes evolve into edge modes. We introduce a topological index for these edge modes and discuss their implications in biology.

Analysis of security and threat of underwater wireless sensor network topology

NASA Astrophysics Data System (ADS)

Yang, Guang; Wei, Zhiqiang; Cong, Yanping; Jia, Dongning

2012-04-01

Underwater wireless sensor networks (UWSNs) are a subclass of wireless sensor networks. Underwater sensor deployment is a significant challenge due to the characteristics of UWSNs and underwater environment. Recent researches for UWSNs deployment mostly focus on the maintenance of network connectivity and maximum communication coverage. However, the broadcast nature of the transmission medium incurs various types of security attacks. This paper studies the security issues and threats of UWSNs topology. Based on the cluster-based topology, an underwater cluster-based security scheme (U-CBSS) is presented to defend against these attacks. and safety.

General Dynamics of Topology and Traffic on Weighted Technological Networks

NASA Astrophysics Data System (ADS)

Wang, Wen-Xu; Wang, Bing-Hong; Hu, Bo; Yan, Gang; Ou, Qing

2005-05-01

For most technical networks, the interplay of dynamics, traffic, and topology is assumed crucial to their evolution. In this Letter, we propose a traffic-driven evolution model of weighted technological networks. By introducing a general strength-coupling mechanism under which the traffic and topology mutually interact, the model gives power-law distributions of degree, weight, and strength, as confirmed in many real networks. Particularly, depending on a parameter W that controls the total weight growth of the system, the nontrivial clustering coefficient C, degree assortativity coefficient r, and degree-strength correlation are all consistent with empirical evidence.

Topology-optimized metasurfaces: impact of initial geometric layout.

PubMed

Yang, Jianji; Fan, Jonathan A

2017-08-15

Topology optimization is a powerful iterative inverse design technique in metasurface engineering and can transform an initial layout into a high-performance device. With this method, devices are optimized within a local design phase space, making the identification of suitable initial geometries essential. In this Letter, we examine the impact of initial geometric layout on the performance of large-angle (75 deg) topology-optimized metagrating deflectors. We find that when conventional metasurface designs based on dielectric nanoposts are used as initial layouts for topology optimization, the final devices have efficiencies around 65%. In contrast, when random initial layouts are used, the final devices have ultra-high efficiencies that can reach 94%. Our numerical experiments suggest that device topologies based on conventional metasurface designs may not be suitable to produce ultra-high-efficiency, large-angle metasurfaces. Rather, initial geometric layouts with non-trivial topologies and shapes are required.

Cascading failures in interconnected networks with dynamical redistribution of loads

NASA Astrophysics Data System (ADS)

Zhao, Zhuang; Zhang, Peng; Yang, Hujiang

2015-09-01

Cascading failures of loads in isolated networks and coupled networks have been studied in the past few years. In most of the corresponding results, the topologies of the networks are destroyed. Here, we present an interconnected network model considering cascading failures based on the dynamic redistribution of flow in the networks. Compared with the results of single scale-free networks, we find that interconnected scale-free networks have higher vulnerability. Additionally, the network heterogeneity plays an important role in the robustness of interconnected networks under intentional attacks. Considering the effects of various coupling preferences, the results show that there are almost no differences. Finally, the application of our model to the Beijing interconnected traffic network, which consists of a subway network and a bus network, shows that the subway network suffers more damage under the attack. Moreover, the interconnected traffic network may be more exposed to damage after initial attacks on the bus network. These discussions are important for the design and optimization of interconnected networks.

Integrative topological analysis of mass spectrometry data reveals molecular features with clinical relevance in esophageal squamous cell carcinoma

PubMed Central

Gao, She-Gan; Liu, Rui-Min; Zhao, Yun-Gang; Wang, Pei; Ward, Douglas G.; Wang, Guang-Chao; Guo, Xiang-Qian; Gu, Juan; Niu, Wan-Bin; Zhang, Tian; Martin, Ashley; Guo, Zhi-Peng; Feng, Xiao-Shan; Qi, Yi-Jun; Ma, Yuan-Fang

2016-01-01

Combining MS-based proteomic data with network and topological features of such network would identify more clinically relevant molecules and meaningfully expand the repertoire of proteins derived from MS analysis. The integrative topological indexes representing 95.96% information of seven individual topological measures of node proteins were calculated within a protein-protein interaction (PPI) network, built using 244 differentially expressed proteins (DEPs) identified by iTRAQ 2D-LC-MS/MS. Compared with DEPs, differentially expressed genes (DEGs) and comprehensive features (CFs), structurally dominant nodes (SDNs) based on integrative topological index distribution produced comparable classification performance in three different clinical settings using five independent gene expression data sets. The signature molecules of SDN-based classifier for distinction of early from late clinical TNM stages were enriched in biological traits of protein synthesis, intracellular localization and ribosome biogenesis, which suggests that ribosome biogenesis represents a promising therapeutic target for treating ESCC. In addition, ITGB1 expression selected exclusively by integrative topological measures correlated with clinical stages and prognosis, which was further validated with two independent cohorts of ESCC samples. Thus the integrative topological analysis of PPI networks proposed in this study provides an alternative approach to identify potential biomarkers and therapeutic targets from MS/MS data with functional insights in ESCC. PMID:26898710

Autonomous distributed self-organization for mobile wireless sensor networks.

PubMed

Wen, Chih-Yu; Tang, Hung-Kai

2009-01-01

This paper presents an adaptive combined-metrics-based clustering scheme for mobile wireless sensor networks, which manages the mobile sensors by utilizing the hierarchical network structure and allocates network resources efficiently A local criteria is used to help mobile sensors form a new cluster or join a current cluster. The messages transmitted during hierarchical clustering are applied to choose distributed gateways such that communication for adjacent clusters and distributed topology control can be achieved. In order to balance the load among clusters and govern the topology change, a cluster reformation scheme using localized criterions is implemented. The proposed scheme is simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithm provides efficient network topology management and achieves high scalability in mobile sensor networks.

Cooperation-Induced Topological Complexity: A Promising Road to Fault Tolerance and Hebbian Learning

DTIC Science & Technology

2012-03-16

topological complexity a way to compare the efficiency of a scale-free network to the random network of Erdos and Renyi . All this is extensively dis- cussed in...an excellent review paper byArenas et al. (2008) showing very interesting comparisons of Erdos– Renyi networks and scale- free networks as a function

Identifying partial topology of complex dynamical networks via a pinning mechanism

NASA Astrophysics Data System (ADS)

Zhu, Shuaibing; Zhou, Jin; Lu, Jun-an

2018-04-01

In this paper, we study the problem of identifying the partial topology of complex dynamical networks via a pinning mechanism. By using the network synchronization theory and the adaptive feedback controlling method, we propose a method which can greatly reduce the number of nodes and observers in the response network. Particularly, this method can also identify the whole topology of complex networks. A theorem is established rigorously, from which some corollaries are also derived in order to make our method more cost-effective. Several numerical examples are provided to verify the effectiveness of the proposed method. In the simulation, an approach is also given to avoid possible identification failure caused by inner synchronization of the drive network.

Analysis of stationary availability factor of two-level backbone computer networks with arbitrary topology

NASA Astrophysics Data System (ADS)

Rahman, P. A.

2018-05-01

This scientific paper deals with the two-level backbone computer networks with arbitrary topology. A specialized method, offered by the author for calculation of the stationary availability factor of the two-level backbone computer networks, based on the Markov reliability models for the set of the independent repairable elements with the given failure and repair rates and the methods of the discrete mathematics, is also discussed. A specialized algorithm, offered by the author for analysis of the network connectivity, taking into account different kinds of the network equipment failures, is also observed. Finally, this paper presents an example of calculation of the stationary availability factor for the backbone computer network with the given topology.

Concept Model on Topological Learning

NASA Astrophysics Data System (ADS)

Ae, Tadashi; Kioi, Kazumasa

2010-11-01

We discuss a new model for concept based on topological learning, where the learning process on the neural network is represented by mathematical topology. The topological learning of neural networks is summarized by a quotient of input space and the hierarchical step induces a tree where each node corresponds to a quotient. In general, the concept acquisition is a difficult problem, but the emotion for a subject is represented by providing the questions to a person. Therefore, a kind of concept is captured by such data and the answer sheet can be mapped into a topology consisting of trees. In this paper, we will discuss a way of mapping the emotional concept to a topological learning model.

Fibril growth kinetics link buffer conditions and topology of 3D collagen I networks.

PubMed

Kalbitzer, Liv; Pompe, Tilo

2018-02-01

Three-dimensional fibrillar networks reconstituted from collagen I are widely used as biomimetic scaffolds for in vitro and in vivo cell studies. Various physicochemical parameters of buffer conditions for in vitro fibril formation are well known, including pH-value, ion concentrations and temperature. However, there is a lack of a detailed understanding of reconstituting well-defined 3D network topologies, which is required to mimic specific properties of the native extracellular matrix. We screened a wide range of relevant physicochemical buffer conditions and characterized the topology of the reconstituted 3D networks in terms of mean pore size and fibril diameter. A congruent analysis of fibril formation kinetics by turbidimetry revealed the adjustment of the lateral growth phase of fibrils by buffer conditions to be key in the determination of pore size and fibril diameter of the networks. Although the kinetics of nucleation and linear growth phase were affected by buffer conditions as well, network topology was independent of those two growth phases. Overall, the results of our study provide necessary insights into how to engineer 3D collagen matrices with an independent control over topology parameters, in order to mimic in vivo tissues in in vitro experiments and tissue engineering applications. The study reports a comprehensive analysis of physicochemical conditions of buffer solutions to reconstitute defined 3D collagen I matrices. By a combined analysis of network topology, i.e., pore size and fibril diameter, and the kinetics of fibril formation we can reveal the dependence of 3D network topology on buffer conditions, such as pH-value, phosphate concentration and sodium chloride content. With those results we are now able to provide engineering strategies to independently tune the topology parameters of widely used 3D collagen scaffolds based on the buffer conditions. By that, we enable the straightforward mimicking of extracellular matrices of in vivo tissues for in vitro cell culture experiments and tissue engineering applications. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Multifractal analysis and topological properties of a new family of weighted Koch networks

NASA Astrophysics Data System (ADS)

Huang, Da-Wen; Yu, Zu-Guo; Anh, Vo

2017-03-01

Weighted complex networks, especially scale-free networks, which characterize real-life systems better than non-weighted networks, have attracted considerable interest in recent years. Studies on the multifractality of weighted complex networks are still to be undertaken. In this paper, inspired by the concepts of Koch networks and Koch island, we propose a new family of weighted Koch networks, and investigate their multifractal behavior and topological properties. We find some key topological properties of the new networks: their vertex cumulative strength has a power-law distribution; there is a power-law relationship between their topological degree and weight strength; the networks have a high weighted clustering coefficient of 0.41004 (which is independent of the scaling factor c) in the limit of large generation t; the second smallest eigenvalue μ2 and the maximum eigenvalue μn are approximated by quartic polynomials of the scaling factor c for the general Laplacian operator, while μ2 is approximately a quartic polynomial of c and μn= 1.5 for the normalized Laplacian operator. Then, we find that weighted koch networks are both fractal and multifractal, their fractal dimension is influenced by the scaling factor c. We also apply these analyses to six real-world networks, and find that the multifractality in three of them are strong.

Brain anatomical networks in early human brain development.

PubMed

Fan, Yong; Shi, Feng; Smith, Jeffrey Keith; Lin, Weili; Gilmore, John H; Shen, Dinggang

2011-02-01

Recent neuroimaging studies have demonstrated that human brain networks have economic small-world topology and modular organization, enabling efficient information transfer among brain regions. However, it remains largely unknown how the small-world topology and modular organization of human brain networks emerge and develop. Using longitudinal MRI data of 28 healthy pediatric subjects, collected at their ages of 1 month, 1 year, and 2 years, we analyzed development patterns of brain anatomical networks derived from morphological correlations of brain regional volumes. The results show that the brain network of 1-month-olds has the characteristically economic small-world topology and nonrandom modular organization. The network's cost efficiency increases with the brain development to 1 year and 2 years, so does the modularity, providing supportive evidence for the hypothesis that the small-world topology and the modular organization of brain networks are established during early brain development to support rapid synchronization and information transfer with minimal rewiring cost, as well as to balance between local processing and global integration of information. Copyright © 2010. Published by Elsevier Inc.

Enhanced collective influence: A paradigm to optimize network disruption

NASA Astrophysics Data System (ADS)

Wu, Tao; Chen, Leiting; Zhong, Linfeng; Xian, Xingping

2017-04-01

The function of complex networks typically relies on the integrity of underlying structure. Sometimes, practical applications need to attack networks' function, namely inactivate and fragment networks' underlying structure. To effectively dismantle complex networks and regulate the function of them, a centrality measure, named CI (Morone and Makse, 2015), was proposed for node ranking. We observe that the performance of CI centrality in network disruption problem may deteriorate when it is used in networks with different topology properties. Specifically, the structural features of local network topology are overlooked in CI centrality, even though the local network topology of the nodes with a fixed CI value may have very different organization. To improve the ranking accuracy of CI, this paper proposes a variant ECI to CI by considering loop density and degree diversity of local network topology. And the proposed ECI centrality would degenerate into CI centrality with the reduction of the loop density and the degree diversity level. By comparing ECI with CI and classical centrality measures in both synthetic and real networks, the experimental results suggest that ECI can largely improve the performance of CI for network disruption. Based on the results, we analyze the correlation between the improvement and the properties of the networks. We find that the performance of ECI is positively correlated with assortative coefficient and community modularity and negatively correlated with degree inequality of networks, which can be used as guidance for practical applications.

Optimal topologies for maximizing network transmission capacity

NASA Astrophysics Data System (ADS)

Chen, Zhenhao; Wu, Jiajing; Rong, Zhihai; Tse, Chi K.

2018-04-01

It has been widely demonstrated that the structure of a network is a major factor that affects its traffic dynamics. In this work, we try to identify the optimal topologies for maximizing the network transmission capacity, as well as to build a clear relationship between structural features of a network and the transmission performance in terms of traffic delivery. We propose an approach for designing optimal network topologies against traffic congestion by link rewiring and apply them on the Barabási-Albert scale-free, static scale-free and Internet Autonomous System-level networks. Furthermore, we analyze the optimized networks using complex network parameters that characterize the structure of networks, and our simulation results suggest that an optimal network for traffic transmission is more likely to have a core-periphery structure. However, assortative mixing and the rich-club phenomenon may have negative impacts on network performance. Based on the observations of the optimized networks, we propose an efficient method to improve the transmission capacity of large-scale networks.

Stability and Topology of Scale-Free Networks under Attack and Defense Strategies

NASA Astrophysics Data System (ADS)

Gallos, Lazaros K.; Cohen, Reuven; Argyrakis, Panos; Bunde, Armin; Havlin, Shlomo

2005-05-01

We study tolerance and topology of random scale-free networks under attack and defense strategies that depend on the degree k of the nodes. This situation occurs, for example, when the robustness of a node depends on its degree or in an intentional attack with insufficient knowledge of the network. We determine, for all strategies, the critical fraction pc of nodes that must be removed for disintegrating the network. We find that, for an intentional attack, little knowledge of the well-connected sites is sufficient to strongly reduce pc. At criticality, the topology of the network depends on the removal strategy, implying that different strategies may lead to different kinds of percolation transitions.

Network of listed companies based on common shareholders and the prediction of market volatility

NASA Astrophysics Data System (ADS)

Li, Jie; Ren, Da; Feng, Xu; Zhang, Yongjie

2016-11-01

In this paper, we build a network of listed companies in the Chinese stock market based on common shareholding data from 2003 to 2013. We analyze the evolution of topological characteristics of the network (e.g., average degree, diameter, average path length and clustering coefficient) with respect to the time sequence. Additionally, we consider the economic implications of topological characteristic changes on market volatility and use them to make future predictions. Our study finds that the network diameter significantly predicts volatility. After adding control variables used in traditional financial studies (volume, turnover and previous volatility), network topology still significantly influences volatility and improves the predictive ability of the model.

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

Emergence of hysteresis loop in social contagions on complex networks.

PubMed

Su, Zhen; Wang, Wei; Li, Lixiang; Xiao, Jinghua; Stanley, H Eugene

2017-07-21

Understanding the spreading mechanisms of social contagions in complex network systems has attracted much attention in the physics community. Here we propose a generalized threshold model to describe social contagions. Using extensive numerical simulations and theoretical analyses, we find that a hysteresis loop emerges in the system. Specifically, the steady state of the system is sensitive to the initial conditions of the dynamics of the system. In the steady state, the adoption size increases discontinuously with the transmission probability of information about social contagions, and trial size exhibits a non-monotonic pattern, i.e., it first increases discontinuously then decreases continuously. Finally we study social contagions on heterogeneous networks and find that network topology does not qualitatively affect our results.

Smart-Grid Backbone Network Real-Time Delay Reduction via Integer Programming.

PubMed

Pagadrai, Sasikanth; Yilmaz, Muhittin; Valluri, Pratyush

2016-08-01

This research investigates an optimal delay-based virtual topology design using integer linear programming (ILP), which is applied to the current backbone networks such as smart-grid real-time communication systems. A network traffic matrix is applied and the corresponding virtual topology problem is solved using the ILP formulations that include a network delay-dependent objective function and lightpath routing, wavelength assignment, wavelength continuity, flow routing, and traffic loss constraints. The proposed optimization approach provides an efficient deterministic integration of intelligent sensing and decision making, and network learning features for superior smart grid operations by adaptively responding the time-varying network traffic data as well as operational constraints to maintain optimal virtual topologies. A representative optical backbone network has been utilized to demonstrate the proposed optimization framework whose simulation results indicate that superior smart-grid network performance can be achieved using commercial networks and integer programming.

Glass transition temperature and topological constraints of sodium borophosphate glass-forming liquids.

PubMed

Jiang, Qi; Zeng, Huidan; Liu, Zhao; Ren, Jing; Chen, Guorong; Wang, Zhaofeng; Sun, Luyi; Zhao, Donghui

2013-09-28

Sodium borophosphate glasses exhibit intriguing mixed network former effect, with the nonlinear compositional dependence of their glass transition temperature as one of the most typical examples. In this paper, we establish the widely applicable topological constraint model of sodium borophosphate mixed network former glasses to explain the relationship between the internal structure and nonlinear changes of glass transition temperature. The application of glass topology network was discussed in detail in terms of the unified methodology for the quantitative distribution of each coordinated boron and phosphorus units and glass transition temperature dependence of atomic constraints. An accurate prediction of composition scaling of the glass transition temperature was obtained based on topological constraint model.

Attribute and topology based change detection in a constellation of previously detected objects

DOEpatents

Paglieroni, David W.; Beer, Reginald N.

2016-01-19

A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

Routing Topologies of Wireless Sensor Networks for Health Monitoring of a Cultural Heritage Site.

PubMed

Aparicio, Sofía; Martínez-Garrido, María I; Ranz, Javier; Fort, Rafael; Izquierdo, Miguel Ángel G

2016-10-19

This paper provides a performance evaluation of tree and mesh routing topologies of wireless sensor networks (WSNs) in a cultural heritage site. The historical site selected was San Juan Bautista church in Talamanca de Jarama (Madrid, Spain). We report the preliminary analysis required to study the effects of heating in this historical location using WSNs to monitor the temperature and humidity conditions during periods of weeks. To test which routing topology was better for this kind of application, the WSNs were first deployed on the upper floor of the CAEND institute in Arganda del Rey simulating the church deployment, but in the former scenario there was no direct line of sight between the WSN elements. Two parameters were selected to evaluate the performance of the routing topologies of WSNs: the percentage of received messages and the lifetime of the wireless sensor network. To analyze in more detail which topology gave the best performance, other communication parameters were also measured. The tree topology used was the collection tree protocol and the mesh topology was the XMESH provided by MEMSIC (Andover, MA, USA). For the scenarios presented in this paper, it can be concluded that the tree topology lost fewer messages than the mesh topology.

Food-web complexity emerging from ecological dynamics on adaptive networks.

PubMed

Garcia-Domingo, Josep L; Saldaña, Joan

2007-08-21

Food webs are complex networks describing trophic interactions in ecological communities. Since Robert May's seminal work on random structured food webs, the complexity-stability debate is a central issue in ecology: does network complexity increase or decrease food-web persistence? A multi-species predator-prey model incorporating adaptive predation shows that the action of ecological dynamics on the topology of a food web (whose initial configuration is generated either by the cascade model or by the niche model) render, when a significant fraction of adaptive predators is present, similar hyperbolic complexity-persistence relationships as those observed in empirical food webs. It is also shown that the apparent positive relation between complexity and persistence in food webs generated under the cascade model, which has been pointed out in previous papers, disappears when the final connection is used instead of the initial one to explain species persistence.

Aberrant brain functional connectome in patients with obstructive sleep apnea.

PubMed

Chen, Li-Ting; Fan, Xiao-Le; Li, Hai-Jun; Ye, Cheng-Long; Yu, Hong-Hui; Xin, Hui-Zhen; Gong, Hong-Han; Peng, De-Chang; Yan, Li-Ping

2018-01-01

Obstructive sleep apnea (OSA) is accompanied by widespread abnormal spontaneous regional activity related to cognitive deficits. However, little is known about the topological properties of the functional brain connectome of patients with OSA. This study aimed to use the graph theory approaches to investigate the topological properties and functional connectivity (FC) of the functional connectome in patients with OSA, based on resting-state functional magnetic resonance imaging (rs-fMRI). Forty-five male patients with newly diagnosed untreated severe OSA and 45 male good sleepers (GSs) underwent a polysomnography (PSG), clinical evaluations, and rs-fMRI scans. The automated anatomical labeling (AAL) atlas was used to construct the functional brain connectome. The topological organization and FC of brain functional networks in patients with OSA were characterized using graph theory methods and investigated the relationship between functional network topology and clinical variables. Both the patients with OSA and the GSs exhibited high-efficiency "small-world" network attributes. However, the patients with OSA exhibited decreased σ, γ, E glob ; increased Lp, λ; and abnormal nodal centralities in several default-mode network (DMN), salience network (SN), and central executive network (CEN) regions. However, the patients with OSA exhibited abnormal functional connections between the DMN, SN, and CEN. The disrupted FC was significantly positive correlations with the global network metrics γ and σ. The global network metrics were significantly correlated with the Epworth Sleepiness Scale (ESS) score, Montreal Cognitive Assessment (MoCA) score, and oxygen desaturation index. The findings suggest that the functional connectome of patients with OSA exhibited disrupted functional integration and segregation, and functional disconnections of the DMN, SN, and CEN. The aberrant topological attributes may be associated with disrupted FC and cognitive functions. These topological abnormalities and disconnections might be potential biomarkers of cognitive impairments in patients with OSA.

A network perspective on the topological importance of enzymes and their phylogenetic conservation

PubMed Central

Liu, Wei-chung; Lin, Wen-hsien; Davis, Andrew J; Jordán, Ferenc; Yang, Hsih-te; Hwang, Ming-jing

2007-01-01

Background A metabolic network is the sum of all chemical transformations or reactions in the cell, with the metabolites being interconnected by enzyme-catalyzed reactions. Many enzymes exist in numerous species while others occur only in a few. We ask if there are relationships between the phylogenetic profile of an enzyme, or the number of different bacterial species that contain it, and its topological importance in the metabolic network. Our null hypothesis is that phylogenetic profile is independent of topological importance. To test our null hypothesis we constructed an enzyme network from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. We calculated three network indices of topological importance: the degree or the number of connections of a network node; closeness centrality, which measures how close a node is to others; and betweenness centrality measuring how frequently a node appears on all shortest paths between two other nodes. Results Enzyme phylogenetic profile correlates best with betweenness centrality and also quite closely with degree, but poorly with closeness centrality. Both betweenness and closeness centralities are non-local measures of topological importance and it is intriguing that they have contrasting power of predicting phylogenetic profile in bacterial species. We speculate that redundancy in an enzyme network may be reflected by betweenness centrality but not by closeness centrality. We also discuss factors influencing the correlation between phylogenetic profile and topological importance. Conclusion Our analysis falsifies the hypothesis that phylogenetic profile of enzymes is independent of enzyme network importance. Our results show that phylogenetic profile correlates better with degree and betweenness centrality, but less so with closeness centrality. Enzymes that occur in many bacterial species tend to be those that have high network importance. We speculate that this phenomenon originates in mechanisms driving network evolution. Closeness centrality reflects phylogenetic profile poorly. This is because metabolic networks often consist of distinct functional modules and some are not in the centre of the network. Enzymes in these peripheral parts of a network might be important for cell survival and should therefore occur in many bacterial species. They are, however, distant from other enzymes in the same network. PMID:17425808

Routing in Networks with Random Topologies

NASA Technical Reports Server (NTRS)

Bambos, Nicholas

1997-01-01

We examine the problems of routing and server assignment in networks with random connectivities. In such a network the basic topology is fixed, but during each time slot and for each of tis input queues, each server (node) is either connected to or disconnected from each of its queues with some probability.

Analysis on the urban street network of Korea: Connections between topology and meta-information

NASA Astrophysics Data System (ADS)

Lee, Byoung-Hwa; Jung, Woo-Sung

2018-05-01

Cities consist of infrastructure that enables transportation, which can be considered as topology in abstract terms. Once cities are physically organized in terms of infrastructure, people interact with each other to form the values, which can be regarded as the meta-information of the cities. The topology and meta-information coevolve together as the cities are developed. In this study, we investigate the relationship between the topology and meta-information for a street network, which has aspects of both a complex network and planar graph. The degree of organization of a street structure determines the efficiency and productivity of the city in that they act as blood vessels to transport people, goods, and information. We analyze the topological aspect of a street network using centralities including the betweenness, closeness, straightness, and information. We classify the cities into several groups that share common meta-information based on the centrality, indicating that the topological factor of the street structure is closely related to meta-information through coevolution. We also obtain the coevolution in the planned cities using the regularity. Another footprint is the relation between the street segment length and the population, which shows the sublinear scaling.

Maximum likelihood: Extracting unbiased information from complex networks

NASA Astrophysics Data System (ADS)

Garlaschelli, Diego; Loffredo, Maria I.

2008-07-01

The choice of free parameters in network models is subjective, since it depends on what topological properties are being monitored. However, we show that the maximum likelihood (ML) principle indicates a unique, statistically rigorous parameter choice, associated with a well-defined topological feature. We then find that, if the ML condition is incompatible with the built-in parameter choice, network models turn out to be intrinsically ill defined or biased. To overcome this problem, we construct a class of safely unbiased models. We also propose an extension of these results that leads to the fascinating possibility to extract, only from topological data, the “hidden variables” underlying network organization, making them “no longer hidden.” We test our method on World Trade Web data, where we recover the empirical gross domestic product using only topological information.

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.

Minimal Network Topologies for Signal Processing during Collective Cell Chemotaxis.

PubMed

Yue, Haicen; Camley, Brian A; Rappel, Wouter-Jan

2018-06-19

Cell-cell communication plays an important role in collective cell migration. However, it remains unclear how cells in a group cooperatively process external signals to determine the group's direction of motion. Although the topology of signaling pathways is vitally important in single-cell chemotaxis, the signaling topology for collective chemotaxis has not been systematically studied. Here, we combine mathematical analysis and simulations to find minimal network topologies for multicellular signal processing in collective chemotaxis. We focus on border cell cluster chemotaxis in the Drosophila egg chamber, in which responses to several experimental perturbations of the signaling network are known. Our minimal signaling network includes only four elements: a chemoattractant, the protein Rac (indicating cell activation), cell protrusion, and a hypothesized global factor responsible for cell-cell interaction. Experimental data on cell protrusion statistics allows us to systematically narrow the number of possible topologies from more than 40,000,000 to only six minimal topologies with six interactions between the four elements. This analysis does not require a specific functional form of the interactions, and only qualitative features are needed; it is thus robust to many modeling choices. Simulations of a stochastic biochemical model of border cell chemotaxis show that the qualitative selection procedure accurately determines which topologies are consistent with the experiment. We fit our model for all six proposed topologies; each produces results that are consistent with all experimentally available data. Finally, we suggest experiments to further discriminate possible pathway topologies. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

A system biology approach for understanding the miRNA regulatory network in colon rectal cancer.

PubMed

Pradhan, Meeta; Nagulapalli, Kshithija; Ledford, Lakenvia; Pandit, Yogesh; Palakal, Mathew

2015-01-01

In this paper we present a systems biology approach to the understanding of the miRNA-regulatory network in colon rectal cancer. An initial set of significant genes in Colon Rectal Cancer (CRC) were obtained by mining relevant literature. An initial set of cancer-related miRNAs were obtained from three databases: miRBase, miRWalk, Targetscan and GEO microarray experiment. First principle methods were then used to generate the global miRNA-gene network. Significant miRNAs and associated transcription factors in the global miRNA-gene network were identified using topological and sub-graph analyses. Eleven novel miRNAs were identified and three of the novel miRNAs, hsa-miR-630, hsa-miR-100 and hsa-miR-99a, were further analysed to elucidate their role in CRC. The proposed methodology effectively made use of literature data and was able to show novel, significant miRNA-transcription associations in CRC.

Multiple Dirac cones and topological magnetism in honeycomb-monolayer transition metal trichalcogenides

NASA Astrophysics Data System (ADS)

Sugita, Yusuke; Miyake, Takashi; Motome, Yukitoshi

2018-01-01

The discovery of monolayer graphene has initiated two fertile fields in condensed matter physics: Dirac semimetals and atomically thin layered materials. When these trends meet again in transition metal compounds, which possess spin and orbital degrees of freedom and strong electron correlations, more exotic phenomena are expected to emerge in the cross section of topological states of matter and Mott physics. Here, we show by using ab initio calculations that a monolayer form of transition metal trichalcogenides (TMTs), which has a honeycomb network of 4 d and 5 d transition metal cations, may exhibit multiple Dirac cones in the electronic structure of the half-filled eg orbitals. The Dirac cones are gapped by the spin-orbit coupling under the trigonal lattice distortion and, hence, can be tuned by tensile strain. Furthermore, we show that electron correlations and carrier doping turn the multiple Dirac semimetal into a topological ferromagnet with high Chern number. Our findings indicate that the honeycomb-monolayer TMTs provide a good playground for correlated Dirac electrons and topologically nontrivial magnetism.

Persistent homology analysis of ion aggregations and hydrogen-bonding networks.

PubMed

Xia, Kelin

2018-05-16

Despite the great advancement of experimental tools and theoretical models, a quantitative characterization of the microscopic structures of ion aggregates and their associated water hydrogen-bonding networks still remains a challenging problem. In this paper, a newly-invented mathematical method called persistent homology is introduced, for the first time, to quantitatively analyze the intrinsic topological properties of ion aggregation systems and hydrogen-bonding networks. The two most distinguishable properties of persistent homology analysis of assembly systems are as follows. First, it does not require a predefined bond length to construct the ion or hydrogen-bonding network. Persistent homology results are determined by the morphological structure of the data only. Second, it can directly measure the size of circles or holes in ion aggregates and hydrogen-bonding networks. To validate our model, we consider two well-studied systems, i.e., NaCl and KSCN solutions, generated from molecular dynamics simulations. They are believed to represent two morphological types of aggregation, i.e., local clusters and extended ion networks. It has been found that the two aggregation types have distinguishable topological features and can be characterized by our topological model very well. Further, we construct two types of networks, i.e., O-networks and H2O-networks, for analyzing the topological properties of hydrogen-bonding networks. It is found that for both models, KSCN systems demonstrate much more dramatic variations in their local circle structures with a concentration increase. A consistent increase of large-sized local circle structures is observed and the sizes of these circles become more and more diverse. In contrast, NaCl systems show no obvious increase of large-sized circles. Instead a consistent decline of the average size of the circle structures is observed and the sizes of these circles become more and more uniform with a concentration increase. As far as we know, these unique intrinsic topological features in ion aggregation systems have never been pointed out before. More importantly, our models can be directly used to quantitatively analyze the intrinsic topological invariants, including circles, loops, holes, and cavities, of any network-like structures, such as nanomaterials, colloidal systems, biomolecular assemblies, among others. These topological invariants cannot be described by traditional graph and network models.

Detecting network communities beyond assortativity-related attributes

NASA Astrophysics Data System (ADS)

Liu, Xin; Murata, Tsuyoshi; Wakita, Ken

2014-07-01

In network science, assortativity refers to the tendency of links to exist between nodes with similar attributes. In social networks, for example, links tend to exist between individuals of similar age, nationality, location, race, income, educational level, religious belief, and language. Thus, various attributes jointly affect the network topology. An interesting problem is to detect community structure beyond some specific assortativity-related attributes ρ, i.e., to take out the effect of ρ on network topology and reveal the hidden community structures which are due to other attributes. An approach to this problem is to redefine the null model of the modularity measure, so as to simulate the effect of ρ on network topology. However, a challenge is that we do not know to what extent the network topology is affected by ρ and by other attributes. In this paper, we propose a distance modularity, which allows us to freely choose any suitable function to simulate the effect of ρ. Such freedom can help us probe the effect of ρ and detect the hidden communities which are due to other attributes. We test the effectiveness of distance modularity on synthetic benchmarks and two real-world networks.

Becoming-Topologies of Education: Deformations, Networks and the Database Effect

ERIC Educational Resources Information Center

Thompson, Greg; Cook, Ian

2015-01-01

This article uses topological approaches to suggest that education is becoming-topological. Analyses presented in a recent double-issue of "Theory, Culture & Society" are used to demonstrate the utility of topology for education. In particular, the article explains education's topological character through examining the global…

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.

Classification of complex networks based on similarity of topological network features

NASA Astrophysics Data System (ADS)

Attar, Niousha; Aliakbary, Sadegh

2017-09-01

Over the past few decades, networks have been widely used to model real-world phenomena. Real-world networks exhibit nontrivial topological characteristics and therefore, many network models are proposed in the literature for generating graphs that are similar to real networks. Network models reproduce nontrivial properties such as long-tail degree distributions or high clustering coefficients. In this context, we encounter the problem of selecting the network model that best fits a given real-world network. The need for a model selection method reveals the network classification problem, in which a target-network is classified into one of the candidate network models. In this paper, we propose a novel network classification method which is independent of the network size and employs an alignment-free metric of network comparison. The proposed method is based on supervised machine learning algorithms and utilizes the topological similarities of networks for the classification task. The experiments show that the proposed method outperforms state-of-the-art methods with respect to classification accuracy, time efficiency, and robustness to noise.

Dynamical and topological aspects of consensus formation in complex networks

NASA Astrophysics Data System (ADS)

Chacoma, A.; Mato, G.; Kuperman, M. N.

2018-04-01

The present work analyzes a particular scenario of consensus formation, where the individuals navigate across an underlying network defining the topology of the walks. The consensus, associated to a given opinion coded as a simple message, is generated by interactions during the agent's walk and manifest itself in the collapse of the various opinions into a single one. We analyze how the topology of the underlying networks and the rules of interaction between the agents promote or inhibit the emergence of this consensus. We find that non-linear interaction rules are required to form consensus and that consensus is more easily achieved in networks whose degree distribution is narrower.

A fault-tolerant small world topology control model in ad hoc networks for search and rescue

NASA Astrophysics Data System (ADS)

Tan, Mian; Fang, Ling; Wu, Yue; Zhang, Bo; Chang, Bowen; Holme, Petter; Zhao, Jing

2018-02-01

Due to their self-organized, multi-hop and distributed characteristics, ad hoc networks are useful in search and rescue. Topology control models need to be designed for energy-efficient, robust and fast communication in ad hoc networks. This paper proposes a topology control model which specializes for search and rescue-Compensation Small World-Repeated Game (CSWRG)-which integrates mobility models, constructing small world networks and a game-theoretic approach to the allocation of resources. Simulation results show that our mobility models can enhance the communication performance of the constructed small-world networks. Our strategy, based on repeated game, can suppress selfish behavior and compensate agents that encounter selfish or faulty neighbors. This model could be useful for the design of ad hoc communication networks.

The stability of financial market networks

NASA Astrophysics Data System (ADS)

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

2014-08-01

We investigate the stability of a financial market network by measuring its topological robustness, namely the ability of the network to resist structural or topological changes. The closing prices of 710 stocks in the Shanghai Stock Exchange (SSE) from 2005 to 2011 are chosen as the empirical data. We divide the period into three sub-periods: before, during, and after the US sub-prime crisis. By monitoring the size of the clusters which fall apart from the network after removing the nodes (i.e., the listed companies in the SSE), we find that: i) the SSE network is sensitive to the nodes' failure, which implies that the network is unstable. ii) the SSE network before the financial crisis has the strongest robustness against the intentional topological damage; iii) the hubs (i.e., highly connected nodes) connect with each other directly and play a vital important role in maintaining SSE network's stability.

Isolated desynchronization and intertwined synchronization in networks of semiconductor lasers

NASA Astrophysics Data System (ADS)

Xu, Mingfeng; Pan, Wei; Zhang, Liyue

2018-04-01

Two patterns of synchronization in networks of semiconductor lasers (SLs) induced by symmetries of inherent network topology are presented. One type is termed isolated desynchronization, in which one or more clusters lose stability while all others remain synchronized. Another type is intertwined synchronization, in which some clusters always achieve and lose their synchrony at the same time. The existence of these special synchronization patterns and their relationship with the topology of network is discussed systemically. The results show that such behaviors exist in different topologies of SL networks. We also discussed the influence of significant parameters of SL networks on the stability of cluster synchronization. It is shown that the network dynamics is sensitive to the two key internal parameters of SLs, the linewidth-enhancement factor, and gain saturation coefficient. Our work is very beneficial to the implementation of secure communication and synchronization networks based on SLs.

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series.

PubMed

Klimovskaia, Anna; Ganscha, Stefan; Claassen, Manfred

2016-12-01

Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.

Revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy.

PubMed

Niu, Haijing; Wang, Jinhui; Zhao, Tengda; Shu, Ni; He, Yong

2012-01-01

The human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics. We utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques. Our results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.

Topological enslavement in evolutionary games on correlated multiplex networks

NASA Astrophysics Data System (ADS)

Kleineberg, Kaj-Kolja; Helbing, Dirk

2018-05-01

Governments and enterprises strongly rely on incentives to generate favorable outcomes from social and strategic interactions between individuals. The incentives are usually modeled by payoffs in evolutionary games, such as the prisoners dilemma or the harmony game, with imitation dynamics. Adjusting the incentives by changing the payoff parameters can favor cooperation, as found in the harmony game, over defection, which prevails in the prisoner’s dilemma. Here, we show that this is not always the case if individuals engage in strategic interactions in multiple domains. In particular, we investigate evolutionary games on multiplex networks where individuals obtain an aggregate payoff. We explicitly control the strength of degree correlations between nodes in the different layers of the multiplex. We find that if the multiplex is composed of many layers and degree correlations are strong, the topology of the system enslaves the dynamics and the final outcome, cooperation or defection, becomes independent of the payoff parameters. The fate of the system is then determined by the initial conditions.

Topology-function conservation in protein-protein interaction networks.

PubMed

Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša

2015-05-15

Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.

NASA Communications Augmentation network

NASA Technical Reports Server (NTRS)

Omidyar, Guy C.; Butler, Thomas E.; Laios, Straton C.

1990-01-01

The NASA Communications (Nascom) Division of the Mission Operations and Data Systems Directorate (MO&DSD) is to undertake a major initiative to develop the Nascom Augmentation (NAUG) network to achieve its long-range service objectives for operational data transport to support the Space Station Freedom Program, the Earth Observing System (EOS), and other projects. The NAUG is the Nascom ground communications network being developed to accommodate the operational traffic of the mid-1990s and beyond. The NAUG network development will be based on the Open Systems Interconnection Reference Model (OSI-RM). This paper describes the NAUG network architecture, subsystems, topology, and services; addresses issues of internetworking the Nascom network with other elements of the Space Station Information System (SSIS); discusses the operations environment. This paper also notes the areas of related research and presents the current conception of how the network will provide broadband services in 1998.

Noise enhances information transfer in hierarchical networks.

PubMed

Czaplicka, Agnieszka; Holyst, Janusz A; Sloot, Peter M A

2013-01-01

We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor.

Noise enhances information transfer in hierarchical networks

PubMed Central

Czaplicka, Agnieszka; Holyst, Janusz A.; Sloot, Peter M. A.

2013-01-01

We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor. PMID:23390574

Initial data sets and the topology of closed three-manifolds in general relativity

NASA Astrophysics Data System (ADS)

Carfora, M.

1983-10-01

The interaction between the matter content of a closed physical space associated with a generic gravitational configuration and the topology of the underlying closed three-manifold is discussed. Within the context of the conformal approach to the initial value problem, it is shown that the presence of enough matter and radiation favors the three-sphere topology or the worm-hole topology. It is argued that such topologies leave more room for possible gravitational initial data sets for the field equations.

The topology of large Open Connectome networks for the human brain.

PubMed

Gastner, Michael T; Ódor, Géza

2016-06-07

The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to nodes and edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension D and the small-world coefficient σ of these networks. While σ suggests a small-world topology, we found that D < 4 showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.

The topology of large Open Connectome networks for the human brain

NASA Astrophysics Data System (ADS)

Gastner, Michael T.; Ódor, Géza

2016-06-01

The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to nodes and edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension D and the small-world coefficient σ of these networks. While σ suggests a small-world topology, we found that D < 4 showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.

Fermionic topological quantum states as tensor networks

NASA Astrophysics Data System (ADS)

Wille, C.; Buerschaper, O.; Eisert, J.

2017-06-01

Tensor network states, and in particular projected entangled pair states, play an important role in the description of strongly correlated quantum lattice systems. They do not only serve as variational states in numerical simulation methods, but also provide a framework for classifying phases of quantum matter and capture notions of topological order in a stringent and rigorous language. The rapid development in this field for spin models and bosonic systems has not yet been mirrored by an analogous development for fermionic models. In this work, we introduce a tensor network formalism capable of capturing notions of topological order for quantum systems with fermionic components. At the heart of the formalism are axioms of fermionic matrix-product operator injectivity, stable under concatenation. Building upon that, we formulate a Grassmann number tensor network ansatz for the ground state of fermionic twisted quantum double models. A specific focus is put on the paradigmatic example of the fermionic toric code. This work shows that the program of describing topologically ordered systems using tensor networks carries over to fermionic models.

Topological Origin of the Network Dilation Anomaly in Ion-Exchanged Glasses

NASA Astrophysics Data System (ADS)

Wang, Mengyi; Smedskjaer, Morten M.; Mauro, John C.; Sant, Gaurav; Bauchy, Mathieu

2017-11-01

Ion exchange is commonly used to strengthen oxide glasses. However, the resulting stuffed glasses usually do not reach the molar volume of as-melted glasses of similar composition—a phenomenon known as the network dilation anomaly. This behavior seriously limits the potential for the chemical strengthening of glasses and its origin remains one of the mysteries of glass science. Here, based on molecular dynamics simulations of sodium silicate glasses coupled with topological constraint theory, we show that the topology of the atomic network controls the extent of ion-exchange-induced dilation. We demonstrate that isostatic glasses do not show any network dilation anomaly. This is found to arise from the combined absence of floppy modes of deformation and internal eigenstress in isostatic atomic networks.

Multiplex lexical networks reveal patterns in early word acquisition in children

NASA Astrophysics Data System (ADS)

Stella, Massimo; Beckage, Nicole M.; Brede, Markus

2017-04-01

Network models of language have provided a way of linking cognitive processes to language structure. However, current approaches focus only on one linguistic relationship at a time, missing the complex multi-relational nature of language. In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.e. a multi-layered network where N = 529 words/nodes are connected according to four relationship: (i) free association, (ii) feature sharing, (iii) co-occurrence, and (iv) phonological similarity. We investigate the topology of the resulting multiplex and then proceed to evaluate single layers and the full multiplex structure on their ability to predict empirically observed age of acquisition data of English speaking toddlers. We find that the multiplex topology is an important proxy of the cognitive processes of acquisition, capable of capturing emergent lexicon structure. In fact, we show that the multiplex structure is fundamentally more powerful than individual layers in predicting the ordering with which words are acquired. Furthermore, multiplex analysis allows for a quantification of distinct phases of lexical acquisition in early learners: while initially all the multiplex layers contribute to word learning, after about month 23 free associations take the lead in driving word acquisition.

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

John Homer; Ashok Varikuti; Xinming Ou

Various tools exist to analyze enterprise network systems and to produce attack graphs detailing how attackers might penetrate into the system. These attack graphs, however, are often complex and difficult to comprehend fully, and a human user may find it problematic to reach appropriate configuration decisions. This paper presents methodologies that can 1) automatically identify portions of an attack graph that do not help a user to understand the core security problems and so can be trimmed, and 2) automatically group similar attack steps as virtual nodes in a model of the network topology, to immediately increase the understandability ofmore » the data. We believe both methods are important steps toward improving visualization of attack graphs to make them more useful in configuration management for large enterprise networks. We implemented our methods using one of the existing attack-graph toolkits. Initial experimentation shows that the proposed approaches can 1) significantly reduce the complexity of attack graphs by trimming a large portion of the graph that is not needed for a user to understand the security problem, and 2) significantly increase the accessibility and understandability of the data presented in the attack graph by clearly showing, within a generated visualization of the network topology, the number and type of potential attacks to which each host is exposed.« less

Connected Dominating Set Based Topology Control in Wireless Sensor Networks

ERIC Educational Resources Information Center

He, Jing

2012-01-01

Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…

Network Design: Best Practices for Alberta School Jurisdictions.

ERIC Educational Resources Information Center

Schienbein, Ralph

This report examines subsections of the computer network topology that relate to end-to-end performance and capacity planning in schools. Active star topology, Category 5 wiring, Ethernet, and intelligent devices are assumed. The report describes a model that can be used to project WAN (wide area network) connection speeds based on user traffic,…

Chimera states: Effects of different coupling topologies

NASA Astrophysics Data System (ADS)

Bera, Bidesh K.; Majhi, Soumen; Ghosh, Dibakar; Perc, Matjaž

2017-04-01

Collective behavior among coupled dynamical units can emerge in various forms as a result of different coupling topologies as well as different types of coupling functions. Chimera states have recently received ample attention as a fascinating manifestation of collective behavior, in particular describing a symmetry breaking spatiotemporal pattern where synchronized and desynchronized states coexist in a network of coupled oscillators. In this perspective, we review the emergence of different chimera states, focusing on the effects of different coupling topologies that describe the interaction network connecting the oscillators. We cover chimera states that emerge in local, nonlocal and global coupling topologies, as well as in modular, temporal and multilayer networks. We also provide an outline of challenges and directions for future research.

On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes.

PubMed

Vegué, Marina; Perin, Rodrigo; Roxin, Alex

2017-08-30

The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering several distinct classes of network topology, including clustered networks, networks with distance-dependent connectivity, and those with broad degree distributions. To our surprise, we found that all of these qualitatively distinct topologies could account equally well for all reported nonrandom features despite being easily distinguishable from one another at the network level. This apparent paradox was a consequence of estimating network properties given only small sample sizes. In other words, networks that differ markedly in their global structure can look quite similar locally. This makes inferring network structure from small sample sizes, a necessity given the technical difficulty inherent in simultaneous intracellular recordings, problematic. We found that a network statistic called the sample degree correlation (SDC) overcomes this difficulty. The SDC depends only on parameters that can be estimated reliably given small sample sizes and is an accurate fingerprint of every topological family. We applied the SDC criterion to data from rat visual and somatosensory cortex and discovered that the connectivity was not consistent with any of these main topological classes. However, we were able to fit the experimental data with a more general network class, of which all previous topologies were special cases. The resulting network topology could be interpreted as a combination of physical spatial dependence and nonspatial, hierarchical clustering. SIGNIFICANCE STATEMENT The connectivity of cortical microcircuits exhibits features that are inconsistent with a simple random network. Here, we show that several classes of network models can account for this nonrandom structure despite qualitative differences in their global properties. This apparent paradox is a consequence of the small numbers of simultaneously recorded neurons in experiment: when inferred via small sample sizes, many networks may be indistinguishable despite being globally distinct. We develop a connectivity measure that successfully classifies networks even when estimated locally with a few neurons at a time. We show that data from rat cortex is consistent with a network in which the likelihood of a connection between neurons depends on spatial distance and on nonspatial, asymmetric clustering. Copyright © 2017 the authors 0270-6474/17/378498-13$15.00/0.

A Dynamic Game on Network Topology for Counterinsurgency Applications

DTIC Science & Technology

2015-03-26

scenario. This study creates a dynamic game on network topology to provide insight into the effec- tiveness of offensive targeting strategies determined by...focused upon the diffusion of thoughts and innovations throughout complex social networks. Coleman et al. (1966) and Ryan & Gross (1950) investigated...free networks make them extremely resilient against errors but very vulnerable to attack. Most interest- ingly, a determined attacker can remove well

Gene regulatory and signaling networks exhibit distinct topological distributions of motifs

NASA Astrophysics Data System (ADS)

Ferreira, Gustavo Rodrigues; Nakaya, Helder Imoto; Costa, Luciano da Fontoura

2018-04-01

The biological processes of cellular decision making and differentiation involve a plethora of signaling pathways and gene regulatory circuits. These networks in turn exhibit a multitude of motifs playing crucial parts in regulating network activity. Here we compare the topological placement of motifs in gene regulatory and signaling networks and observe that it suggests different evolutionary strategies in motif distribution for distinct cellular subnetworks.

A Comparison of Geographic Information Systems, Complex Networks, and Other Models for Analyzing Transportation Network Topologies

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia (Technical Monitor); Kuby, Michael; Tierney, Sean; Roberts, Tyler; Upchurch, Christopher

2005-01-01

This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.

Packet Traffic Dynamics Near Onset of Congestion in Data Communication Network Model

NASA Astrophysics Data System (ADS)

Lawniczak, A. T.; Tang, X.

2006-05-01

The dominant technology of data communication networks is the Packet Switching Network (PSN). It is a complex technology organized as various hierarchical layers according to the International Standard Organization (ISO) Open Systems Interconnect (OSI) Reference Model. The Network Layer of the ISO OSI Reference Model is responsible for delivering packets from their sources to their destinations and for dealing with congestion if it arises in a network. Thus, we focus on this layer and present an abstraction of the Network Layer of the ISO OSI Reference Model. Using this abstraction we investigate how onset of traffic congestion is affected for various routing algorithms by changes in network connection topology. We study how aggregate measures of network performance depend on network connection topology and routing. We explore packets traffic spatio-temporal dynamics near the phase transition point from free flow to congestion for various network connection topologies and routing algorithms. We consider static and adaptive routings. We present selected simulation results.

Identifying protein complexes based on brainstorming strategy.

PubMed

Shen, Xianjun; Zhou, Jin; Yi, Li; Hu, Xiaohua; He, Tingting; Yang, Jincai

2016-11-01

Protein complexes comprising of interacting proteins in protein-protein interaction network (PPI network) play a central role in driving biological processes within cells. Recently, more and more swarm intelligence based algorithms to detect protein complexes have been emerging, which have become the research hotspot in proteomics field. In this paper, we propose a novel algorithm for identifying protein complexes based on brainstorming strategy (IPC-BSS), which is integrated into the main idea of swarm intelligence optimization and the improved K-means algorithm. Distance between the nodes in PPI network is defined by combining the network topology and gene ontology (GO) information. Inspired by human brainstorming process, IPC-BSS algorithm firstly selects the clustering center nodes, and then they are separately consolidated with the other nodes with short distance to form initial clusters. Finally, we put forward two ways of updating the initial clusters to search optimal results. Experimental results show that our IPC-BSS algorithm outperforms the other classic algorithms on yeast and human PPI networks, and it obtains many predicted protein complexes with biological significance. Copyright © 2016 Elsevier Inc. All rights reserved.

Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs

PubMed Central

Fretter, Christoph; Lesne, Annick; Hilgetag, Claus C.; Hütt, Marc-Thorsten

2017-01-01

Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of network reverberations and self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience. PMID:28186182

Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs.

PubMed

Fretter, Christoph; Lesne, Annick; Hilgetag, Claus C; Hütt, Marc-Thorsten

2017-02-10

Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of network reverberations and self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience.

Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs

NASA Astrophysics Data System (ADS)

Fretter, Christoph; Lesne, Annick; Hilgetag, Claus C.; Hütt, Marc-Thorsten

2017-02-01

Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of network reverberations and self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience.

Bayesian network models for error detection in radiotherapy plans

NASA Astrophysics Data System (ADS)

Kalet, Alan M.; Gennari, John H.; Ford, Eric C.; Phillips, Mark H.

2015-04-01

The purpose of this study is to design and develop a probabilistic network for detecting errors in radiotherapy plans for use at the time of initial plan verification. Our group has initiated a multi-pronged approach to reduce these errors. We report on our development of Bayesian models of radiotherapy plans. Bayesian networks consist of joint probability distributions that define the probability of one event, given some set of other known information. Using the networks, we find the probability of obtaining certain radiotherapy parameters, given a set of initial clinical information. A low probability in a propagated network then corresponds to potential errors to be flagged for investigation. To build our networks we first interviewed medical physicists and other domain experts to identify the relevant radiotherapy concepts and their associated interdependencies and to construct a network topology. Next, to populate the network’s conditional probability tables, we used the Hugin Expert software to learn parameter distributions from a subset of de-identified data derived from a radiation oncology based clinical information database system. These data represent 4990 unique prescription cases over a 5 year period. Under test case scenarios with approximately 1.5% introduced error rates, network performance produced areas under the ROC curve of 0.88, 0.98, and 0.89 for the lung, brain and female breast cancer error detection networks, respectively. Comparison of the brain network to human experts performance (AUC of 0.90 ± 0.01) shows the Bayes network model performs better than domain experts under the same test conditions. Our results demonstrate the feasibility and effectiveness of comprehensive probabilistic models as part of decision support systems for improved detection of errors in initial radiotherapy plan verification procedures.

On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies

PubMed Central

Garcia-Sanchez, Antonio-Javier; Losilla, Fernando; Rodenas-Herraiz, David; Cruz-Martinez, Felipe; Garcia-Sanchez, Felipe

2016-01-01

Wireless Multimedia Sensor Networks (WMSNs) are a special type of Wireless Sensor Network (WSN) where large amounts of multimedia data are transmitted over networks composed of low power devices. Hierarchical routing protocols typically used in WSNs for multi-path communication tend to overload nodes located within radio communication range of the data collection unit or data sink. The battery life of these nodes is therefore reduced considerably, requiring frequent battery replacement work to extend the operational life of the WSN system. In a wireless sensor network with mesh topology, any node may act as a forwarder node, thereby enabling multiple routing paths toward any other node or collection unit. In addition, mesh topologies have proven advantages, such as data transmission reliability, network robustness against node failures, and potential reduction in energy consumption. This work studies the feasibility of implementing WMSNs in mesh topologies and their limitations by means of exhaustive computer simulation experiments. To this end, a module developed for the Synchronous Energy Saving (SES) mode of the IEEE 802.15.5 mesh standard has been integrated with multimedia tools to thoroughly test video sequences encoded using H.264 in mesh networks. PMID:27164106

On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies.

PubMed

Garcia-Sanchez, Antonio-Javier; Losilla, Fernando; Rodenas-Herraiz, David; Cruz-Martinez, Felipe; Garcia-Sanchez, Felipe

2016-05-05

Wireless Multimedia Sensor Networks (WMSNs) are a special type of Wireless Sensor Network (WSN) where large amounts of multimedia data are transmitted over networks composed of low power devices. Hierarchical routing protocols typically used in WSNs for multi-path communication tend to overload nodes located within radio communication range of the data collection unit or data sink. The battery life of these nodes is therefore reduced considerably, requiring frequent battery replacement work to extend the operational life of the WSN system. In a wireless sensor network with mesh topology, any node may act as a forwarder node, thereby enabling multiple routing paths toward any other node or collection unit. In addition, mesh topologies have proven advantages, such as data transmission reliability, network robustness against node failures, and potential reduction in energy consumption. This work studies the feasibility of implementing WMSNs in mesh topologies and their limitations by means of exhaustive computer simulation experiments. To this end, a module developed for the Synchronous Energy Saving (SES) mode of the IEEE 802.15.5 mesh standard has been integrated with multimedia tools to thoroughly test video sequences encoded using H.264 in mesh networks.

Topology determines force distributions in one-dimensional random spring networks.

PubMed

Heidemann, Knut M; Sageman-Furnas, Andrew O; Sharma, Abhinav; Rehfeldt, Florian; Schmidt, Christoph F; Wardetzky, Max

2018-02-01

Networks of elastic fibers are ubiquitous in biological systems and often provide mechanical stability to cells and tissues. Fiber-reinforced materials are also common in technology. An important characteristic of such materials is their resistance to failure under load. Rupture occurs when fibers break under excessive force and when that failure propagates. Therefore, it is crucial to understand force distributions. Force distributions within such networks are typically highly inhomogeneous and are not well understood. Here we construct a simple one-dimensional model system with periodic boundary conditions by randomly placing linear springs on a circle. We consider ensembles of such networks that consist of N nodes and have an average degree of connectivity z but vary in topology. Using a graph-theoretical approach that accounts for the full topology of each network in the ensemble, we show that, surprisingly, the force distributions can be fully characterized in terms of the parameters (N,z). Despite the universal properties of such (N,z) ensembles, our analysis further reveals that a classical mean-field approach fails to capture force distributions correctly. We demonstrate that network topology is a crucial determinant of force distributions in elastic spring networks.

Topology determines force distributions in one-dimensional random spring networks

NASA Astrophysics Data System (ADS)

Heidemann, Knut M.; Sageman-Furnas, Andrew O.; Sharma, Abhinav; Rehfeldt, Florian; Schmidt, Christoph F.; Wardetzky, Max

2018-02-01

Networks of elastic fibers are ubiquitous in biological systems and often provide mechanical stability to cells and tissues. Fiber-reinforced materials are also common in technology. An important characteristic of such materials is their resistance to failure under load. Rupture occurs when fibers break under excessive force and when that failure propagates. Therefore, it is crucial to understand force distributions. Force distributions within such networks are typically highly inhomogeneous and are not well understood. Here we construct a simple one-dimensional model system with periodic boundary conditions by randomly placing linear springs on a circle. We consider ensembles of such networks that consist of N nodes and have an average degree of connectivity z but vary in topology. Using a graph-theoretical approach that accounts for the full topology of each network in the ensemble, we show that, surprisingly, the force distributions can be fully characterized in terms of the parameters (N ,z ) . Despite the universal properties of such (N ,z ) ensembles, our analysis further reveals that a classical mean-field approach fails to capture force distributions correctly. We demonstrate that network topology is a crucial determinant of force distributions in elastic spring networks.

[Aberrant topological properties of whole-brain functional network in chronic right-sided sensorineural hearing loss: a resting-state functional MRI study].

PubMed

Zhang, Lingling; Liu, Bin; Xu, Yangwen; Yang, Ming; Feng, Yuan; Huang, Yaqing; Huan, Zhichun; Hou, Zhaorui

2015-02-03

To investigate the topological properties of the functional brain network in unilateral sensorineural hearing loss patients. In this study, we acquired resting-state BOLD- fMRI data from 19 right-sided SNHL patients and 31 healthy controls with normal hearing and constructed their whole brain functional networks. Two-sample two-tailed t-tests were performed to investigate group differences in topological parameters between the USNHL patients and the controls. Partial correlation analysis was conducted to determine the relationships between the network metrics and USNHL-related variables. Both USNHL patients and controls exhibited small-word architecture in their brain functional networks within the range 0. 1 - 0. 2 of sparsity. Compared to the controls, USNHL patients showed significant increase in characteristic path length and normalized characteristic path length, but significant decrease in global efficiency. Clustering coefficient, local efficiency and normalized clustering coefficient demonstrated no significant difference. Furthermore, USNHL patients exhibited no significant association between the altered network metrics and the duration of USNHL or the severity of hearing loss. Our results indicated the altered topological properties of whole brain functional networks in USNHL patients, which may help us to understand pathophysiologic mechanism of USNHL patients.

Reconfigureable network node

DOEpatents

Vanderveen, Keith B [Tracy, CA; Talbot, Edward B [Livermore, CA; Mayer, Laurence E [Davis, CA

2008-04-08

Nodes in a network having a plurality of nodes establish communication links with other nodes using available transmission media, as the ability to establish such links becomes available and desirable. The nodes predict when existing communications links will fail, become overloaded or otherwise degrade network effectiveness and act to establish substitute or additional links before the node's ability to communicate with the other nodes on the network is adversely affected. A node stores network topology information and programmed link establishment rules and criteria. The node evaluates characteristics that predict existing links with other nodes becoming unavailable or degraded. The node then determines whether it can form a communication link with a substitute node, in order to maintain connectivity with the network. When changing its communication links, a node broadcasts that information to the network. Other nodes update their stored topology information and consider the updated topology when establishing new communications links for themselves.

A hybrid network-based method for the detection of disease-related genes

NASA Astrophysics Data System (ADS)

Cui, Ying; Cai, Meng; Dai, Yang; Stanley, H. Eugene

2018-02-01

Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein-protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

Inferring topological features of proteins from amino acid residue networks

NASA Astrophysics Data System (ADS)

Alves, Nelson Augusto; Martinez, Alexandre Souto

2007-02-01

Topological properties of native folds are obtained from statistical analysis of 160 low homology proteins covering the four structural classes. This is done analyzing one, two and three-vertex joint distribution of quantities related to the corresponding network of amino acid residues. Emphasis on the amino acid residue hydrophobicity leads to the definition of their center of mass as vertices in this contact network model with interactions represented by edges. The network analysis helps us to interpret experimental results such as hydrophobic scales and fraction of buried accessible surface area in terms of the network connectivity. Moreover, those networks show assortative mixing by degree. To explore the vertex-type dependent correlations, we build a network of hydrophobic and polar vertices. This procedure presents the wiring diagram of the topological structure of globular proteins leading to the following attachment probabilities between hydrophobic-hydrophobic 0.424(5), hydrophobic-polar 0.419(2) and polar-polar 0.157(3) residues.

Dynamical networks with topological self-organization

NASA Technical Reports Server (NTRS)

Zak, M.

2001-01-01

Coupled evolution of state and topology of dynamical networks is introduced. Due to the well organized tensor structure, the governing equations are presented in a canonical form, and required attractors as well as their basins can be easily implanted and controlled.

The Topographical Mapping in Drosophila Central Complex Network and Its Signal Routing

PubMed Central

Chang, Po-Yen; Su, Ta-Shun; Shih, Chi-Tin; Lo, Chung-Chuan

2017-01-01

Neural networks regulate brain functions by routing signals. Therefore, investigating the detailed organization of a neural circuit at the cellular levels is a crucial step toward understanding the neural mechanisms of brain functions. To study how a complicated neural circuit is organized, we analyzed recently published data on the neural circuit of the Drosophila central complex, a brain structure associated with a variety of functions including sensory integration and coordination of locomotion. We discovered that, except for a small number of “atypical” neuron types, the network structure formed by the identified 194 neuron types can be described by only a few simple mathematical rules. Specifically, the topological mapping formed by these neurons can be reconstructed by applying a generation matrix on a small set of initial neurons. By analyzing how information flows propagate with or without the atypical neurons, we found that while the general pattern of signal propagation in the central complex follows the simple topological mapping formed by the “typical” neurons, some atypical neurons can substantially re-route the signal pathways, implying specific roles of these neurons in sensory signal integration. The present study provides insights into the organization principle and signal integration in the central complex. PMID:28443014

Multiplexing topologies and time scales: The gains and losses of synchrony

NASA Astrophysics Data System (ADS)

Makovkin, Sergey; Kumar, Anil; Zaikin, Alexey; Jalan, Sarika; Ivanchenko, Mikhail

2017-11-01

Inspired by the recent interest in collective dynamics of biological neural networks immersed in the glial cell medium, we investigate the frequency and phase order, i.e., Kuramoto type of synchronization in a multiplex two-layer network of phase oscillators of different time scales and topologies. One of them has a long-range connectivity, exemplified by the Erdős-Rényi random network, and supports both kinds of synchrony. The other is a locally coupled two-dimensional lattice that can reach frequency synchronization but lacks phase order. Drastically different layer frequencies disentangle intra- and interlayer synchronization. We find that an indirect but sufficiently strong coupling through the regular layer can induce both phase order in the originally nonsynchronized random layer and global order, even when an isolated regular layer does not manifest it in principle. At the same time, the route to global synchronization is complex: an initial onset of (partial) synchrony in the regular layer, when its intra- and interlayer coupling is increased, provokes the loss of synchrony even in the originally synchronized random layer. Ultimately, a developed asynchronous dynamics in both layers is abruptly taken over by the global synchrony of both kinds.

Distributed fault detection over sensor networks with Markovian switching topologies

NASA Astrophysics Data System (ADS)

Ge, Xiaohua; Han, Qing-Long

2014-05-01

This paper deals with the distributed fault detection for discrete-time Markov jump linear systems over sensor networks with Markovian switching topologies. The sensors are scatteredly deployed in the sensor field and the fault detectors are physically distributed via a communication network. The system dynamics changes and sensing topology variations are modeled by a discrete-time Markov chain with incomplete mode transition probabilities. Each of these sensor nodes firstly collects measurement outputs from its all underlying neighboring nodes, processes these data in accordance with the Markovian switching topologies, and then transmits the processed data to the remote fault detector node. Network-induced delays and accumulated data packet dropouts are incorporated in the data transmission between the sensor nodes and the distributed fault detector nodes through the communication network. To generate localized residual signals, mode-independent distributed fault detection filters are proposed. By means of the stochastic Lyapunov functional approach, the residual system performance analysis is carried out such that the overall residual system is stochastically stable and the error between each residual signal and the fault signal is made as small as possible. Furthermore, a sufficient condition on the existence of the mode-independent distributed fault detection filters is derived in the simultaneous presence of incomplete mode transition probabilities, Markovian switching topologies, network-induced delays, and accumulated data packed dropouts. Finally, a stirred-tank reactor system is given to show the effectiveness of the developed theoretical results.

Structure and formation of ant transportation networks

PubMed Central

Latty, Tanya; Ramsch, Kai; Ito, Kentaro; Nakagaki, Toshiyuki; Sumpter, David J. T.; Middendorf, Martin; Beekman, Madeleine

2011-01-01

Many biological systems use extensive networks for the transport of resources and information. Ants are no exception. How do biological systems achieve efficient transportation networks in the absence of centralized control and without global knowledge of the environment? Here, we address this question by studying the formation and properties of inter-nest transportation networks in the Argentine ant (Linepithema humile). We find that the formation of inter-nest networks depends on the number of ants involved in the construction process. When the number of ants is sufficient and networks do form, they tend to have short total length but a low level of robustness. These networks are topologically similar to either minimum spanning trees or Steiner networks. The process of network formation involves an initial construction of multiple links followed by a pruning process that reduces the number of trails. Our study thus illuminates the conditions under and the process by which minimal biological transport networks can be constructed. PMID:21288958

A Brief Historical Introduction to Euler's Formula for Polyhedra, Topology, Graph Theory and Networks

ERIC Educational Resources Information Center

Debnath, Lokenath

2010-01-01

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 Konigsberg seven-bridge problem and some of the basic properties of graphs and networks for some understanding of the macroscopic behaviour of real…

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

Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study.

PubMed

Liang, Xia; Wang, Jinhui; Yan, Chaogan; Shu, Ni; Xu, Ke; Gong, Gaolang; He, Yong

2012-01-01

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01-0.027 Hz) versus slow-4 (0.027-0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the "best" network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027-0.073 Hz band exhibited greater reliability than those in the 0.01-0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies.

Modeling and dynamical topology properties of VANET based on complex networks theory

NASA Astrophysics Data System (ADS)

Zhang, Hong; Li, Jie

2015-01-01

Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate and control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What's more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a macroscopic perspective.

Modeling and dynamical topology properties of VANET based on complex networks theory

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

Zhang, Hong; Li, Jie, E-mail: prof.li@foxmail.com

2015-01-15

Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate andmore » control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What’s more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a macroscopic perspective.« less

A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.

PubMed

Zhang, Xiaomeng; Shao, Bin; Wu, Yangle; Qi, Ouyang

2013-01-01

One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.

A study of the Immune Epitope Database for some fungi species using network topological indices.

PubMed

Vázquez-Prieto, Severo; Paniagua, Esperanza; Solana, Hugo; Ubeira, Florencio M; González-Díaz, Humberto

2017-08-01

In the last years, the encryption of system structure information with different network topological indices has been a very active field of research. In the present study, we assembled for the first time a complex network using data obtained from the Immune Epitope Database for fungi species, and we then considered the general topology, the node degree distribution, and the local structure of this network. We also calculated eight node centrality measures for the observed network and compared it with three theoretical models. In view of the results obtained, we may expect that the present approach can become a valuable tool to explore the complexity of this database, as well as for the storage, manipulation, comparison, and retrieval of information contained therein.

Architecture of marine food webs: To be or not be a 'small-world'.

PubMed

Marina, Tomás Ignacio; Saravia, Leonardo A; Cordone, Georgina; Salinas, Vanesa; Doyle, Santiago R; Momo, Fernando R

2018-01-01

The search for general properties in network structure has been a central issue for food web studies in recent years. One such property is the small-world topology that combines a high clustering and a small distance between nodes of the network. This property may increase food web resilience but make them more sensitive to the extinction of connected species. Food web theory has been developed principally from freshwater and terrestrial ecosystems, largely omitting marine habitats. If theory needs to be modified to accommodate observations from marine ecosystems, based on major differences in several topological characteristics is still on debate. Here we investigated if the small-world topology is a common structural pattern in marine food webs. We developed a novel, simple and statistically rigorous method to examine the largest set of complex marine food webs to date. More than half of the analyzed marine networks exhibited a similar or lower characteristic path length than the random expectation, whereas 39% of the webs presented a significantly higher clustering than its random counterpart. Our method proved that 5 out of 28 networks fulfilled both features of the small-world topology: short path length and high clustering. This work represents the first rigorous analysis of the small-world topology and its associated features in high-quality marine networks. We conclude that such topology is a structural pattern that is not maximized in marine food webs; thus it is probably not an effective model to study robustness, stability and feasibility of marine ecosystems.

Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks

PubMed Central

Kominami, Daichi; Leibnitz, Kenji; Murata, Masayuki

2018-01-01

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes. PMID:29642483

Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks.

PubMed

Murakami, Masaya; Kominami, Daichi; Leibnitz, Kenji; Murata, Masayuki

2018-04-08

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

Effects of topology on network evolution

NASA Astrophysics Data System (ADS)

Oikonomou, Panos; Cluzel, Philippe

2006-08-01

The ubiquity of scale-free topology in nature raises the question of whether this particular network design confers an evolutionary advantage. A series of studies has identified key principles controlling the growth and the dynamics of scale-free networks. Here, we use neuron-based networks of boolean components as a framework for modelling a large class of dynamical behaviours in both natural and artificial systems. Applying a training algorithm, we characterize how networks with distinct topologies evolve towards a pre-established target function through a process of random mutations and selection. We find that homogeneous random networks and scale-free networks exhibit drastically different evolutionary paths. Whereas homogeneous random networks accumulate neutral mutations and evolve by sparse punctuated steps, scale-free networks evolve rapidly and continuously. Remarkably, this latter property is robust to variations of the degree exponent. In contrast, homogeneous random networks require a specific tuning of their connectivity to optimize their ability to evolve. These results highlight an organizing principle that governs the evolution of complex networks and that can improve the design of engineered systems.

Tensor Network Wavefunctions for Topological Phases

NASA Astrophysics Data System (ADS)

Ware, Brayden Alexander

The combination of quantum effects and interactions in quantum many-body systems can result in exotic phases with fundamentally entangled ground state wavefunctions--topological phases. Topological phases come in two types, both of which will be studied in this thesis. In topologically ordered phases, the pattern of entanglement in the ground state wavefunction encodes the statistics of exotic emergent excitations, a universal indicator of a phase that is robust to all types of perturbations. In symmetry protected topological phases, the entanglement instead encodes a universal response of the system to symmetry defects, an indicator that is robust only to perturbations respecting the protecting symmetry. Finding and creating these phases in physical systems is a motivating challenge that tests all aspects--analytical, numerical, and experimental--of our understanding of the quantum many-body problem. Nearly three decades ago, the creation of simple ansatz wavefunctions--such as the Laughlin fractional quantum hall state, the AKLT state, and the resonating valence bond state--spurred analytical understanding of both the role of entanglement in topological physics and physical mechanisms by which it can arise. However, quantitative understanding of the relevant phase diagrams is still challenging. For this purpose, tensor networks provide a toolbox for systematically improving wavefunction ansatz while still capturing the relevant entanglement properties. In this thesis, we use the tools of entanglement and tensor networks to analyze ansatz states for several proposed new phases. In the first part, we study a featureless phase of bosons on the honeycomb lattice and argue that this phase can be topologically protected under any one of several distinct subsets of the crystalline lattice symmetries. We discuss methods of detecting such phases with entanglement and without. In the second part, we consider the problem of constructing fixed-point wavefunctions for intrinsically fermionic topological phases, i.e. topological phases contructed out of fermions with a nontrivial response to fermion parity defects. A zero correlation length wavefunction and a commuting projector Hamiltonian that realizes this wavefunction as its ground state are constructed. Using an appropriate generalization of the minimally entangled states method for extraction of topological order from the ground states on a torus to the intrinsically fermionic case, we fully characterize the corresponding topological order as Ising x (px - ipy). We argue that this phase can be captured using fermionic tensor networks, expanding the applicability of tensor network methods.

Pseudo-random dynamic address configuration (PRDAC) algorithm for mobile ad hoc networks

NASA Astrophysics Data System (ADS)

Wu, Shaochuan; Tan, Xuezhi

2007-11-01

By analyzing all kinds of address configuration algorithms, this paper provides a new pseudo-random dynamic address configuration (PRDAC) algorithm for mobile ad hoc networks. Based on PRDAC, the first node that initials this network randomly chooses a nonlinear shift register that can generates an m-sequence. When another node joins this network, the initial node will act as an IP address configuration sever to compute an IP address according to this nonlinear shift register, and then allocates this address and tell the generator polynomial of this shift register to this new node. By this means, when other node joins this network, any node that has obtained an IP address can act as a server to allocate address to this new node. PRDAC can also efficiently avoid IP conflicts and deal with network partition and merge as same as prophet address (PA) allocation and dynamic configuration and distribution protocol (DCDP). Furthermore, PRDAC has less algorithm complexity, less computational complexity and more sufficient assumption than PA. In addition, PRDAC radically avoids address conflicts and maximizes the utilization rate of IP addresses. Analysis and simulation results show that PRDAC has rapid convergence, low overhead and immune from topological structures.

Topological dynamics of vortex-line networks in hexagonal manganites

NASA Astrophysics Data System (ADS)

Xue, Fei; Wang, Nan; Wang, Xueyun; Ji, Yanzhou; Cheong, Sang-Wook; Chen, Long-Qing

2018-01-01

The two-dimensional X Y model is the first well-studied system with topological point defects. On the other hand, although topological line defects are common in three-dimensional systems, the evolution mechanism of line defects is not fully understood. The six domains in hexagonal manganites converge to vortex lines in three dimensions. Using phase-field simulations, we predicted that during the domain coarsening process, the vortex-line network undergoes three types of basic topological changes, i.e., vortex-line loop shrinking, coalescence, and splitting. It is shown that the vortex-antivortex annihilation controls the scaling dynamics.

Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease

PubMed Central

Daianu, Madelaine; Jahanshad, Neda; Nir, Talia M.; Leonardo, Cassandra D.; Jack, Clifford R.; Weiner, Michael W.; Bernstein, Matthew A.; Thompson, Paul M.

2015-01-01

Measures of network topology and connectivity aid the understanding of network breakdown as the brain degenerates in Alzheimer's disease (AD). We analyzed 3-Tesla diffusion-weighted images from 202 patients scanned by the Alzheimer's Disease Neuroimaging Initiative – 50 healthy controls, 72 with early- and 38 with late-stage mild cognitive impairment (eMCI/lMCI) and 42 with AD. Using whole-brain tractography, we reconstructed structural connectivity networks representing connections between pairs of cortical regions. We examined, for the first time in this context, the network's Laplacian matrix and its Fiedler value, describing the network's algebraic connectivity, and the Fiedler vector, used to partition a graph. We assessed algebraic connectivity and four additional supporting metrics, revealing a decrease in network robustness and increasing disarray among nodes as dementia progressed. Network components became more disconnected and segregated, and their modularity increased. These measures are sensitive to diagnostic group differences, and may help understand the complex changes in AD. PMID:26640830

Bribery games on interdependent complex networks.

PubMed

Verma, Prateek; Nandi, Anjan K; Sengupta, Supratim

2018-08-07

Bribe demands present a social conflict scenario where decisions have wide-ranging economic and ethical consequences. Nevertheless, such incidents occur daily in many countries across the globe. Harassment bribery constitute a significant sub-set of such bribery incidents where a government official demands a bribe for providing a service to a citizen legally entitled to it. We employ an evolutionary game-theoretic framework to analyse the evolution of corrupt and honest strategies in structured populations characterized by an interdependent complex network. The effects of changing network topology, average number of links and asymmetry in size of the citizen and officer population on the proliferation of incidents of bribery are explored. A complex network topology is found to be beneficial for the dominance of corrupt strategies over a larger region of phase space when compared with the outcome for a regular network, for equal citizen and officer population sizes. However, the extent of the advantage depends critically on the network degree and topology. A different trend is observed when there is a difference between the citizen and officer population sizes. Under those circumstances, increasing randomness of the underlying citizen network can be beneficial to the fixation of honest officers up to a certain value of the network degree. Our analysis reveals how the interplay between network topology, connectivity and strategy update rules can affect population level outcomes in such asymmetric games. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Reorganization of Human Brain Networks Modulated by Driving Mental Fatigue.

PubMed

Chunlin Zhao; Min Zhao; Yong Yang; Junfeng Gao; Nini Rao; Pan Lin

2017-05-01

The organization of the brain functional network is associated with mental fatigue, but little is known about the brain network topology that is modulated by the mental fatigue. In this study, we used the graph theory approach to investigate reconfiguration changes in functional networks of different electroen-cephalography (EEG) bands from 16 subjects performing a simulated driving task. Behavior and brain functional networks were compared between the normal and driving mental fatigue states. The scores of subjective self-reports indicated that 90 min of simulated driving-induced mental fatigue. We observed that coherence was significantly increased in the frontal, central, and temporal brain regions. Furthermore, in the brain network topology metric, significant increases were observed in the clustering coefficient (Cp) for beta, alpha, and delta bands and the character path length (Lp) for all EEG bands. The normalized measures γ showed significant increases in beta, alpha, and delta bands, and λ showed similar patterns in beta and theta bands. These results indicate that functional network topology can shift the network topology structure toward a more economic but less efficient configuration, which suggests low wiring costs in functional networks and disruption of the effective interactions between and across cortical regions during mental fatigue states. Graph theory analysis might be a useful tool for further understanding the neural mechanisms of driving mental fatigue.

Topological chaos of the spatial prisoner's dilemma game on regular networks.

PubMed

Jin, Weifeng; Chen, Fangyue

2016-02-21

The spatial version of evolutionary prisoner's dilemma on infinitely large regular lattice with purely deterministic strategies and no memories among players is investigated in this paper. Based on the statistical inferences, it is pertinent to confirm that the frequency of cooperation for characterizing its macroscopic behaviors is very sensitive to the initial conditions, which is the most practically significant property of chaos. Its intrinsic complexity is then justified on firm ground from the theory of symbolic dynamics; that is, this game is topologically mixing and possesses positive topological entropy on its subsystems. It is demonstrated therefore that its frequency of cooperation could not be adopted by simply averaging over several steps after the game reaches the equilibrium state. Furthermore, the chaotically changing spatial patterns via empirical observations can be defined and justified in view of symbolic dynamics. It is worth mentioning that the procedure proposed in this work is also applicable to other deterministic spatial evolutionary games therein. Copyright © 2015 Elsevier Ltd. All rights reserved.

Association between resting-state brain network topological organization and creative ability: Evidence from a multiple linear regression model.

PubMed

Jiao, Bingqing; Zhang, Delong; Liang, Aiying; Liang, Bishan; Wang, Zengjian; Li, Junchao; Cai, Yuxuan; Gao, Mengxia; Gao, Zhenni; Chang, Song; Huang, Ruiwang; Liu, Ming

2017-10-01

Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. Copyright © 2017 Elsevier B.V. All rights reserved.

State criminal justice telecommunications (STACOM). Volume 1: Executive summary

NASA Technical Reports Server (NTRS)

Fielding, J. E.; Frewing, H. K.; Lee, J. J.; Leflang, W. G.; Reilly, N. B.

1977-01-01

Techniques for identifying user requirements and network designs for criminal justice networks on a state wide basis are discussed. Topics covered include: methods for determining data required; data collection and survey; data organization procedures, and methods for forecasting network traffic volumes. Developed network design techniques center around a computerized topology program which enables the user to generate least cost network topologies that satisfy network traffic requirements, response time requirements and other specified functional requirements. The developed techniques were applied in Texas and Ohio, and results of these studies are presented.

Go Ahead of Malware’s Infections and Controls: Towards New Techniques for Proactive Cyber Defense

DTIC Science & Technology

2016-12-08

in SDN (such as topology poisoning attacks and data-to-control plan saturation attacks) and developed new defense for SDN (such as TopoGuard and... Poisoning Network Visibility in Software-Defined Networks: New Attacks and Countermeasures As part of our research on discovering new vulnerabilities...future network- ing paradigm. We demonstrate that this new attacks can effectively poison the network topology information, then further successfully

Topology Optimization for Energy Management in Underwater Sensor Networks

DTIC Science & Technology

2015-02-01

1 To appear in International Journal of Control as a regular paper Topology Optimization for Energy Management in Underwater Sensor Networks ⋆ Devesh...K. Jha1 Thomas A. Wettergren2 Asok Ray1 Kushal Mukherjee3 Keywords: Underwater Sensor Network , Energy Management, Pareto Optimization, Adaptation...Optimization for Energy Management in Underwater Sensor Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d

Relating Topological Determinants of Complex Networks to Their Spectral Properties: Structural and Dynamical Effects

NASA Astrophysics Data System (ADS)

Castellano, Claudio; Pastor-Satorras, Romualdo

2017-10-01

The largest eigenvalue of a network's adjacency matrix and its associated principal eigenvector are key elements for determining the topological structure and the properties of dynamical processes mediated by it. We present a physically grounded expression relating the value of the largest eigenvalue of a given network to the largest eigenvalue of two network subgraphs, considered as isolated: the hub with its immediate neighbors and the densely connected set of nodes with maximum K -core index. We validate this formula by showing that it predicts, with good accuracy, the largest eigenvalue of a large set of synthetic and real-world topologies. We also present evidence of the consequences of these findings for broad classes of dynamics taking place on the networks. As a by-product, we reveal that the spectral properties of heterogeneous networks built according to the linear preferential attachment model are qualitatively different from those of their static counterparts.

Learning and innovative elements of strategy adoption rules expand cooperative network topologies.

PubMed

Wang, Shijun; Szalay, Máté S; Zhang, Changshui; Csermely, Peter

2008-04-09

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoner's Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.

Network topology: patterns and mechanisms in plant-herbivore and host-parasitoid food webs.

PubMed

Cagnolo, Luciano; Salvo, Adriana; Valladares, Graciela

2011-03-01

1. Biological communities are organized in complex interaction networks such as food webs, which topology appears to be non-random. Gradients, compartments, nested subsets and even combinations of these structures have been shown in bipartite networks. However, in most studies only one pattern is tested against randomness and mechanistic hypotheses are generally lacking. 2. Here we examined the topology of regional, coexisting plant-herbivore and host-parasitoid food webs to discriminate between the mentioned network patterns. We also evaluated the role of species body size, local abundance, regional frequency and phylogeny as determinants of network topology. 3. We found both food webs to be compartmented, with interaction range boundaries imposed by host phylogeny. Species degree within compartments was mostly related to their regional frequency and local abundance. Only one compartment showed an internal nested structure in the distribution of interactions between species, but species position within this compartment was unrelated to species size or abundance. 4. These results suggest that compartmentalization may be more common than previously considered, and that network structure is a result of multiple, hierarchical, non-exclusive processes. © 2010 The Authors. Journal compilation © 2010 British Ecological Society.

Controllability of flow-conservation networks

NASA Astrophysics Data System (ADS)

Zhao, Chen; Zeng, An; Jiang, Rui; Yuan, Zhengzhong; Wang, Wen-Xu

2017-07-01

The ultimate goal of exploring complex networks is to control them. As such, controllability of complex networks has been intensively investigated. Despite recent advances in studying the impact of a network's topology on its controllability, a comprehensive understanding of the synergistic impact of network topology and dynamics on controllability is still lacking. Here, we explore the controllability of flow-conservation networks, trying to identify the minimal number of driver nodes that can guide the network to any desirable state. We develop a method to analyze the controllability on flow-conservation networks based on exact controllability theory, transforming the original analysis on adjacency matrix to Laplacian matrix. With this framework, we systematically investigate the impact of some key factors of networks, including link density, link directionality, and link polarity, on the controllability of these networks. We also obtain the analytical equations by investigating the network's structural properties approximatively and design the efficient tools. Finally, we consider some real networks with flow dynamics, finding that their controllability is significantly different from that predicted by only considering the topology. These findings deepen our understanding of network controllability with flow-conservation dynamics and provide a general framework to incorporate real dynamics in the analysis of network controllability.

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

PubMed Central

2009-01-01

Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality. PMID:19758426

Personalized anticancer therapy selection using molecular landscape topology and thermodynamics.

PubMed

Rietman, Edward A; Scott, Jacob G; Tuszynski, Jack A; Klement, Giannoula Lakka

2017-03-21

Personalized anticancer therapy requires continuous consolidation of emerging bioinformatics data into meaningful and accurate information streams. The use of novel mathematical and physical approaches, namely topology and thermodynamics can enable merging differing data types for improved accuracy in selecting therapeutic targets. We describe a method that uses chemical thermodynamics and two topology measures to link RNA-seq data from individual patients with academically curated protein-protein interaction networks to select clinically relevant targets for treatment of low-grade glioma (LGG). We show that while these three histologically distinct tumor types (astrocytoma, oligoastrocytoma, and oligodendroglioma) may share potential therapeutic targets, the majority of patients would benefit from more individualized therapies. The method involves computing Gibbs free energy of the protein-protein interaction network and applying a topological filtration on the energy landscape to produce a subnetwork known as persistent homology. We then determine the most likely best target for therapeutic intervention using a topological measure of the network known as Betti number. We describe the algorithm and discuss its application to several patients.

Resonant UPS topologies for the emerging hybrid fiber-coaxial networks

NASA Astrophysics Data System (ADS)

Pinheiro, Humberto

Uninterruptible power supply (UPS) systems have been extensively applied to feed critical loads in many areas. Typical examples of critical loads include life-support equipment, computers and telecommunication systems. Although all UPS systems have a common purpose to provide continuous power-to critical loads, the emerging hybrid fiber-coaxial networks have created the need for specific types of UPS topologies. For example, galvanic isolation for the load and the battery, small size, high input power factor, and trapezoidal output voltage waveforms are among the required features of UPS topologies for hybrid fiber-coaxial networks. None of the conventional UPS topologies meet all these requirements. Consequently. this thesis is directed towards the design and analysis of UPS topologies for this new application. Novel UPS topologies are proposed and control techniques are developed to allow operation at high switching frequencies without penalizing the converter efficiency. By the use of resonant converters in the proposed UPS topologies. a high input power factor is achieved without requiring a dedicated power factor correction stage. In addition, a self-sustained oscillation control method is proposed to ensure soft switching under all operating conditions. A detailed analytical treatment of the resonant converters in the proposed UPS topologies is presented and design procedures illustrated. Simulation and experimental results are presented to validate the analyses and to demonstrate the feasibility of the proposed schemes.

Dynamic weight evolution network with preferential attachment

NASA Astrophysics Data System (ADS)

Dai, Meifeng; Xie, Qi; Li, Lei

2014-12-01

A dynamic weight evolution network with preferential attachment is introduced. The network includes two significant characteristics. (i) Topological growth: triggered by newly added node with M links at each time step, each new edge carries an initial weight growing nonlinearly with time. (ii) Weight dynamics: the weight between two existing nodes experiences increasing or decreasing in a nonlinear way. By using continuum theory and mean-field method, we study the strength, the degree, the weight and their distributions. We find that the distributions exhibit a power-law feature. In particular, the relationship between the degree and the strength is nonlinear, and the power-law exponents of the three are the same. All the theoretical predictions are successfully contrasted with numerical simulations.

Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-Modification.

PubMed

Turner, Alexander P; Caves, Leo S D; Stepney, Susan; Tyrrell, Andy M; Lones, Michael A

2017-01-01

This paper describes the artificial epigenetic network, a recurrent connectionist architecture that is able to dynamically modify its topology in order to automatically decompose and solve dynamical problems. The approach is motivated by the behavior of gene regulatory networks, particularly the epigenetic process of chromatin remodeling that leads to topological change and which underlies the differentiation of cells within complex biological organisms. We expected this approach to be useful in situations where there is a need to switch between different dynamical behaviors, and do so in a sensitive and robust manner in the absence of a priori information about problem structure. This hypothesis was tested using a series of dynamical control tasks, each requiring solutions that could express different dynamical behaviors at different stages within the task. In each case, the addition of topological self-modification was shown to improve the performance and robustness of controllers. We believe this is due to the ability of topological changes to stabilize attractors, promoting stability within a dynamical regime while allowing rapid switching between different regimes. Post hoc analysis of the controllers also demonstrated how the partitioning of the networks could provide new insights into problem structure.

From Sensor Networks to Internet of Things. Bluetooth Low Energy, a Standard for This Evolution

PubMed Central

Hortelano, Diego; Olivares, Teresa; Ruiz, M. Carmen; Garrido-Hidalgo, Celia; López, Vicente

2017-01-01

Current sensor networks need to be improved and updated to satisfy new essential requirements of the Internet of Things, where cutting-edge applications will appear. These requirements are: total coverage, zero fails (high performance), scalability and sustainability (hardware and software). We are going to evaluate Bluetooth Low Energy as wireless transmission technology and as the ideal candidate for these improvements, due to its low power consumption, its low cost radio chips and its ability to communicate with users directly, using their smartphones or smartbands. However, this technology is relatively recent, and standard network topologies are not able to fulfil its new requirements. To address these shortcomings, the implementation of other more flexible topologies (as the mesh topology) will be very interesting. After studying it in depth, we have identified certain weaknesses, for example, specific devices are needed to provide network scalability, and the need to choose between high performance or sustainability. In this paper, after presenting the studies carried out on these new technologies, we propose a new packet format and a new BLE mesh topology, with two different configurations: Individual Mesh and Collaborative Mesh. Our results show how this topology improves the scalability, sustainability, coverage and performance. PMID:28216560

From Sensor Networks to Internet of Things. Bluetooth Low Energy, a Standard for This Evolution.

PubMed

Hortelano, Diego; Olivares, Teresa; Ruiz, M Carmen; Garrido-Hidalgo, Celia; López, Vicente

2017-02-14

Current sensor networks need to be improved and updated to satisfy new essential requirements of the Internet of Things, where cutting-edge applications will appear. These requirements are: total coverage, zero fails (high performance), scalability and sustainability (hardware and software). We are going to evaluate Bluetooth Low Energy as wireless transmission technology and as the ideal candidate for these improvements, due to its low power consumption, its low cost radio chips and its ability to communicate with users directly, using their smartphones or smartbands. However, this technology is relatively recent, and standard network topologies are not able to fulfil its new requirements. To address these shortcomings, the implementation of other more flexible topologies (as the mesh topology) will be very interesting. After studying it in depth, we have identified certain weaknesses, for example, specific devices are needed to provide network scalability, and the need to choose between high performance or sustainability. In this paper, after presenting the studies carried out on these new technologies, we propose a new packet format and a new BLE mesh topology, with two different configurations: Individual Mesh and Collaborative Mesh . Our results show how this topology improves the scalability, sustainability, coverage and performance.

Disrupted brain network functional dynamics and hyper-correlation of structural and functional connectome topology in patients with breast cancer prior to treatment.

PubMed

Kesler, Shelli R; Adams, Marjorie; Packer, Melissa; Rao, Vikram; Henneghan, Ashley M; Blayney, Douglas W; Palesh, Oxana

2017-03-01

Several previous studies have demonstrated that cancer chemotherapy is associated with brain injury and cognitive dysfunction. However, evidence suggests that cancer pathogenesis alone may play a role, even in non-CNS cancers. Using a multimodal neuroimaging approach, we measured structural and functional connectome topology as well as functional network dynamics in newly diagnosed patients with breast cancer. Our study involved a novel, pretreatment assessment that occurred prior to the initiation of any cancer therapies, including surgery with anesthesia. We enrolled 74 patients with breast cancer age 29-65 and 50 frequency-matched healthy female controls who underwent anatomic and resting-state functional MRI as well as cognitive testing. Compared to controls, patients with breast cancer demonstrated significantly lower functional network dynamics ( p  = .046) and cognitive functioning ( p  < .02, corrected). The breast cancer group also showed subtle alterations in structural local clustering and functional local clustering ( p  < .05, uncorrected) as well as significantly increased correlation between structural global clustering and functional global clustering compared to controls ( p  = .03). This hyper-correlation between structural and functional topologies was significantly associated with cognitive dysfunction ( p  = .005). Our findings could not be accounted for by psychological distress and suggest that non-CNS cancer may directly and/or indirectly affect the brain via mechanisms such as tumor-induced neurogenesis, inflammation, and/or vascular changes, for example. Our results also have broader implications concerning the importance of the balance between structural and functional connectome properties as a potential biomarker of general neurologic deficit.

Exact coupling threshold for structural transition reveals diversified behaviors in interconnected networks.

PubMed

Darabi Sahneh, Faryad; Scoglio, Caterina; Van Mieghem, Piet

2015-10-01

An interconnected network features a structural transition between two regimes [F. Radicchi and A. Arenas, Nat. Phys. 9, 717 (2013)]: one where the network components are structurally distinguishable and one where the interconnected network functions as a whole. Our exact solution for the coupling threshold uncovers network topologies with unexpected behaviors. Specifically, we show conditions that superdiffusion, introduced by Gómez et al. [Phys. Rev. Lett. 110, 028701 (2013)], can occur despite the network components functioning distinctly. Moreover, we find that components of certain interconnected network topologies are indistinguishable despite very weak coupling between them.

Retinal vascular segmentation using superpixel-based line operator and its application to vascular topology estimation.

PubMed

Na, Tong; Xie, Jianyang; Zhao, Yitian; Zhao, Yifan; Liu, Yue; Wang, Yongtian; Liu, Jiang

2018-05-09

Automatic methods of analyzing of retinal vascular networks, such as retinal blood vessel detection, vascular network topology estimation, and arteries/veins classification are of great assistance to the ophthalmologist in terms of diagnosis and treatment of a wide spectrum of diseases. We propose a new framework for precisely segmenting retinal vasculatures, constructing retinal vascular network topology, and separating the arteries and veins. A nonlocal total variation inspired Retinex model is employed to remove the image intensity inhomogeneities and relatively poor contrast. For better generalizability and segmentation performance, a superpixel-based line operator is proposed as to distinguish between lines and the edges, thus allowing more tolerance in the position of the respective contours. The concept of dominant sets clustering is adopted to estimate retinal vessel topology and classify the vessel network into arteries and veins. The proposed segmentation method yields competitive results on three public data sets (STARE, DRIVE, and IOSTAR), and it has superior performance when compared with unsupervised segmentation methods, with accuracy of 0.954, 0.957, and 0.964, respectively. The topology estimation approach has been applied to five public databases (DRIVE,STARE, INSPIRE, IOSTAR, and VICAVR) and achieved high accuracy of 0.830, 0.910, 0.915, 0.928, and 0.889, respectively. The accuracies of arteries/veins classification based on the estimated vascular topology on three public databases (INSPIRE, DRIVE and VICAVR) are 0.90.9, 0.910, and 0.907, respectively. The experimental results show that the proposed framework has effectively addressed crossover problem, a bottleneck issue in segmentation and vascular topology reconstruction. The vascular topology information significantly improves the accuracy on arteries/veins classification. © 2018 American Association of Physicists in Medicine.

Energy-efficient pulse-coupled synchronization strategy design for wireless sensor networks through reduced idle listening

PubMed Central

Wang, Yongqiang; Núñez, Felipe; Doyle, Francis J.

2013-01-01

Synchronization is crucial to wireless sensor networks due to their decentralized structure. We propose an energy-efficient pulse-coupled synchronization strategy to achieve this goal. The basic idea is to reduce idle listening by intentionally introducing a large refractory period in the sensors’ cooperation. The large refractory period greatly reduces idle listening in each oscillation period, and is analytically proven to have no influence on the time to synchronization. Hence, it significantly reduces the total energy consumption in a synchronization process. A topology control approach tailored for pulse-coupled synchronization is given to guarantee a k-edge strongly connected interaction topology, which is tolerant to communication-link failures. The topology control approach is totally decentralized and needs no information exchange among sensors, and it is applicable to dynamic network topologies as well. This facilitates a completely decentralized implementation of the synchronization strategy. The strategy is applicable to mobile sensor networks, too. QualNet case studies confirm the effectiveness of the synchronization strategy. PMID:24307831

A toolbox model of evolution of metabolic pathways on networks of arbitrary topology.

PubMed

Pang, Tin Yau; Maslov, Sergei

2011-05-01

In prokaryotic genomes the number of transcriptional regulators is known to be proportional to the square of the total number of protein-coding genes. A toolbox model of evolution was recently proposed to explain this empirical scaling for metabolic enzymes and their regulators. According to its rules, the metabolic network of an organism evolves by horizontal transfer of pathways from other species. These pathways are part of a larger "universal" network formed by the union of all species-specific networks. It remained to be understood, however, how the topological properties of this universal network influence the scaling law of functional content of genomes in the toolbox model. Here we answer this question by first analyzing the scaling properties of the toolbox model on arbitrary tree-like universal networks. We prove that critical branching topology, in which the average number of upstream neighbors of a node is equal to one, is both necessary and sufficient for quadratic scaling. We further generalize the rules of the model to incorporate reactions with multiple substrates/products as well as branched and cyclic metabolic pathways. To achieve its metabolic tasks, the new model employs evolutionary optimized pathways with minimal number of reactions. Numerical simulations of this realistic model on the universal network of all reactions in the KEGG database produced approximately quadratic scaling between the number of regulated pathways and the size of the metabolic network. To quantify the geometrical structure of individual pathways, we investigated the relationship between their number of reactions, byproducts, intermediate, and feedback metabolites. Our results validate and explain the ubiquitous appearance of the quadratic scaling for a broad spectrum of topologies of underlying universal metabolic networks. They also demonstrate why, in spite of "small-world" topology, real-life metabolic networks are characterized by a broad distribution of pathway lengths and sizes of metabolic regulons in regulatory networks.

Towards a unified description of the hydrogen bond network of liquid water: A dynamics based approach

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

Ozkanlar, Abdullah, E-mail: abdullah.ozkanlar@wsu.edu; Zhou, Tiecheng; Clark, Aurora E., E-mail: auclark@wsu.edu

2014-12-07

The definition of a hydrogen bond (H-bond) is intimately related to the topological and dynamic properties of the hydrogen bond network within liquid water. The development of a universal H-bond definition for water is an active area of research as it would remove many ambiguities in the network properties that derive from the fixed definition employed to assign whether a water dimer is hydrogen bonded. This work investigates the impact that an electronic-structure based definition, an energetic, and a geometric definition of the H-bond has upon both topological and dynamic network behavior of simulated water. In each definition, the usemore » of a cutoff (either geometric or energetic) to assign the presence of a H-bond leads to the formation of transiently bonded or broken dimers, which have been quantified within the simulation data. The relative concentration of transient species, and their duration, results in two of the three definitions sharing similarities in either topological or dynamic features (H-bond distribution, H-bond lifetime, etc.), however no two definitions exhibit similar behavior for both classes of network properties. In fact, two networks with similar local network topology (as indicated by similar average H-bonds) can have dramatically different global network topology (as indicated by the defect state distributions) and altered H-bond lifetimes. A dynamics based correction scheme is then used to remove artificially transient H-bonds and to repair artificially broken bonds within the network such that the corrected network exhibits the same structural and dynamic properties for two H-bond definitions (the properties of the third definition being significantly improved). The algorithm described represents a significant step forward in the development of a unified hydrogen bond network whose properties are independent of the original hydrogen bond definition that is employed.« less

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.

Topological Edge Modes in Active Mikado Networks

NASA Astrophysics Data System (ADS)

Zhou, Di; Zhang, Leyou; Mao, Xiaoming

Mechanical properties of disordered fiber networks are not only important in understanding a broad range of natural (such as the cytoskeleton and the extracellular matrix) and manmade materials (such as aerogels and porous media) but also exhibit interesting and rich physics. In this talk, we discuss how topological floppy edge modes can emerge from these fiber networks as a result of active driving. It is known that straight fibers in a network carries a state of self-stress and bears a bulk floppy mode. We find that, interestingly, by driving the network with a tiny perturbation, the bulk modes evolve into edge modes. We introduce a new transfer matrix formulation that can be applied to this strongly disordered system, to characterize the topological edge modes. We also discuss possible implications of these edge modes in biological processes. NSF-DMR-1609051.

Topological data analysis of contagion maps for examining spreading processes on networks.

PubMed

Taylor, Dane; Klimm, Florian; Harrington, Heather A; Kramár, Miroslav; Mischaikow, Konstantin; Porter, Mason A; Mucha, Peter J

2015-07-21

Social and biological contagions are influenced by the spatial embeddedness of networks. Historically, many epidemics spread as a wave across part of the Earth's surface; however, in modern contagions long-range edges-for example, due to airline transportation or communication media-allow clusters of a contagion to appear in distant locations. Here we study the spread of contagions on networks through a methodology grounded in topological data analysis and nonlinear dimension reduction. We construct 'contagion maps' that use multiple contagions on a network to map the nodes as a point cloud. By analysing the topology, geometry and dimensionality of manifold structure in such point clouds, we reveal insights to aid in the modelling, forecast and control of spreading processes. Our approach highlights contagion maps also as a viable tool for inferring low-dimensional structure in networks.

Topological data analysis of contagion maps for examining spreading processes on networks

NASA Astrophysics Data System (ADS)

Taylor, Dane; Klimm, Florian; Harrington, Heather A.; Kramár, Miroslav; Mischaikow, Konstantin; Porter, Mason A.; Mucha, Peter J.

2015-07-01

Social and biological contagions are influenced by the spatial embeddedness of networks. Historically, many epidemics spread as a wave across part of the Earth's surface; however, in modern contagions long-range edges--for example, due to airline transportation or communication media--allow clusters of a contagion to appear in distant locations. Here we study the spread of contagions on networks through a methodology grounded in topological data analysis and nonlinear dimension reduction. We construct `contagion maps' that use multiple contagions on a network to map the nodes as a point cloud. By analysing the topology, geometry and dimensionality of manifold structure in such point clouds, we reveal insights to aid in the modelling, forecast and control of spreading processes. Our approach highlights contagion maps also as a viable tool for inferring low-dimensional structure in networks.

Characterization of topological structure on complex networks.

PubMed

Nakamura, Ikuo

2003-10-01

Characterizing the topological structure of complex networks is a significant problem especially from the viewpoint of data mining on the World Wide Web. "Page rank" used in the commercial search engine Google is such a measure of authority to rank all the nodes matching a given query. We have investigated the page-rank distribution of the real Web and a growing network model, both of which have directed links and exhibit a power law distributions of in-degree (the number of incoming links to the node) and out-degree (the number of outgoing links from the node), respectively. We find a concentration of page rank on a small number of nodes and low page rank on high degree regimes in the real Web, which can be explained by topological properties of the network, e.g., network motifs, and connectivities of nearest neighbors.

A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks

PubMed Central

Lloret, Jaime; Garcia, Miguel; Bri, Diana; Diaz, Juan R.

2009-01-01

A wireless sensor network is a self-configuring network of mobile nodes connected by wireless links where the nodes have limited capacity and energy. In many cases, the application environment requires the design of an exclusive network topology for a particular case. Cluster-based network developments and proposals in existence have been designed to build a network for just one type of node, where all nodes can communicate with any other nodes in their coverage area. Let us suppose a set of clusters of sensor nodes where each cluster is formed by different types of nodes (e.g., they could be classified by the sensed parameter using different transmitting interfaces, by the node profile or by the type of device: laptops, PDAs, sensor etc.) and exclusive networks, as virtual networks, are needed with the same type of sensed data, or the same type of devices, or even the same type of profiles. In this paper, we propose an algorithm that is able to structure the topology of different wireless sensor networks to coexist in the same environment. It allows control and management of the topology of each network. The architecture operation and the protocol messages will be described. Measurements from a real test-bench will show that the designed protocol has low bandwidth consumption and also demonstrates the viability and the scalability of the proposed architecture. Our ccluster-based algorithm is compared with other algorithms reported in the literature in terms of architecture and protocol measurements. PMID:22303185

Fronto-Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue.

PubMed

Taya, Fumihiko; Dimitriadis, Stavros I; Dragomir, Andrei; Lim, Julian; Sun, Yu; Wong, Kian Foong; Thakor, Nitish V; Bezerianos, Anastasios

2018-04-24

Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue. © 2018 Wiley Periodicals, Inc.

Optimal Topology Control and Power Allocation for Minimum Energy Consumption in Consensus Networks

DTIC Science & Technology

2011-12-16

network topologies, such as small world graphs, can greatly increase the convergence rate. In [9], the authors show that nonbipartite Ramanujan graphs...unclassified c . THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 23384 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 60...of iterations necessary to achieve consensus. From this perspec- tive, enforcing a small world, scale-free, or Ramanujan graph topology may not be the

Complexity and dynamics of topological and community structure in complex networks

NASA Astrophysics Data System (ADS)

Berec, Vesna

2017-07-01

Complexity is highly susceptible to variations in the network dynamics, reflected on its underlying architecture where topological organization of cohesive subsets into clusters, system's modular structure and resulting hierarchical patterns, are cross-linked with functional dynamics of the system. Here we study connection between hierarchical topological scales of the simplicial complexes and the organization of functional clusters - communities in complex networks. The analysis reveals the full dynamics of different combinatorial structures of q-th-dimensional simplicial complexes and their Laplacian spectra, presenting spectral properties of resulting symmetric and positive semidefinite matrices. The emergence of system's collective behavior from inhomogeneous statistical distribution is induced by hierarchically ordered topological structure, which is mapped to simplicial complex where local interactions between the nodes clustered into subcomplexes generate flow of information that characterizes complexity and dynamics of the full system.

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity.

PubMed

Leuthaeuser, Janelle B; Knutson, Stacy T; Kumar, Kiran; Babbitt, Patricia C; Fetrow, Jacquelyn S

2015-09-01

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods. © 2015 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Topological Structure of the Space of Phenotypes: The Case of RNA Neutral Networks

PubMed Central

Aguirre, Jacobo; Buldú, Javier M.; Stich, Michael; Manrubia, Susanna C.

2011-01-01

The evolution and adaptation of molecular populations is constrained by the diversity accessible through mutational processes. RNA is a paradigmatic example of biopolymer where genotype (sequence) and phenotype (approximated by the secondary structure fold) are identified in a single molecule. The extreme redundancy of the genotype-phenotype map leads to large ensembles of RNA sequences that fold into the same secondary structure and can be connected through single-point mutations. These ensembles define neutral networks of phenotypes in sequence space. Here we analyze the topological properties of neutral networks formed by 12-nucleotides RNA sequences, obtained through the exhaustive folding of sequence space. A total of 412 sequences fragments into 645 subnetworks that correspond to 57 different secondary structures. The topological analysis reveals that each subnetwork is far from being random: it has a degree distribution with a well-defined average and a small dispersion, a high clustering coefficient, and an average shortest path between nodes close to its minimum possible value, i.e. the Hamming distance between sequences. RNA neutral networks are assortative due to the correlation in the composition of neighboring sequences, a feature that together with the symmetries inherent to the folding process explains the existence of communities. Several topological relationships can be analytically derived attending to structural restrictions and generic properties of the folding process. The average degree of these phenotypic networks grows logarithmically with their size, such that abundant phenotypes have the additional advantage of being more robust to mutations. This property prevents fragmentation of neutral networks and thus enhances the navigability of sequence space. In summary, RNA neutral networks show unique topological properties, unknown to other networks previously described. PMID:22028856

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity

PubMed Central

Leuthaeuser, Janelle B; Knutson, Stacy T; Kumar, Kiran; Babbitt, Patricia C; Fetrow, Jacquelyn S

2015-01-01

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods. PMID:26073648

Genome-Wide Networks of Amino Acid Covariances Are Common among Viruses

PubMed Central

Donlin, Maureen J.; Szeto, Brandon; Gohara, David W.; Aurora, Rajeev

2012-01-01

Coordinated variation among positions in amino acid sequence alignments can reveal genetic dependencies at noncontiguous positions, but methods to assess these interactions are incompletely developed. Previously, we found genome-wide networks of covarying residue positions in the hepatitis C virus genome (R. Aurora, M. J. Donlin, N. A. Cannon, and J. E. Tavis, J. Clin. Invest. 119:225–236, 2009). Here, we asked whether such networks are present in a diverse set of viruses and, if so, what they may imply about viral biology. Viral sequences were obtained for 16 viruses in 13 species from 9 families. The entire viral coding potential for each virus was aligned, all possible amino acid covariances were identified using the observed-minus-expected-squared algorithm at a false-discovery rate of ≤1%, and networks of covariances were assessed using standard methods. Covariances that spanned the viral coding potential were common in all viruses. In all cases, the covariances formed a single network that contained essentially all of the covariances. The hepatitis C virus networks had hub-and-spoke topologies, but all other networks had random topologies with an unusually large number of highly connected nodes. These results indicate that genome-wide networks of genetic associations and the coordinated evolution they imply are very common in viral genomes, that the networks rarely have the hub-and-spoke topology that dominates other biological networks, and that network topologies can vary substantially even within a given viral group. Five examples with hepatitis B virus and poliovirus are presented to illustrate how covariance network analysis can lead to inferences about viral biology. PMID:22238298

On-line training of recurrent neural networks with continuous topology adaptation.

PubMed

Obradovic, D

1996-01-01

This paper presents an online procedure for training dynamic neural networks with input-output recurrences whose topology is continuously adjusted to the complexity of the target system dynamics. This is accomplished by changing the number of the elements of the network hidden layer whenever the existing topology cannot capture the dynamics presented by the new data. The training mechanism is based on the suitably altered extended Kalman filter (EKF) algorithm which is simultaneously used for the network parameter adjustment and for its state estimation. The network consists of a single hidden layer with Gaussian radial basis functions (GRBF), and a linear output layer. The choice of the GRBF is induced by the requirements of the online learning. The latter implies the network architecture which permits only local influence of the new data point in order not to forget the previously learned dynamics. The continuous topology adaptation is implemented in our algorithm to avoid memory and computational problems of using a regular grid of GRBF'S which covers the network input space. Furthermore, we show that the resulting parameter increase can be handled "smoothly" without interfering with the already acquired information. If the target system dynamics are changing over time, we show that a suitable forgetting factor can be used to "unlearn" the no longer-relevant dynamics. The quality of the recurrent network training algorithm is demonstrated on the identification of nonlinear dynamic systems.

Determining root correspondence between previously and newly detected objects

DOEpatents

Paglieroni, David W.; Beer, N Reginald

2014-06-17

A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

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

Self-determined mechanisms in complex networks

NASA Astrophysics Data System (ADS)

Liu, Yang; Yuan, Jian; Shan, Xiuming; Ren, Yong; Ma, Zhengxin

2008-03-01

Self-organized networks are pervasive in communication systems such as the Internet, overlay networks, peer-to-peer networks, and cluster-based services. These networks evolve into complex topologies, under specific driving forces, i.e. user demands, technological innovations, design objectives and so on. Our study focuses on the driving forces behind individual evolutions of network components, and their stimulation and domination to the self-organized networks which are defined as self-determined mechanisms in this paper. Understanding forces underlying the evolution of networks should enable informed design decisions and help to avoid unwanted surprises, such as congestion collapse. A case study on the macroscopic evolution of the Internet topology of autonomous systems under a specific driving force is then presented. Using computer simulations, it is found that the power-law degree distribution can originate from a connection preference to larger numbers of users, and that the small-world property can be caused by rapid growth in the number of users. Our results provide a new feasible perspective to understand intrinsic fundamentals in the topological evolution of complex networks.

Damage Response in Fluid Flow Networks

NASA Astrophysics Data System (ADS)

Gavrilchenko, Tatyana; Katifori, Eleni

The networks found in biological fluid flow systems such as leaf venation and animal vasculature are characterized by hierarchically nested loops. This structure allows the system to be resilient against fluctuations in the flow of fluid and to be robust against damage. We analytically and computationally investigate how this loopy hierarchy determines the extent of disruption in fluid flow in the vicinity of a damage site. Perturbing the network with the removal of a single edge results in the differential flow as a function of distance from the perturbation decaying as a power law. The power law exponent is generally around -2 in 2D, but we find that it varies due to edge effects, initial edge conductivity, and local topology. We expect that these network flow findings, directly applicable to plant and animal veins, will have analogues in electrical grids, traffic flow and other transport networks.

Spatially correlated heterogeneous aspirations to enhance network reciprocity

NASA Astrophysics Data System (ADS)

Tanimoto, Jun; Nakata, Makoto; Hagishima, Aya; Ikegaya, Naoki

2012-02-01

Perc & Wang demonstrated that aspiring to be the fittest under conditions of pairwise strategy updating enhances network reciprocity in structured populations playing 2×2 Prisoner's Dilemma games (Z. Wang, M. Perc, Aspiring to the fittest and promoted of cooperation in the Prisoner's Dilemma game, Physical Review E 82 (2010) 021115; M. Perc, Z. Wang, Heterogeneous aspiration promotes cooperation in the Prisoner's Dilemma game, PLOS one 5 (12) (2010) e15117). Through numerical simulations, this paper shows that network reciprocity is even greater if heterogeneous aspirations are imposed. We also suggest why heterogeneous aspiration fosters network reciprocity. It distributes strategy updating speed among agents in a manner that fortifies the initially allocated cooperators' clusters against invasion. This finding prompted us to further enhance the usual heterogeneous aspiration cases for heterogeneous network topologies. We find that a negative correlation between degree and aspiration level does extend cooperation among heterogeneously structured agents.

Topology of foreign exchange markets using hierarchical structure methods

NASA Astrophysics Data System (ADS)

Naylor, Michael J.; Rose, Lawrence C.; Moyle, Brendan J.

2007-08-01

This paper uses two physics derived hierarchical techniques, a minimal spanning tree and an ultrametric hierarchical tree, to extract a topological influence map for major currencies from the ultrametric distance matrix for 1995-2001. We find that these two techniques generate a defined and robust scale free network with meaningful taxonomy. The topology is shown to be robust with respect to method, to time horizon and is stable during market crises. This topology, appropriately used, gives a useful guide to determining the underlying economic or regional causal relationships for individual currencies and to understanding the dynamics of exchange rate price determination as part of a complex network.

Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality

DOE PAGES

Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.; ...

2015-03-27

Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as ‘topologically important.’ Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termedmore » as ‘functionally important’ genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.« less

Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality

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

Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.

Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as ‘topologically important.’ Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termedmore » as ‘functionally important’ genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.« less

Survivability in Layered Networks

DTIC Science & Technology

2011-02-01

of different lightpath routing formulations. ..... 60 2-7 Probabilty that logical topology becomes disconnected if physical links fail independently...13 14 15 Logical Topology Size Figure 2-7: Probabilty that logical topology becomes disconnected if physical links fail independently with probability

Analysis of radio wave propagation for ISM 2.4 GHz Wireless Sensor Networks in inhomogeneous vegetation environments.

PubMed

Azpilicueta, Leire; López-Iturri, Peio; Aguirre, Erik; Mateo, Ignacio; Astrain, José Javier; Villadangos, Jesús; Falcone, Francisco

2014-12-10

The use of wireless networks has experienced exponential growth due to the improvements in terms of battery life and low consumption of the devices. However, it is compulsory to conduct previous radio propagation analysis when deploying a wireless sensor network. These studies are necessary to perform an estimation of the range coverage, in order to optimize the distance between devices in an actual network deployment. In this work, the radio channel characterization for ISM 2.4 GHz Wireless Sensor Networks (WSNs) in an inhomogeneous vegetation environment has been analyzed. This analysis allows designing environment monitoring tools based on ZigBee and WiFi where WSN and smartphones cooperate, providing rich and customized monitoring information to users in a friendly manner. The impact of topology as well as morphology of the environment is assessed by means of an in-house developed 3D Ray Launching code, to emulate the realistic operation in the framework of the scenario. Experimental results gathered from a measurement campaign conducted by deploying a ZigBee Wireless Sensor Network, are analyzed and compared with simulations in this paper. The scenario where this network is intended to operate is a combination of buildings and diverse vegetation species. To gain insight in the effects of radio propagation, a simplified vegetation model has been developed, considering the material parameters and simplified geometry embedded in the simulation scenario. An initial location-based application has been implemented in a real scenario, to test the functionality within a context aware scenario. The use of deterministic tools can aid to know the impact of the topological influence in the deployment of the optimal Wireless Sensor Network in terms of capacity, coverage and energy consumption, making the use of these systems attractive for multiple applications in inhomogeneous vegetation environments.

A Topology Control Strategy with Reliability Assurance for Satellite Cluster Networks in Earth Observation

PubMed Central

Chen, Qing; Zhang, Jinxiu; Hu, Ze

2017-01-01

This article investigates the dynamic topology control problem of satellite cluster networks (SCNs) in Earth observation (EO) missions by applying a novel metric of stability for inter-satellite links (ISLs). The properties of the periodicity and predictability of satellites’ relative position are involved in the link cost metric which is to give a selection criterion for choosing the most reliable data routing paths. Also, a cooperative work model with reliability is proposed for the situation of emergency EO missions. Based on the link cost metric and the proposed reliability model, a reliability assurance topology control algorithm and its corresponding dynamic topology control (RAT) strategy are established to maximize the stability of data transmission in the SCNs. The SCNs scenario is tested through some numeric simulations of the topology stability of average topology lifetime and average packet loss rate. Simulation results show that the proposed reliable strategy applied in SCNs significantly improves the data transmission performance and prolongs the average topology lifetime. PMID:28241474

A Topology Control Strategy with Reliability Assurance for Satellite Cluster Networks in Earth Observation.

PubMed

Chen, Qing; Zhang, Jinxiu; Hu, Ze

2017-02-23

This article investigates the dynamic topology control problemof satellite cluster networks (SCNs) in Earth observation (EO) missions by applying a novel metric of stability for inter-satellite links (ISLs). The properties of the periodicity and predictability of satellites' relative position are involved in the link cost metric which is to give a selection criterion for choosing the most reliable data routing paths. Also, a cooperative work model with reliability is proposed for the situation of emergency EO missions. Based on the link cost metric and the proposed reliability model, a reliability assurance topology control algorithm and its corresponding dynamic topology control (RAT) strategy are established to maximize the stability of data transmission in the SCNs. The SCNs scenario is tested through some numeric simulations of the topology stability of average topology lifetime and average packet loss rate. Simulation results show that the proposed reliable strategy applied in SCNs significantly improves the data transmission performance and prolongs the average topology lifetime.

A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks

PubMed Central

Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan

2017-01-01

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance. PMID:28471387

A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks.

PubMed

Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan

2017-05-04

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV's parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance.

Asymmetry of Hemispheric Network Topology Reveals Dissociable Processes between Functional and Structural Brain Connectome in Community-Living Elders

PubMed Central

Sun, Yu; Li, Junhua; Suckling, John; Feng, Lei

2017-01-01

Human brain is structurally and functionally asymmetrical and the asymmetries of brain phenotypes have been shown to change in normal aging. Recent advances in graph theoretical analysis have showed topological lateralization between hemispheric networks in the human brain throughout the lifespan. Nevertheless, apparent discrepancies of hemispheric asymmetry were reported between the structural and functional brain networks, indicating the potentially complex asymmetry patterns between structural and functional networks in aging population. In this study, using multimodal neuroimaging (resting-state fMRI and structural diffusion tensor imaging), we investigated the characteristics of hemispheric network topology in 76 (male/female = 15/61, age = 70.08 ± 5.30 years) community-dwelling older adults. Hemispheric functional and structural brain networks were obtained for each participant. Graph theoretical approaches were then employed to estimate the hemispheric topological properties. We found that the optimal small-world properties were preserved in both structural and functional hemispheric networks in older adults. Moreover, a leftward asymmetry in both global and local levels were observed in structural brain networks in comparison with a symmetric pattern in functional brain network, suggesting a dissociable process of hemispheric asymmetry between structural and functional connectome in healthy older adults. Finally, the scores of hemispheric asymmetry in both structural and functional networks were associated with behavioral performance in various cognitive domains. Taken together, these findings provide new insights into the lateralized nature of multimodal brain connectivity, highlight the potentially complex relationship between structural and functional brain network alterations, and augment our understanding of asymmetric structural and functional specializations in normal aging. PMID:29209197

Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia.

PubMed

Lo, Chun-Yi Zac; Su, Tsung-Wei; Huang, Chu-Chung; Hung, Chia-Chun; Chen, Wei-Ling; Lan, Tsuo-Hung; Lin, Ching-Po; Bullmore, Edward T

2015-07-21

Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population.

On the contributions of topological features to transcriptional regulatory network robustness

PubMed Central

2012-01-01

Background Because biological networks exhibit a high-degree of robustness, a systemic understanding of their architecture and function requires an appraisal of the network design principles that confer robustness. In this project, we conduct a computational study of the contribution of three degree-based topological properties (transcription factor-target ratio, degree distribution, cross-talk suppression) and their combinations on the robustness of transcriptional regulatory networks. We seek to quantify the relative degree of robustness conferred by each property (and combination) and also to determine the extent to which these properties alone can explain the robustness observed in transcriptional networks. Results To study individual properties and their combinations, we generated synthetic, random networks that retained one or more of the three properties with values derived from either the yeast or E. coli gene regulatory networks. Robustness of these networks were estimated through simulation. Our results indicate that the combination of the three properties we considered explains the majority of the structural robustness observed in the real transcriptional networks. Surprisingly, scale-free degree distribution is, overall, a minor contributor to robustness. Instead, most robustness is gained through topological features that limit the complexity of the overall network and increase the transcription factor subnetwork sparsity. Conclusions Our work demonstrates that (i) different types of robustness are implemented by different topological aspects of the network and (ii) size and sparsity of the transcription factor subnetwork play an important role for robustness induction. Our results are conserved across yeast and E Coli, which suggests that the design principles examined are present within an array of living systems. PMID:23194062

Discrete-event simulation of a wide-area health care network.

PubMed Central

McDaniel, J G

1995-01-01

OBJECTIVE: Predict the behavior and estimate the telecommunication cost of a wide-area message store-and-forward network for health care providers that uses the telephone system. DESIGN: A tool with which to perform large-scale discrete-event simulations was developed. Network models for star and mesh topologies were constructed to analyze the differences in performances and telecommunication costs. The distribution of nodes in the network models approximates the distribution of physicians, hospitals, medical labs, and insurers in the Province of Saskatchewan, Canada. Modeling parameters were based on measurements taken from a prototype telephone network and a survey conducted at two medical clinics. Simulation studies were conducted for both topologies. RESULTS: For either topology, the telecommunication cost of a network in Saskatchewan is projected to be less than $100 (Canadian) per month per node. The estimated telecommunication cost of the star topology is approximately half that of the mesh. Simulations predict that a mean end-to-end message delivery time of two hours or less is achievable at this cost. A doubling of the data volume results in an increase of less than 50% in the mean end-to-end message transfer time. CONCLUSION: The simulation models provided an estimate of network performance and telecommunication cost in a specific Canadian province. At the expected operating point, network performance appeared to be relatively insensitive to increases in data volume. Similar results might be anticipated in other rural states and provinces in North America where a telephone-based network is desired. PMID:7583646

Spatial correlation between chemical and topological defects in vitreous silica: UV-resonance Raman study

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

Saito, M., E-mail: makina.saito@elettra.eu; D’Amico, F.; Bencivenga, F.

2014-06-28

A spatial correlation between chemical and topological defects in the tetrahedron network in vitreous silica produced by a fusion process of natural quartz crystals was found by synchrotron-based UV resonance Raman experiments. Furthermore, a quantitative correlation between these defects was obtained by comparing visible Raman and UV absorption spectra. These results indicate that in vitreous silica produced by the fusion process the topological defects disturb the surrounding tetrahedral silica network and induce further disorder regions with sub nanometric sizes.

Reactive immunization on complex networks

NASA Astrophysics Data System (ADS)

Alfinito, Eleonora; Beccaria, Matteo; Fachechi, Alberto; Macorini, Guido

2017-01-01

Epidemic spreading on complex networks depends on the topological structure as well as on the dynamical properties of the infection itself. Generally speaking, highly connected individuals play the role of hubs and are crucial to channel information across the network. On the other hand, static topological quantities measuring the connectivity structure are independent of the dynamical mechanisms of the infection. A natural question is therefore how to improve the topological analysis by some kind of dynamical information that may be extracted from the ongoing infection itself. In this spirit, we propose a novel vaccination scheme that exploits information from the details of the infection pattern at the moment when the vaccination strategy is applied. Numerical simulations of the infection process show that the proposed immunization strategy is effective and robust on a wide class of complex networks.

Topological networks for quantum communication between distant qubits

NASA Astrophysics Data System (ADS)

Lang, Nicolai; Büchler, Hans Peter

2017-11-01

Efficient communication between qubits relies on robust networks, which allow for fast and coherent transfer of quantum information. It seems natural to harvest the remarkable properties of systems characterized by topological invariants to perform this task. Here, we show that a linear network of coupled bosonic degrees of freedom, characterized by topological bands, can be employed for the efficient exchange of quantum information over large distances. Important features of our setup are that it is robust against quenched disorder, all relevant operations can be performed by global variations of parameters, and the time required for communication between distant qubits approaches linear scaling with their distance. We demonstrate that our concept can be extended to an ensemble of qubits embedded in a two-dimensional network to allow for communication between all of them.

Non-consensus opinion model with a neutral view on complex networks

NASA Astrophysics Data System (ADS)

Tian, Zihao; Dong, Gaogao; Du, Ruijin; Ma, Jing

2016-05-01

A nonconsensus opinion (NCO) model was introduced recently, which allows the stable coexistence of minority and majority opinions. However, due ​to disparities in the knowledge, experiences, and personality or self-protection of agents, they often remain ​neutral when faced with some opinions in real scenarios. ​To address this issue, we propose a general non-consensus opinion model with neutral view (NCON) ​and we define the dynamic opinion ​change process. We applied the NCON model to different topological networks and studied the formation of opinion clusters. In the case of random graphs, random regular networks, and scale-free (SF) networks, we found that the system moved from a continuous phase transition to a discontinuous phase transition as the connectivity density and exponent of the SF network λ ​decreased and increased in the steady state, respectively. Moreover, the initial proportions of neutral opinions were found to have little effect on the proportional structure of opinions at the steady state. These results suggest that the majority choice between positive and negative opinions depends on the initial proportion of each opinion. The NCON model may have potential applications for decision makers.

Effects of Different Correlation Metrics and Preprocessing Factors on Small-World Brain Functional Networks: A Resting-State Functional MRI Study

PubMed Central

Liang, Xia; Wang, Jinhui; Yan, Chaogan; Shu, Ni; Xu, Ke; Gong, Gaolang; He, Yong

2012-01-01

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01–0.027 Hz) versus slow-4 (0.027–0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the “best” network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027–0.073 Hz band exhibited greater reliability than those in the 0.01–0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies. PMID:22412922

From brain to earth and climate systems: small-world interaction networks or not?

PubMed

Bialonski, Stephan; Horstmann, Marie-Therese; Lehnertz, Klaus

2010-03-01

We consider recent reports on small-world topologies of interaction networks derived from the dynamics of spatially extended systems that are investigated in diverse scientific fields such as neurosciences, geophysics, or meteorology. With numerical simulations that mimic typical experimental situations, we have identified an important constraint when characterizing such networks: indications of a small-world topology can be expected solely due to the spatial sampling of the system along with the commonly used time series analysis based approaches to network characterization.

Semantic networks based on titles of scientific papers

NASA Astrophysics Data System (ADS)

Pereira, H. B. B.; Fadigas, I. S.; Senna, V.; Moret, M. A.

2011-03-01

In this paper we study the topological structure of semantic networks based on titles of papers published in scientific journals. It discusses its properties and presents some reflections on how the use of social and complex network models can contribute to the diffusion of knowledge. The proposed method presented here is applied to scientific journals where the titles of papers are in English or in Portuguese. We show that the topology of studied semantic networks are small-world and scale-free.

On Adding Structure to Unstructured Overlay Networks

NASA Astrophysics Data System (ADS)

Leitão, João; Carvalho, Nuno A.; Pereira, José; Oliveira, Rui; Rodrigues, Luís

Unstructured peer-to-peer overlay networks are very resilient to churn and topology changes, while requiring little maintenance cost. Therefore, they are an infrastructure to build highly scalable large-scale services in dynamic networks. Typically, the overlay topology is defined by a peer sampling service that aims at maintaining, in each process, a random partial view of peers in the system. The resulting random unstructured topology is suboptimal when a specific performance metric is considered. On the other hand, structured approaches (for instance, a spanning tree) may optimize a given target performance metric but are highly fragile. In fact, the cost for maintaining structures with strong constraints may easily become prohibitive in highly dynamic networks. This chapter discusses different techniques that aim at combining the advantages of unstructured and structured networks. Namely we focus on two distinct approaches, one based on optimizing the overlay and another based on optimizing the gossip mechanism itself.

The topological requirements for robust perfect adaptation in networks of any size.

PubMed

Araujo, Robyn P; Liotta, Lance A

2018-05-01

Robustness, and the ability to function and thrive amid changing and unfavorable environments, is a fundamental requirement for living systems. Until now it has been an open question how large and complex biological networks can exhibit robust behaviors, such as perfect adaptation to a variable stimulus, since complexity is generally associated with fragility. Here we report that all networks that exhibit robust perfect adaptation (RPA) to a persistent change in stimulus are decomposable into well-defined modules, of which there exist two distinct classes. These two modular classes represent a topological basis for all RPA-capable networks, and generate the full set of topological realizations of the internal model principle for RPA in complex, self-organizing, evolvable bionetworks. This unexpected result supports the notion that evolutionary processes are empowered by simple and scalable modular design principles that promote robust performance no matter how large or complex the underlying networks become.

The topology of large-scale structure. I - Topology and the random phase hypothesis. [galactic formation models

NASA Technical Reports Server (NTRS)

Weinberg, David H.; Gott, J. Richard, III; Melott, Adrian L.

1987-01-01

Many models for the formation of galaxies and large-scale structure assume a spectrum of random phase (Gaussian), small-amplitude density fluctuations as initial conditions. In such scenarios, the topology of the galaxy distribution on large scales relates directly to the topology of the initial density fluctuations. Here a quantitative measure of topology - the genus of contours in a smoothed density distribution - is described and applied to numerical simulations of galaxy clustering, to a variety of three-dimensional toy models, and to a volume-limited sample of the CfA redshift survey. For random phase distributions the genus of density contours exhibits a universal dependence on threshold density. The clustering simulations show that a smoothing length of 2-3 times the mass correlation length is sufficient to recover the topology of the initial fluctuations from the evolved galaxy distribution. Cold dark matter and white noise models retain a random phase topology at shorter smoothing lengths, but massive neutrino models develop a cellular topology.

A network analysis of the Chinese stock market

NASA Astrophysics Data System (ADS)

Huang, Wei-Qiang; Zhuang, Xin-Tian; Yao, Shuang

2009-07-01

In many practical important cases, a massive dataset can be represented as a very large network with certain attributes associated with its vertices and edges. Stock markets generate huge amounts of data, which can be use for constructing the network reflecting the market’s behavior. In this paper, we use a threshold method to construct China’s stock correlation network and then study the network’s structural properties and topological stability. We conduct a statistical analysis of this network and show that it follows a power-law model. We also detect components, cliques and independent sets in this network. These analyses allows one to apply a new data mining technique of classifying financial instruments based on stock price data, which provides a deeper insight into the internal structure of the stock market. Moreover, we test the topological stability of this network and find that it displays a topological robustness against random vertex failures, but it is also fragile to intentional attacks. Such a network stability property would be also useful for portfolio investment and risk management.

The geometric nature of weights in real complex networks

NASA Astrophysics Data System (ADS)

Allard, Antoine; Serrano, M. Ángeles; García-Pérez, Guillermo; Boguñá, Marián

2017-01-01

The topology of many real complex networks has been conjectured to be embedded in hidden metric spaces, where distances between nodes encode their likelihood of being connected. Besides of providing a natural geometrical interpretation of their complex topologies, this hypothesis yields the recipe for sustainable Internet's routing protocols, sheds light on the hierarchical organization of biochemical pathways in cells, and allows for a rich characterization of the evolution of international trade. Here we present empirical evidence that this geometric interpretation also applies to the weighted organization of real complex networks. We introduce a very general and versatile model and use it to quantify the level of coupling between their topology, their weights and an underlying metric space. Our model accurately reproduces both their topology and their weights, and our results suggest that the formation of connections and the assignment of their magnitude are ruled by different processes.

Structure-topology-property correlations of sodium phosphosilicate glasses.

PubMed

Hermansen, Christian; Guo, Xiaoju; Youngman, Randall E; Mauro, John C; Smedskjaer, Morten M; Yue, Yuanzheng

2015-08-14

In this work, we investigate the correlations among structure, topology, and properties in a series of sodium phosphosilicate glasses with [SiO2]/[SiO2 + P2O5] ranging from 0 to 1. The network structure is characterized by (29)Si and (31)P magic-angle spinning nuclear magnetic resonance and Raman spectroscopy. The results show the formation of six-fold coordinated silicon species in phosphorous-rich glasses. Based on the structural data, we propose a formation mechanism of the six-fold coordinated silicon, which is used to develop a quantitative structural model for predicting the speciation of the network forming units as a function of chemical composition. The structural model is then used to establish a temperature-dependent constraint description of phosphosilicate glass topology that enables prediction of glass transition temperature, liquid fragility, and indentation hardness. The topological constraint model provides insight into structural origin of the mixed network former effect in phosphosilicate glasses.

Hemispheric Asymmetry of Human Brain Anatomical Network Revealed by Diffusion Tensor Tractography

PubMed Central

Liu, Yaou; Duan, Yunyun; Li, Kuncheng

2015-01-01

The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. However, few studies have investigated the hemispheric asymmetries of the human brain from the perspective of the network model, and little is known about the asymmetries of the connection patterns of brain regions, which may reflect the functional integration and interaction between different regions. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 72 right-handed healthy adult subjects. We established the existence of structural connections between any pair of the 90 cortical and subcortical regions using deterministic tractography. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties, such as degree of connectivity, characteristic path length, and betweenness centrality. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries. Combined with the previous studies, we suggested that the topological asymmetries in the anatomical network may reflect the functional lateralization of the human brain. PMID:26539535

Suppression of anomalous synchronization and nonstationary behavior of neural network under small-world topology

NASA Astrophysics Data System (ADS)

Boaretto, B. R. R.; Budzinski, R. C.; Prado, T. L.; Kurths, J.; Lopes, S. R.

2018-05-01

It is known that neural networks under small-world topology can present anomalous synchronization and nonstationary behavior for weak coupling regimes. Here, we propose methods to suppress the anomalous synchronization and also to diminish the nonstationary behavior occurring in weakly coupled neural network under small-world topology. We consider a network of 2000 thermally sensitive identical neurons, based on the model of Hodgkin-Huxley in a small-world topology, with the probability of adding non local connection equal to p = 0 . 001. Based on experimental protocols to suppress anomalous synchronization, as well as nonstationary behavior of the neural network dynamics, we make use of (i) external stimulus (pulsed current); (ii) biologic parameters changing (neuron membrane conductance changes); and (iii) body temperature changes. Quantification analysis to evaluate phase synchronization makes use of the Kuramoto's order parameter, while recurrence quantification analysis, particularly the determinism, computed over the easily accessible mean field of network, the local field potential (LFP), is used to evaluate nonstationary states. We show that the methods proposed can control the anomalous synchronization and nonstationarity occurring for weak coupling parameter without any effect on the individual neuron dynamics, neither in the expected asymptotic synchronized states occurring for large values of the coupling parameter.

Development of Human Brain Structural Networks Through Infancy and Childhood

PubMed Central

Huang, Hao; Shu, Ni; Mishra, Virendra; Jeon, Tina; Chalak, Lina; Wang, Zhiyue J.; Rollins, Nancy; Gong, Gaolang; Cheng, Hua; Peng, Yun; Dong, Qi; He, Yong

2015-01-01

During human brain development through infancy and childhood, microstructural and macrostructural changes take place to reshape the brain's structural networks and better adapt them to sophisticated functional and cognitive requirements. However, structural topological configuration of the human brain during this specific development period is not well understood. In this study, diffusion magnetic resonance image (dMRI) of 25 neonates, 13 toddlers, and 25 preadolescents were acquired to characterize network dynamics at these 3 landmark cross-sectional ages during early childhood. dMRI tractography was used to construct human brain structural networks, and the underlying topological properties were quantified by graph-theory approaches. Modular organization and small-world attributes are evident at birth with several important topological metrics increasing monotonically during development. Most significant increases of regional nodes occur in the posterior cingulate cortex, which plays a pivotal role in the functional default mode network. Positive correlations exist between nodal efficiencies and fractional anisotropy of the white matter traced from these nodes, while correlation slopes vary among the brain regions. These results reveal substantial topological reorganization of human brain structural networks through infancy and childhood, which is likely to be the outcome of both heterogeneous strengthening of the major white matter tracts and pruning of other axonal fibers. PMID:24335033

AlignNemo: a local network alignment method to integrate homology and topology.

PubMed

Ciriello, Giovanni; Mina, Marco; Guzzi, Pietro H; Cannataro, Mario; Guerra, Concettina

2012-01-01

Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo.

Inclusion of Topological Measurements into Analytic Estimates of Effective Permeability in Fractured Media

NASA Astrophysics Data System (ADS)

Sævik, P. N.; Nixon, C. W.

2017-11-01

We demonstrate how topology-based measures of connectivity can be used to improve analytical estimates of effective permeability in 2-D fracture networks, which is one of the key parameters necessary for fluid flow simulations at the reservoir scale. Existing methods in this field usually compute fracture connectivity using the average fracture length. This approach is valid for ideally shaped, randomly distributed fractures, but is not immediately applicable to natural fracture networks. In particular, natural networks tend to be more connected than randomly positioned fractures of comparable lengths, since natural fractures often terminate in each other. The proposed topological connectivity measure is based on the number of intersections and fracture terminations per sampling area, which for statistically stationary networks can be obtained directly from limited outcrop exposures. To evaluate the method, numerical permeability upscaling was performed on a large number of synthetic and natural fracture networks, with varying topology and geometry. The proposed method was seen to provide much more reliable permeability estimates than the length-based approach, across a wide range of fracture patterns. We summarize our results in a single, explicit formula for the effective permeability.

Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market.

PubMed

Long, Haiming; Zhang, Ji; Tang, Nengyu

2017-01-01

This study considers the effect of an industry's network topology on its systemic risk contribution to the stock market using data from the CSI 300 two-tier industry indices from the Chinese stock market. We first measure industry's conditional-value-at-risk (CoVaR) and the systemic risk contribution (ΔCoVaR) using the fitted time-varying t-copula function. The network of the stock industry is established based on dynamic conditional correlations with the minimum spanning tree. Then, we investigate the connection characteristics and topology of the network. Finally, we utilize seemingly unrelated regression estimation (SUR) of panel data to analyze the relationship between network topology of the stock industry and the industry's systemic risk contribution. The results show that the systemic risk contribution of small-scale industries such as real estate, food and beverage, software services, and durable goods and clothing, is higher than that of large-scale industries, such as banking, insurance and energy. Industries with large betweenness centrality, closeness centrality, and clustering coefficient and small node occupancy layer are associated with greater systemic risk contribution. In addition, further analysis using a threshold model confirms that the results are robust.

EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis

NASA Astrophysics Data System (ADS)

Fraschini, Matteo; Demuru, Matteo; Hillebrand, Arjan; Cuccu, Lorenza; Porcu, Silvia; di Stefano, Francesca; Puligheddu, Monica; Floris, Gianluca; Borghero, Giuseppe; Marrosu, Francesco

2016-12-01

Amyotrophic Lateral Sclerosis (ALS) is one of the most severe neurodegenerative diseases, which is known to affect upper and lower motor neurons. In contrast to the classical tenet that ALS represents the outcome of extensive and progressive impairment of a fixed set of motor connections, recent neuroimaging findings suggest that the disease spreads along vast non-motor connections. Here, we hypothesised that functional network topology is perturbed in ALS, and that this reorganization is associated with disability. We tested this hypothesis in 21 patients affected by ALS at several stages of impairment using resting-state electroencephalography (EEG) and compared the results to 16 age-matched healthy controls. We estimated functional connectivity using the Phase Lag Index (PLI), and characterized the network topology using the minimum spanning tree (MST). We found a significant difference between groups in terms of MST dissimilarity and MST leaf fraction in the beta band. Moreover, some MST parameters (leaf, hierarchy and kappa) significantly correlated with disability. These findings suggest that the topology of resting-state functional networks in ALS is affected by the disease in relation to disability. EEG network analysis may be of help in monitoring and evaluating the clinical status of ALS patients.

A stochastic and dynamical view of pluripotency in mouse embryonic stem cells

PubMed Central

Lee, Esther J.

2018-01-01

Pluripotent embryonic stem cells are of paramount importance for biomedical sciences because of their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory networks. The rapid growth of single-cell sequencing data has greatly stimulated applications of statistical and machine learning methods for inferring topologies of pluripotency regulating genetic networks. The inferred network topologies, however, often only encode Boolean information while remaining silent about the roles of dynamics and molecular stochasticity inherent in gene expression. Herein we develop a framework for systematically extending Boolean-level network topologies into higher resolution models of networks which explicitly account for the promoter architectures and gene state switching dynamics. We show the framework to be useful for disentangling the various contributions that gene switching, external signaling, and network topology make to the global heterogeneity and dynamics of transcription factor populations. We find the pluripotent state of the network to be a steady state which is robust to global variations of gene switching rates which we argue are a good proxy for epigenetic states of individual promoters. The temporal dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the rates of genetic switching which makes cells more responsive to changes in extracellular signals. PMID:29451874

Graph theoretical modeling of baby brain networks.

PubMed

Zhao, Tengda; Xu, Yuehua; He, Yong

2018-06-12

The human brain undergoes explosive growth during the prenatal period and the first few postnatal years, establishing an early infrastructure for the later development of behaviors and cognitions. Revealing the developmental rules during the early phrase is essential in understanding the emergence of brain function and the origin of developmental disorders. The graph-theoretical network modeling in combination with multiple neuroimaging probes provides an important research framework to explore early development of the topological wiring and organizational paradigms of the brain. Here, we reviewed studies which employed neuroimaging and graph-theoretical modeling to investigate brain network development from approximately 20 gestational weeks to 2 years of age. Specifically, the structural and functional brain networks have evolved to highly efficient topological architectures in the early stage; where the structural network remains ahead and paves the way for the development of functional network. The brain network develops in a heterogeneous order, from primary to higher-order systems and from a tendency of network segregation to network integration in the prenatal and postnatal periods. The early brain network topologies show abilities in predicting certain cognitive and behavior performance in later life, and their impairments are likely to continue into childhood and even adulthood. These macroscopic topological changes are found to be associated with possible microstructural maturations, such as axonal growth and myelinations. Collectively, this review provides a detailed delineation of the early changes of the baby brains in the graph-theoretical modeling framework, which opens up a new avenue to understand the developmental principles of the connectome. Copyright © 2018. Published by Elsevier Inc.

Fault-Tolerant Algorithms for Connectivity Restoration in Wireless Sensor Networks.

PubMed

Zeng, Yali; Xu, Li; Chen, Zhide

2015-12-22

As wireless sensor network (WSN) is often deployed in a hostile environment, nodes in the networks are prone to large-scale failures, resulting in the network not working normally. In this case, an effective restoration scheme is needed to restore the faulty network timely. Most of existing restoration schemes consider more about the number of deployed nodes or fault tolerance alone, but fail to take into account the fact that network coverage and topology quality are also important to a network. To address this issue, we present two algorithms named Full 2-Connectivity Restoration Algorithm (F2CRA) and Partial 3-Connectivity Restoration Algorithm (P3CRA), which restore a faulty WSN in different aspects. F2CRA constructs the fan-shaped topology structure to reduce the number of deployed nodes, while P3CRA constructs the dual-ring topology structure to improve the fault tolerance of the network. F2CRA is suitable when the restoration cost is given the priority, and P3CRA is suitable when the network quality is considered first. Compared with other algorithms, these two algorithms ensure that the network has stronger fault-tolerant function, larger coverage area and better balanced load after the restoration.

The use of nodes attributes in social network analysis with an application to an international trade network

NASA Astrophysics Data System (ADS)

de Andrade, Ricardo Lopes; Rêgo, Leandro Chaves

2018-02-01

The social network analysis (SNA) studies the interactions among actors in a network formed through some relationship (friendship, cooperation, trade, among others). The SNA is constantly approached from a binary point of view, i.e., it is only observed if a link between two actors is present or not regardless of the strength of this link. It is known that different information can be obtained in weighted and unweighted networks and that the information extracted from weighted networks is more accurate and detailed. Another rarely discussed approach in the SNA is related to the individual attributes of the actors (nodes), because such analysis is usually focused on the topological structure of networks. Features of the nodes are not incorporated in the SNA what implies that there is some loss or misperception of information in those analyze. This paper aims at exploring more precisely the complexities of a social network, initially developing a method that inserts the individual attributes in the topological structure of the network and then analyzing the network in four different ways: unweighted, edge-weighted and two methods for using both edge-weights and nodes' attributes. The international trade network was chosen in the application of this approach, where the nodes represent the countries, the links represent the cash flow in the trade transactions and countries' GDP were chosen as nodes' attributes. As a result, it is possible to observe which countries are most connected in the world economy and with higher cash flows, to point out the countries that are central to the intermediation of the wealth flow and those that are most benefited from being included in this network. We also made a principal component analysis to study which metrics are more influential in describing the data variability, which turn out to be mostly the weighted metrics which include the nodes' attributes.

Counting motifs in dynamic networks.

PubMed

Mukherjee, Kingshuk; Hasan, Md Mahmudul; Boucher, Christina; Kahveci, Tamer

2018-04-11

A network motif is a sub-network that occurs frequently in a given network. Detection of such motifs is important since they uncover functions and local properties of the given biological network. Finding motifs is however a computationally challenging task as it requires solving the costly subgraph isomorphism problem. Moreover, the topology of biological networks change over time. These changing networks are called dynamic biological networks. As the network evolves, frequency of each motif in the network also changes. Computing the frequency of a given motif from scratch in a dynamic network as the network topology evolves is infeasible, particularly for large and fast evolving networks. In this article, we design and develop a scalable method for counting the number of motifs in a dynamic biological network. Our method incrementally updates the frequency of each motif as the underlying network's topology evolves. Our experiments demonstrate that our method can update the frequency of each motif in orders of magnitude faster than counting the motif embeddings every time the network changes. If the network evolves more frequently, the margin with which our method outperforms the existing static methods, increases. We evaluated our method extensively using synthetic and real datasets, and show that our method is highly accurate(≥ 96%) and that it can be scaled to large dense networks. The results on real data demonstrate the utility of our method in revealing interesting insights on the evolution of biological processes.

Effect of Field Spread on Resting-State Magneto Encephalography Functional Network Analysis: A Computational Modeling Study.

PubMed

Silva Pereira, Silvana; Hindriks, Rikkert; Mühlberg, Stefanie; Maris, Eric; van Ede, Freek; Griffa, Alessandra; Hagmann, Patric; Deco, Gustavo

2017-11-01

A popular way to analyze resting-state electroencephalography (EEG) and magneto encephalography (MEG) data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel time series and the network connections. Although conceptually appealing, the network-theoretical approach to sensor-level EEG and MEG data is challenged by the fact that EEG and MEG time series are mixtures of source activity. It is, therefore, of interest to assess the relationship between functional networks of source activity and the ensuing sensor-level networks. Since these topological features are of high interest in experimental studies, we address the question of to what extent the network topology can be reconstructed from sensor-level functional connectivity (FC) measures in case of MEG data. Simple simulations that consider only a small number of regions do not allow to assess network properties; therefore, we use a diffusion magnetic resonance imaging-constrained whole-brain computational model of resting-state activity. Our motivation lies behind the fact that still many contributions found in the literature perform network analysis at sensor level, and we aim at showing the discrepancies between source- and sensor-level network topologies by using realistic simulations of resting-state cortical activity. Our main findings are that the effect of field spread on network topology depends on the type of interaction (instantaneous or lagged) and leads to an underestimation of lagged FC at sensor level due to instantaneous mixing of cortical signals, instantaneous interaction is more sensitive to field spread than lagged interaction, and discrepancies are reduced when using planar gradiometers rather than axial gradiometers. We, therefore, recommend using lagged interaction measures on planar gradiometer data when investigating network properties of resting-state sensor-level MEG data.

Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

NASA Astrophysics Data System (ADS)

Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

2015-11-01

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug-target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain.

Going End to End to Deliver High-Speed Data

NASA Technical Reports Server (NTRS)

2005-01-01

By the end of the 1990s, the optical fiber "backbone" of the telecommunication and data-communication networks had evolved from megabits-per-second transmission rates to gigabits-per-second transmission rates. Despite this boom in bandwidth, however, users at the end nodes were still not being reached on a consistent basis. (An end node is any device that does not behave like a router or a managed hub or switch. Examples of end node objects are computers, printers, serial interface processor phones, and unmanaged hubs and switches.) The primary reason that prevents bandwidth from reaching the end nodes is the complex local network topology that exists between the optical backbone and the end nodes. This complex network topology consists of several layers of routing and switch equipment which introduce potential congestion points and network latency. By breaking down the complex network topology, a true optical connection can be achieved. Access Optical Networks, Inc., is making this connection a reality with guidance from NASA s nondestructive evaluation experts.

Dynamical Interplay between Awareness and Epidemic Spreading in Multiplex Networks

NASA Astrophysics Data System (ADS)

Granell, Clara; Gómez, Sergio; Arenas, Alex

2013-09-01

We present the analysis of the interrelation between two processes accounting for the spreading of an epidemic, and the information awareness to prevent its infection, on top of multiplex networks. This scenario is representative of an epidemic process spreading on a network of persistent real contacts, and a cyclic information awareness process diffusing in the network of virtual social contacts between the same individuals. The topology corresponds to a multiplex network where two diffusive processes are interacting affecting each other. The analysis using a microscopic Markov chain approach reveals the phase diagram of the incidence of the epidemics and allows us to capture the evolution of the epidemic threshold depending on the topological structure of the multiplex and the interrelation with the awareness process. Interestingly, the critical point for the onset of the epidemics has a critical value (metacritical point) defined by the awareness dynamics and the topology of the virtual network, from which the onset increases and the epidemics incidence decreases.

Dynamical interplay between awareness and epidemic spreading in multiplex networks.

PubMed

Granell, Clara; Gómez, Sergio; Arenas, Alex

2013-09-20

We present the analysis of the interrelation between two processes accounting for the spreading of an epidemic, and the information awareness to prevent its infection, on top of multiplex networks. This scenario is representative of an epidemic process spreading on a network of persistent real contacts, and a cyclic information awareness process diffusing in the network of virtual social contacts between the same individuals. The topology corresponds to a multiplex network where two diffusive processes are interacting affecting each other. The analysis using a microscopic Markov chain approach reveals the phase diagram of the incidence of the epidemics and allows us to capture the evolution of the epidemic threshold depending on the topological structure of the multiplex and the interrelation with the awareness process. Interestingly, the critical point for the onset of the epidemics has a critical value (metacritical point) defined by the awareness dynamics and the topology of the virtual network, from which the onset increases and the epidemics incidence decreases.

APHiD: Hierarchical Task Placement to Enable a Tapered Fat Tree Topology for Lower Power and Cost in HPC Networks

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

Michelogiannakis, George; Ibrahim, Khaled Z.; Shalf, John

The power and procurement cost of bandwidth in system-wide networks has forced a steady drop in the byte/flop ratio. This trend of computation becoming faster relative to the network is expected to hold. In this paper, we explore how cost-oriented task placement enables reducing the cost of system-wide networks by enabling high performance even on tapered topologies where more bandwidth is provisioned at lower levels. We describe APHiD, an efficient hierarchical placement algorithm that uses new techniques to improve the quality of heuristic solutions and reduces the demand on high-level, expensive bandwidth in hierarchical topologies. We apply APHiD to amore » tapered fat-tree, demonstrating that APHiD maintains application scalability even for severely tapered network configurations. Using simulation, we show that for tapered networks APHiD improves performance by more than 50% over random placement and even 15% in some cases over costlier, state-of-the-art placement algorithms.« less

Dynamic Evolution of Financial Network and its Relation to Economic Crises

NASA Astrophysics Data System (ADS)

Gao, Ya-Chun; Wei, Zong-Wen; Wang, Bing-Hong

2013-02-01

The static topology properties of financial networks have been widely investigated since the work done by Mantegna, yet their dynamic evolution with time is little considered. In this paper, we comprehensively study the dynamic evolution of financial network by a sliding window technique. The vertices and edges of financial network are represented by the stocks from S&P500 components and correlations between pairs of daily returns of price fluctuation, respectively. Furthermore, the duration of stock price fluctuation, spanning from January 4, 1985 to September 14, 2009, makes us to carefully observe the relation between the dynamic topological properties and big financial crashes. The empirical results suggest that the financial network has the robust small-world property when the time evolves, and the topological structure drastically changes when the big financial crashes occur. This correspondence between the dynamic evolution of financial network and big financial crashes may provide a novel view to understand the origin of economic crisis.

Effects of topologies on signal propagation in feedforward networks

NASA Astrophysics Data System (ADS)

Zhao, Jia; Qin, Ying-Mei; Che, Yan-Qiu

2018-01-01

We systematically investigate the effects of topologies on signal propagation in feedforward networks (FFNs) based on the FitzHugh-Nagumo neuron model. FFNs with different topological structures are constructed with same number of both in-degrees and out-degrees in each layer and given the same input signal. The propagation of firing patterns and firing rates are found to be affected by the distribution of neuron connections in the FFNs. Synchronous firing patterns emerge in the later layers of FFNs with identical, uniform, and exponential degree distributions, but the number of synchronous spike trains in the output layers of the three topologies obviously differs from one another. The firing rates in the output layers of the three FFNs can be ordered from high to low according to their topological structures as exponential, uniform, and identical distributions, respectively. Interestingly, the sequence of spiking regularity in the output layers of the three FFNs is consistent with the firing rates, but their firing synchronization is in the opposite order. In summary, the node degree is an important factor that can dramatically influence the neuronal network activity.

Effects of topologies on signal propagation in feedforward networks.

PubMed

Zhao, Jia; Qin, Ying-Mei; Che, Yan-Qiu

2018-01-01

We systematically investigate the effects of topologies on signal propagation in feedforward networks (FFNs) based on the FitzHugh-Nagumo neuron model. FFNs with different topological structures are constructed with same number of both in-degrees and out-degrees in each layer and given the same input signal. The propagation of firing patterns and firing rates are found to be affected by the distribution of neuron connections in the FFNs. Synchronous firing patterns emerge in the later layers of FFNs with identical, uniform, and exponential degree distributions, but the number of synchronous spike trains in the output layers of the three topologies obviously differs from one another. The firing rates in the output layers of the three FFNs can be ordered from high to low according to their topological structures as exponential, uniform, and identical distributions, respectively. Interestingly, the sequence of spiking regularity in the output layers of the three FFNs is consistent with the firing rates, but their firing synchronization is in the opposite order. In summary, the node degree is an important factor that can dramatically influence the neuronal network activity.

Discriminating topology in galaxy distributions using network analysis

NASA Astrophysics Data System (ADS)

Hong, Sungryong; Coutinho, Bruno C.; Dey, Arjun; Barabási, Albert-L.; Vogelsberger, Mark; Hernquist, Lars; Gebhardt, Karl

2016-07-01

The large-scale distribution of galaxies is generally analysed using the two-point correlation function. However, this statistic does not capture the topology of the distribution, and it is necessary to resort to higher order correlations to break degeneracies. We demonstrate that an alternate approach using network analysis can discriminate between topologically different distributions that have similar two-point correlations. We investigate two galaxy point distributions, one produced by a cosmological simulation and the other by a Lévy walk. For the cosmological simulation, we adopt the redshift z = 0.58 slice from Illustris and select galaxies with stellar masses greater than 108 M⊙. The two-point correlation function of these simulated galaxies follows a single power law, ξ(r) ˜ r-1.5. Then, we generate Lévy walks matching the correlation function and abundance with the simulated galaxies. We find that, while the two simulated galaxy point distributions have the same abundance and two-point correlation function, their spatial distributions are very different; most prominently, filamentary structures, absent in Lévy fractals. To quantify these missing topologies, we adopt network analysis tools and measure diameter, giant component, and transitivity from networks built by a conventional friends-of-friends recipe with various linking lengths. Unlike the abundance and two-point correlation function, these network quantities reveal a clear separation between the two simulated distributions; therefore, the galaxy distribution simulated by Illustris is not a Lévy fractal quantitatively. We find that the described network quantities offer an efficient tool for discriminating topologies and for comparing observed and theoretical distributions.

Organizational topology of brain and its relationship to ADHD in adolescents with d-transposition of the great arteries.

PubMed

Schmithorst, Vincent J; Panigrahy, Ashok; Gaynor, J William; Watson, Christopher G; Lee, Vince; Bellinger, David C; Rivkin, Michael J; Newburger, Jane W

2016-08-01

Little is currently known about the impact of congenital heart disease (CHD) on the organization of large-scale brain networks in relation to neurobehavioral outcome. We investigated whether CHD might impact ADHD symptoms via changes in brain structural network topology in a cohort of adolescents with d-transposition of the great arteries (d-TGA) repaired with the arterial switch operation in early infancy and referent subjects. We also explored whether these effects might be modified by apolipoprotein E (APOE) genotype, as the APOE ε2 allele has been associated with worse neurodevelopmental outcomes after repair of d-TGA in infancy. We applied graph analysis techniques to diffusion tensor imaging (DTI) data obtained from 47 d-TGA adolescents and 29 healthy referents to construct measures of structural topology at the global and regional levels. We developed statistical mediation models revealing the respective contributions of d-TGA, APOE genotype, and structural network topology on ADHD outcome as measured by the Connors ADHD/DSM-IV Scales (CADS). Changes in overall network connectivity, integration, and segregation mediated worse ADHD outcomes in d-TGA patients compared to healthy referents; these changes were predominantly in the left and right intrahemispheric regional subnetworks. Exploratory analysis revealed that network topology also mediated detrimental effects of the APOE ε4 allele but improved neurobehavioral outcomes for the APOE ε2 allele. Our results suggest that disruption of organization of large-scale networks may contribute to neurobehavioral dysfunction in adolescents with CHD and that this effect may interact with APOE genotype.

Graph theoretical analysis reveals disrupted topological properties of whole brain functional networks in temporal lobe epilepsy.

PubMed

Wang, Junjing; Qiu, Shijun; Xu, Yong; Liu, Zhenyin; Wen, Xue; Hu, Xiangshu; Zhang, Ruibin; Li, Meng; Wang, Wensheng; Huang, Ruiwang

2014-09-01

Temporal lobe epilepsy (TLE) is one of the most common forms of drug-resistant epilepsy. Previous studies have indicated that the TLE-related impairments existed in extensive local functional networks. However, little is known about the alterations in the topological properties of whole brain functional networks. In this study, we acquired resting-state BOLD-fMRI (rsfMRI) data from 26 TLE patients and 25 healthy controls, constructed their whole brain functional networks, compared the differences in topological parameters between the TLE patients and the controls, and analyzed the correlation between the altered topological properties and the epilepsy duration. The TLE patients showed significant increases in clustering coefficient and characteristic path length, but significant decrease in global efficiency compared to the controls. We also found altered nodal parameters in several regions in the TLE patients, such as the bilateral angular gyri, left middle temporal gyrus, right hippocampus, triangular part of left inferior frontal gyrus, left inferior parietal but supramarginal and angular gyri, and left parahippocampus gyrus. Further correlation analysis showed that the local efficiency of the TLE patients correlated positively with the epilepsy duration. Our results indicated the disrupted topological properties of whole brain functional networks in TLE patients. Our findings indicated the TLE-related impairments in the whole brain functional networks, which may help us to understand the clinical symptoms of TLE patients and offer a clue for the diagnosis and treatment of the TLE patients. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Hierarchical sequencing of online social graphs

NASA Astrophysics Data System (ADS)

Andjelković, Miroslav; Tadić, Bosiljka; Maletić, Slobodan; Rajković, Milan

2015-10-01

In online communications, patterns of conduct of individual actors and use of emotions in the process can lead to a complex social graph exhibiting multilayered structure and mesoscopic communities. Using simplicial complexes representation of graphs, we investigate in-depth topology of the online social network constructed from MySpace dialogs which exhibits original community structure. A simulation of emotion spreading in this network leads to the identification of two emotion-propagating layers. Three topological measures are introduced, referred to as the structure vectors, which quantify graph's architecture at different dimension levels. Notably, structures emerging through shared links, triangles and tetrahedral faces, frequently occur and range from tree-like to maximal 5-cliques and their respective complexes. On the other hand, the structures which spread only negative or only positive emotion messages appear to have much simpler topology consisting of links and triangles. The node's structure vector represents the number of simplices at each topology level in which the node resides and the total number of such simplices determines what we define as the node's topological dimension. The presented results suggest that the node's topological dimension provides a suitable measure of the social capital which measures the actor's ability to act as a broker in compact communities, the so called Simmelian brokerage. We also generalize the results to a wider class of computer-generated networks. Investigating components of the node's vector over network layers reveals that same nodes develop different socio-emotional relations and that the influential nodes build social capital by combining their connections in different layers.

Effects of stream topology on ecological community results from neutral models

EPA Science Inventory

While neutral theory and models have stimulated considerable literature, less well investigated is the effect of topology on neutral metacommunity model simulations. We implemented a neutral metacommunity model using two different stream network topologies, a widely branched netw...

Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

PubMed Central

Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

2013-01-01

Motivation: Most functions within the cell emerge thanks to protein–protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable. Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions. Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction. Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. Availability: https://sites.google.com/site/carlovittoriocannistraci/home Contact: kalokagathos.agon@gmail.com or timothy.ravasi@kaust.edu.sa Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23812985

Revealing catastrophic failure of leaf networks under stress

PubMed Central

Brodribb, Timothy J.; Bienaimé, Diane; Marmottant, Philippe

2016-01-01

The intricate patterns of veins that adorn the leaves of land plants are among the most important networks in biology. Water flows in these leaf irrigation networks under tension and is vulnerable to embolism-forming cavitations, which cut off water supply, ultimately causing leaf death. Understanding the ways in which plants structure their vein supply network to protect against embolism-induced failure has enormous ecological and evolutionary implications, but until now there has been no way of observing dynamic failure in natural leaf networks. Here we use a new optical method that allows the initiation and spread of embolism bubbles in the leaf network to be visualized. Examining embolism-induced failure of architecturally diverse leaf networks, we found that conservative rules described the progression of hydraulic failure within veins. The most fundamental rule was that within an individual venation network, susceptibility to embolism always increased proportionally with the size of veins, and initial nucleation always occurred in the largest vein. Beyond this general framework, considerable diversity in the pattern of network failure was found between species, related to differences in vein network topology. The highest-risk network was found in a fern species, where single events caused massive disruption to leaf water supply, whereas safer networks in angiosperm leaves contained veins with composite properties, allowing a staged failure of water supply. These results reveal how the size structure of leaf venation is a critical determinant of the spread of embolism damage to leaves during drought. PMID:27071104

Revealing catastrophic failure of leaf networks under stress.

PubMed

Brodribb, Timothy J; Bienaimé, Diane; Marmottant, Philippe

2016-04-26

The intricate patterns of veins that adorn the leaves of land plants are among the most important networks in biology. Water flows in these leaf irrigation networks under tension and is vulnerable to embolism-forming cavitations, which cut off water supply, ultimately causing leaf death. Understanding the ways in which plants structure their vein supply network to protect against embolism-induced failure has enormous ecological and evolutionary implications, but until now there has been no way of observing dynamic failure in natural leaf networks. Here we use a new optical method that allows the initiation and spread of embolism bubbles in the leaf network to be visualized. Examining embolism-induced failure of architecturally diverse leaf networks, we found that conservative rules described the progression of hydraulic failure within veins. The most fundamental rule was that within an individual venation network, susceptibility to embolism always increased proportionally with the size of veins, and initial nucleation always occurred in the largest vein. Beyond this general framework, considerable diversity in the pattern of network failure was found between species, related to differences in vein network topology. The highest-risk network was found in a fern species, where single events caused massive disruption to leaf water supply, whereas safer networks in angiosperm leaves contained veins with composite properties, allowing a staged failure of water supply. These results reveal how the size structure of leaf venation is a critical determinant of the spread of embolism damage to leaves during drought.

Resilient Wireless Sensor Networks Using Topology Control: A Review

PubMed Central

Huang, Yuanjiang; Martínez, José-Fernán; Sendra, Juana; López, Lourdes

2015-01-01

Wireless sensor networks (WSNs) may be deployed in failure-prone environments, and WSNs nodes easily fail due to unreliable wireless connections, malicious attacks and resource-constrained features. Nevertheless, if WSNs can tolerate at most losing k − 1 nodes while the rest of nodes remain connected, the network is called k − connected. k is one of the most important indicators for WSNs’ self-healing capability. Following a WSN design flow, this paper surveys resilience issues from the topology control and multi-path routing point of view. This paper provides a discussion on transmission and failure models, which have an important impact on research results. Afterwards, this paper reviews theoretical results and representative topology control approaches to guarantee WSNs to be k − connected at three different network deployment stages: pre-deployment, post-deployment and re-deployment. Multi-path routing protocols are discussed, and many NP-complete or NP-hard problems regarding topology control are identified. The challenging open issues are discussed at the end. This paper can serve as a guideline to design resilient WSNs. PMID:26404272

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.

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.

Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain

PubMed Central

Xia, Mingrui; Lin, Qixiang; Bi, Yanchao; He, Yong

2016-01-01

White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brain's network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain's hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes. PMID:27148015

Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain.

PubMed

Xia, Mingrui; Lin, Qixiang; Bi, Yanchao; He, Yong

2016-01-01

White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brain's network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain's hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.

Topological Alterations of the Intrinsic Brain Network in Patients with Functional Dyspepsia.

PubMed

Nan, Jiaofen; Zhang, Li; Zhu, Fubao; Tian, Xiaorui; Zheng, Qian; Deneen, Karen M von; Liu, Jixin; Zhang, Ming

2016-01-31

Previous studies reported that integrated information in the brain ultimately determines the subjective experience of patients with chronic pain, but how the information is integrated in the brain connectome of functional dyspepsia (FD) patients remains largely unclear. The study aimed to quantify the topological changes of the brain network in FD patients. Small-world properties, network efficiency and nodal centrality were utilized to measure the changes in topological architecture in 25 FD patients and 25 healthy controls based on functional magnetic resonance imaging. Pearson's correlation assessed the relationship of each topological property with clinical symptoms. FD patients showed an increase of clustering coefficients and local efficiency relative to controls from the perspective of a whole network as well as elevated nodal centrality in the right orbital part of the inferior frontal gyrus, left anterior cingulate gyrus and left hippocampus, and decreased nodal centrality in the right posterior cingulate gyrus, left cuneus, right putamen, left middle occipital gyrus and right inferior occipital gyrus. Moreover, the centrality in the anterior cingulate gyrus was significantly associated with symptom severity and duration in FD patients. Nevertheless, the inclusion of anxiety and depression scores as covariates erased the group differences in nodal centralities in the orbital part of the inferior frontal gyrus and hippocampus. The results suggest topological disruption of the functional brain networks in FD patients, presumably in response to disturbances of sensory information integrated with emotion, memory, pain modulation, and selective attention in patients.

Long-Term Effects of Attentional Performance on Functional Brain Network Topology

PubMed Central

Breckel, Thomas P. K.; Thiel, Christiane M.; Bullmore, Edward T.; Zalesky, Andrew; Patel, Ameera X.; Giessing, Carsten

2013-01-01

Individuals differ in their cognitive resilience. Less resilient people demonstrate a greater tendency to vigilance decrements within sustained attention tasks. We hypothesized that a period of sustained attention is followed by prolonged changes in the organization of “resting state” brain networks and that individual differences in cognitive resilience are related to differences in post-task network reorganization. We compared the topological and spatial properties of brain networks as derived from functional MRI data (N = 20) recorded for 6 mins before and 12 mins after the performance of an attentional task. Furthermore we analysed changes in brain topology during task performance and during the switches between rest and task conditions. The cognitive resilience of each individual was quantified as the rate of increase in response latencies over the 32-minute time course of the attentional paradigm. On average, functional networks measured immediately post-task demonstrated significant and prolonged changes in network organization compared to pre-task networks with higher connectivity strength, more clustering, less efficiency, and shorter distance connections. Individual differences in cognitive resilience were significantly correlated with differences in the degree of recovery of some network parameters. Changes in network measures were still present in less resilient individuals in the second half of the post-task period (i.e. 6–12 mins after task completion), while resilient individuals already demonstrated significant reductions of functional connectivity and clustering towards pre-task levels. During task performance brain topology became more integrated with less clustering and higher global efficiency, but linearly decreased with ongoing time-on-task. We conclude that sustained attentional task performance has prolonged, “hang-over” effects on the organization of post-task resting-state brain networks; and that more cognitively resilient individuals demonstrate faster rates of network recovery following a period of attentional effort. PMID:24040185

Cellular automata simulation of topological effects on the dynamics of feed-forward motifs

PubMed Central

Apte, Advait A; Cain, John W; Bonchev, Danail G; Fong, Stephen S

2008-01-01

Background Feed-forward motifs are important functional modules in biological and other complex networks. The functionality of feed-forward motifs and other network motifs is largely dictated by the connectivity of the individual network components. While studies on the dynamics of motifs and networks are usually devoted to the temporal or spatial description of processes, this study focuses on the relationship between the specific architecture and the overall rate of the processes of the feed-forward family of motifs, including double and triple feed-forward loops. The search for the most efficient network architecture could be of particular interest for regulatory or signaling pathways in biology, as well as in computational and communication systems. Results Feed-forward motif dynamics were studied using cellular automata and compared with differential equation modeling. The number of cellular automata iterations needed for a 100% conversion of a substrate into a target product was used as an inverse measure of the transformation rate. Several basic topological patterns were identified that order the specific feed-forward constructions according to the rate of dynamics they enable. At the same number of network nodes and constant other parameters, the bi-parallel and tri-parallel motifs provide higher network efficacy than single feed-forward motifs. Additionally, a topological property of isodynamicity was identified for feed-forward motifs where different network architectures resulted in the same overall rate of the target production. Conclusion It was shown for classes of structural motifs with feed-forward architecture that network topology affects the overall rate of a process in a quantitatively predictable manner. These fundamental results can be used as a basis for simulating larger networks as combinations of smaller network modules with implications on studying synthetic gene circuits, small regulatory systems, and eventually dynamic whole-cell models. PMID:18304325

ClueNet: Clustering a temporal network based on topological similarity rather than denseness.

PubMed

Crawford, Joseph; Milenković, Tijana

2018-01-01

Network clustering is a very popular topic in the network science field. Its goal is to divide (partition) the network into groups (clusters or communities) of "topologically related" nodes, where the resulting topology-based clusters are expected to "correlate" well with node label information, i.e., metadata, such as cellular functions of genes/proteins in biological networks, or age or gender of people in social networks. Even for static data, the problem of network clustering is complex. For dynamic data, the problem is even more complex, due to an additional dimension of the data-their temporal (evolving) nature. Since the problem is computationally intractable, heuristic approaches need to be sought. Existing approaches for dynamic network clustering (DNC) have drawbacks. First, they assume that nodes should be in the same cluster if they are densely interconnected within the network. We hypothesize that in some applications, it might be of interest to cluster nodes that are topologically similar to each other instead of or in addition to requiring the nodes to be densely interconnected. Second, they ignore temporal information in their early steps, and when they do consider this information later on, they do so implicitly. We hypothesize that capturing temporal information earlier in the clustering process and doing so explicitly will improve results. We test these two hypotheses via our new approach called ClueNet. We evaluate ClueNet against six existing DNC methods on both social networks capturing evolving interactions between individuals (such as interactions between students in a high school) and biological networks capturing interactions between biomolecules in the cell at different ages. We find that ClueNet is superior in over 83% of all evaluation tests. As more real-world dynamic data are becoming available, DNC and thus ClueNet will only continue to gain importance.

Network topology mapper

DOEpatents

Quist, Daniel A [Los Alamos, NM; Gavrilov, Eugene M [Los Alamos, NM; Fisk, Michael E [Jemez, NM

2008-01-15

A method enables the topology of an acyclic fully propagated network to be discovered. A list of switches that comprise the network is formed and the MAC address cache for each one of the switches is determined. For each pair of switches, from the MAC address caches the remaining switches that see the pair of switches are located. For each pair of switches the remaining switches are determined that see one of the pair of switches on a first port and the second one of the pair of switches on a second port. A list of insiders is formed for every pair of switches. It is determined whether the insider for each pair of switches is a graph edge and adjacent ones of the graph edges are determined. A symmetric adjacency matrix is formed from the graph edges to represent the topology of the data link network.

Topological Vulnerability Analysis

NASA Astrophysics Data System (ADS)

Jajodia, Sushil; Noel, Steven

Traditionally, network administrators rely on labor-intensive processes for tracking network configurations and vulnerabilities. This requires a great deal of expertise, and is error prone because of the complexity of networks and associated security data. The interdependencies of network vulnerabilities make traditional point-wise vulnerability analysis inadequate. We describe a Topological Vulnerability Analysis (TVA) approach that analyzes vulnerability dependencies and shows all possible attack paths into a network. From models of the network vulnerabilities and potential attacker exploits, we compute attack graphs that convey the impact of individual and combined vulnerabilities on overall security. TVA finds potential paths of vulnerability through a network, showing exactly how attackers may penetrate a network. From this, we identify key vulnerabilities and provide strategies for protection of critical network assets.

Nexus networks in carbon honeycombs

NASA Astrophysics Data System (ADS)

Chen, Yuanping; Xie, Yuee; Gao, Yan; Chang, Po-Yao; Zhang, Shengbai; Vanderbilt, David

2018-04-01

Nexus metals represent a new type of topological material in which nodal lines merge at nexus points. Here we propose novel networks in nexus systems through intertwining between nexus fermions and additional nodal lines. These nexus networks can be realized in recently synthesized carbon honeycomb materials. In these carbon honeycombs, we demonstrate a phase transition between a nexus network and a system with triply degenerate points and additional nodal lines. The Landau level spectra show unusual magnetic transport properties in the nexus networks. Our results pave the way toward realizations of new topological materials with novel transport properties beyond standard Weyl/Dirac semimetals.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Estimating Topology of Discrete Dynamical Networks

NASA Astrophysics Data System (ADS)

Guo, Shu-Juan; Fu, Xin-Chu

2010-07-01

In this paper, by applying Lasalle's invariance principle and some results about the trace of a matrix, we propose a method for estimating the topological structure of a discrete dynamical network based on the dynamical evolution of the network. The network concerned can be directed or undirected, weighted or unweighted, and the local dynamics of each node can be nonidentical. The connections among the nodes can be all unknown or partially known. Finally, two examples, including a Hénon map and a central network, are illustrated to verify the theoretical results.

Analysis of Road Network Pattern Considering Population Distribution and Central Business District

PubMed Central

Zhao, Fangxia; Sun, Huijun; Wu, Jianjun; Gao, Ziyou; Liu, Ronghui

2016-01-01

This paper proposes a road network growing model with the consideration of population distribution and central business district (CBD) attraction. In the model, the relative neighborhood graph (RNG) is introduced as the connection mechanism to capture the characteristics of road network topology. The simulation experiment is set up to illustrate the effects of population distribution and CBD attraction on the characteristics of road network. Moreover, several topological attributes of road network is evaluated by using coverage, circuitness, treeness and total length in the experiment. Finally, the suggested model is verified in the simulation of China and Beijing Highway networks. PMID:26981857

Brain Network Analysis: Separating Cost from Topology Using Cost-Integration

PubMed Central

Ginestet, Cedric E.; Nichols, Thomas E.; Bullmore, Ed T.; Simmons, Andrew

2011-01-01

A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the reporting of (i) differences in weighted costs and (ii) differences in cost-integrated topological measures with respect to different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures. Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero, when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences, which could be masked by cost-integration. PMID:21829437

Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma

PubMed Central

Liu, Li; Dinu, Valentin

2018-01-01

Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway’s topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher’s exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov–Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level. PMID:29666752

Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma.

PubMed

Li, Chaoxing; Liu, Li; Dinu, Valentin

2018-01-01

Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway's topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher's exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov-Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level.

High speed all optical networks

NASA Technical Reports Server (NTRS)

Chlamtac, Imrich; Ganz, Aura

1990-01-01

An inherent problem of conventional point-to-point wide area network (WAN) architectures is that they cannot translate optical transmission bandwidth into comparable user available throughput due to the limiting electronic processing speed of the switching nodes. The first solution to wavelength division multiplexing (WDM) based WAN networks that overcomes this limitation is presented. The proposed Lightnet architecture takes into account the idiosyncrasies of WDM switching/transmission leading to an efficient and pragmatic solution. The Lightnet architecture trades the ample WDM bandwidth for a reduction in the number of processing stages and a simplification of each switching stage, leading to drastically increased effective network throughputs. The principle of the Lightnet architecture is the construction and use of virtual topology networks, embedded in the original network in the wavelength domain. For this construction Lightnets utilize the new concept of lightpaths which constitute the links of the virtual topology. Lightpaths are all-optical, multihop, paths in the network that allow data to be switched through intermediate nodes using high throughput passive optical switches. The use of the virtual topologies and the associated switching design introduce a number of new ideas, which are discussed in detail.

3D Filament Network Segmentation with Multiple Active Contours

NASA Astrophysics Data System (ADS)

Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

2014-03-01

Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and microtubules. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we developed a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D TIRF Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy.

Topology of Innovation Spaces in the Knowledge Networks Emerging through Questions-And-Answers

PubMed Central

Andjelković, Miroslav; Tadić, Bosiljka; Mitrović Dankulov, Marija; Rajković, Milan; Melnik, Roderick

2016-01-01

The communication processes of knowledge creation represent a particular class of human dynamics where the expertise of individuals plays a substantial role, thus offering a unique possibility to study the structure of knowledge networks from online data. Here, we use the empirical evidence from questions-and-answers in mathematics to analyse the emergence of the network of knowledge contents (or tags) as the individual experts use them in the process. After removing extra edges from the network-associated graph, we apply the methods of algebraic topology of graphs to examine the structure of higher-order combinatorial spaces in networks for four consecutive time intervals. We find that the ranking distributions of the suitably scaled topological dimensions of nodes fall into a unique curve for all time intervals and filtering levels, suggesting a robust architecture of knowledge networks. Moreover, these networks preserve the logical structure of knowledge within emergent communities of nodes, labeled according to a standard mathematical classification scheme. Further, we investigate the appearance of new contents over time and their innovative combinations, which expand the knowledge network. In each network, we identify an innovation channel as a subgraph of triangles and larger simplices to which new tags attach. Our results show that the increasing topological complexity of the innovation channels contributes to network’s architecture over different time periods, and is consistent with temporal correlations of the occurrence of new tags. The methodology applies to a wide class of data with the suitable temporal resolution and clearly identified knowledge-content units. PMID:27171149

Robustness and Vulnerability of Networks with Dynamical Dependency Groups.

PubMed

Bai, Ya-Nan; Huang, Ning; Wang, Lei; Wu, Zhi-Xi

2016-11-28

The dependency property and self-recovery of failure nodes both have great effects on the robustness of networks during the cascading process. Existing investigations focused mainly on the failure mechanism of static dependency groups without considering the time-dependency of interdependent nodes and the recovery mechanism in reality. In this study, we present an evolving network model consisting of failure mechanisms and a recovery mechanism to explore network robustness, where the dependency relations among nodes vary over time. Based on generating function techniques, we provide an analytical framework for random networks with arbitrary degree distribution. In particular, we theoretically find that an abrupt percolation transition exists corresponding to the dynamical dependency groups for a wide range of topologies after initial random removal. Moreover, when the abrupt transition point is above the failure threshold of dependency groups, the evolving network with the larger dependency groups is more vulnerable; when below it, the larger dependency groups make the network more robust. Numerical simulations employing the Erdős-Rényi network and Barabási-Albert scale free network are performed to validate our theoretical results.

Hodge Decomposition of Information Flow on Small-World Networks.

PubMed

Haruna, Taichi; Fujiki, Yuuya

2016-01-01

We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow.

A novel game theoretic approach for modeling competitive information diffusion in social networks with heterogeneous nodes

NASA Astrophysics Data System (ADS)

Agha Mohammad Ali Kermani, Mehrdad; Fatemi Ardestani, Seyed Farshad; Aliahmadi, Alireza; Barzinpour, Farnaz

2017-01-01

Influence maximization deals with identification of the most influential nodes in a social network given an influence model. In this paper, a game theoretic framework is developed that models a competitive influence maximization problem. A novel competitive influence model is additionally proposed that incorporates user heterogeneity, message content, and network structure. The proposed game-theoretic model is solved using Nash Equilibrium in a real-world dataset. It is shown that none of the well-known strategies are stable and at least one player has the incentive to deviate from the proposed strategy. Moreover, violation of Nash equilibrium strategy by each player leads to their reduced payoff. Contrary to previous works, our results demonstrate that graph topology, as well as the nodes' sociability and initial tendency measures have an effect on the determination of the influential node in the network.

A new class of finite-time nonlinear consensus protocols for multi-agent systems

NASA Astrophysics Data System (ADS)

Zuo, Zongyu; Tie, Lin

2014-02-01

This paper is devoted to investigating the finite-time consensus problem for a multi-agent system in networks with undirected topology. A new class of global continuous time-invariant consensus protocols is constructed for each single-integrator agent dynamics with the aid of Lyapunov functions. In particular, it is shown that the settling time of the proposed new class of finite-time consensus protocols is upper bounded for arbitrary initial conditions. This makes it possible for network consensus problems that the convergence time is designed and estimated offline for a given undirected information flow and a group volume of agents. Finally, a numerical simulation example is presented as a proof of concept.

Distributed robust finite-time nonlinear consensus protocols for multi-agent systems

NASA Astrophysics Data System (ADS)

Zuo, Zongyu; Tie, Lin

2016-04-01

This paper investigates the robust finite-time consensus problem of multi-agent systems in networks with undirected topology. Global nonlinear consensus protocols augmented with a variable structure are constructed with the aid of Lyapunov functions for each single-integrator agent dynamics in the presence of external disturbances. In particular, it is shown that the finite settling time of the proposed general framework for robust consensus design is upper bounded for any initial condition. This makes it possible for network consensus problems to design and estimate the convergence time offline for a multi-agent team with a given undirected information flow. Finally, simulation results are presented to demonstrate the performance and effectiveness of our finite-time protocols.

The dark-matter axion mass

NASA Astrophysics Data System (ADS)

Klaer, Vincent B.; Moore, Guy D.

2017-11-01

We evaluate the efficiency of axion production from spatially random initial conditions in the axion field, so a network of axionic strings is present. For the first time, we perform numerical simulations which fully account for the large short-distance contributions to the axionic string tension, and the resulting dense network of high-tension axionic strings. We find nevertheless that the total axion production is somewhat less efficient than in the angle-averaged misalignment case. Combining our results with a recent determination of the hot QCD topological susceptibility [1], we find that if the axion makes up all of the dark matter, then the axion mass is ma = 26.2 ± 3.4 μeV.

Topology for Dominance for Network of Multi-Agent System

NASA Astrophysics Data System (ADS)

Szeto, K. Y.

2007-05-01

The resource allocation problem in evolving two-dimensional point patterns is investigated for the existence of good strategies for the construction of initial configuration that leads to fast dominance of the pattern by one single species, which can be interpreted as market dominance by a company in the context of multi-agent systems in econophysics. For hexagonal lattice, certain special topological arrangements of the resource in two-dimensions, such as rings, lines and clusters have higher probability of dominance, compared to random pattern. For more complex networks, a systematic way to search for a stable and dominant strategy of resource allocation in the changing environment is found by means of genetic algorithm. Five typical features can be summarized by means of the distribution function for the local neighborhood of friends and enemies as well as the local clustering coefficients: (1) The winner has more triangles than the loser has. (2) The winner likes to form clusters as the winner tends to connect with other winner rather than with losers; while the loser tends to connect with winners rather than losers. (3) The distribution function of friends as well as enemies for the winner is broader than the corresponding distribution function for the loser. (4) The connectivity at which the peak of the distribution of friends for the winner occurs is larger than that of the loser; while the peak values for friends for winners is lower. (5) The connectivity at which the peak of the distribution of enemies for the winner occurs is smaller than that of the loser; while the peak values for enemies for winners is lower. These five features appear to be general, at least in the context of two-dimensional hexagonal lattices of various sizes, hierarchical lattice, Voronoi diagrams, as well as high-dimensional random networks. These general local topological properties of networks are relevant to strategists aiming at dominance in evolving patterns when the interaction between the agents is local.

The effect of epoch length on estimated EEG functional connectivity and brain network organisation

NASA Astrophysics Data System (ADS)

Fraschini, Matteo; Demuru, Matteo; Crobe, Alessandra; Marrosu, Francesco; Stam, Cornelis J.; Hillebrand, Arjan

2016-06-01

Objective. Graph theory and network science tools have revealed fundamental mechanisms of functional brain organization in resting-state M/EEG analysis. Nevertheless, it is still not clearly understood how several methodological aspects may bias the topology of the reconstructed functional networks. In this context, the literature shows inconsistency in the chosen length of the selected epochs, impeding a meaningful comparison between results from different studies. Approach. The aim of this study was to provide a network approach insensitive to the effects that epoch length has on functional connectivity and network reconstruction. Two different measures, the phase lag index (PLI) and the amplitude envelope correlation (AEC) were applied to EEG resting-state recordings for a group of 18 healthy volunteers using non-overlapping epochs with variable length (1, 2, 4, 6, 8, 10, 12, 14 and 16 s). Weighted clustering coefficient (CCw), weighted characteristic path length (L w) and minimum spanning tree (MST) parameters were computed to evaluate the network topology. The analysis was performed on both scalp and source-space data. Main results. Results from scalp analysis show a decrease in both mean PLI and AEC values with an increase in epoch length, with a tendency to stabilize at a length of 12 s for PLI and 6 s for AEC. Moreover, CCw and L w show very similar behaviour, with metrics based on AEC more reliable in terms of stability. In general, MST parameters stabilize at short epoch lengths, particularly for MSTs based on PLI (1-6 s versus 4-8 s for AEC). At the source-level the results were even more reliable, with stability already at 1 s duration for PLI-based MSTs. Significance. The present work suggests that both PLI and AEC depend on epoch length and that this has an impact on the reconstructed network topology, particularly at the scalp-level. Source-level MST topology is less sensitive to differences in epoch length, therefore enabling the comparison of brain network topology between different studies.

Network structure, topology, and dynamics in generalized models of synchronization

NASA Astrophysics Data System (ADS)

Lerman, Kristina; Ghosh, Rumi

2012-08-01

Network structure is a product of both its topology and interactions between its nodes. We explore this claim using the paradigm of distributed synchronization in a network of coupled oscillators. As the network evolves to a global steady state, nodes synchronize in stages, revealing the network's underlying community structure. Traditional models of synchronization assume that interactions between nodes are mediated by a conservative process similar to diffusion. However, social and biological processes are often nonconservative. We propose a model of synchronization in a network of oscillators coupled via nonconservative processes. We study the dynamics of synchronization of a synthetic and real-world networks and show that the traditional and nonconservative models of synchronization reveal different structures within the same network.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

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

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolicmore » network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. As a result, the defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.« less

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation.

PubMed

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra; Ng, Patrick; Khraiwesh, Basel; Jaiswal, Ashish; Jijakli, Kenan; Koussa, Joseph; Nelson, David R; Cai, Hong; Yang, Xinping; Chang, Roger L; Papin, Jason; Yu, Haiyuan; Balaji, Santhanam; Salehi-Ashtiani, Kourosh

2016-07-19

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

DOE PAGES

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra; ...

2016-06-14

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolicmore » network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. As a result, the defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.« less

Fast algorithm for automatically computing Strahler stream order

USGS Publications Warehouse

Lanfear, Kenneth J.

1990-01-01

An efficient algorithm was developed to determine Strahler stream order for segments of stream networks represented in a Geographic Information System (GIS). The algorithm correctly assigns Strahler stream order in topologically complex situations such as braided streams and multiple drainage outlets. Execution time varies nearly linearly with the number of stream segments in the network. This technique is expected to be particularly useful for studying the topology of dense stream networks derived from digital elevation model data.

Development of human brain structural networks through infancy and childhood.

PubMed

Huang, Hao; Shu, Ni; Mishra, Virendra; Jeon, Tina; Chalak, Lina; Wang, Zhiyue J; Rollins, Nancy; Gong, Gaolang; Cheng, Hua; Peng, Yun; Dong, Qi; He, Yong

2015-05-01

During human brain development through infancy and childhood, microstructural and macrostructural changes take place to reshape the brain's structural networks and better adapt them to sophisticated functional and cognitive requirements. However, structural topological configuration of the human brain during this specific development period is not well understood. In this study, diffusion magnetic resonance image (dMRI) of 25 neonates, 13 toddlers, and 25 preadolescents were acquired to characterize network dynamics at these 3 landmark cross-sectional ages during early childhood. dMRI tractography was used to construct human brain structural networks, and the underlying topological properties were quantified by graph-theory approaches. Modular organization and small-world attributes are evident at birth with several important topological metrics increasing monotonically during development. Most significant increases of regional nodes occur in the posterior cingulate cortex, which plays a pivotal role in the functional default mode network. Positive correlations exist between nodal efficiencies and fractional anisotropy of the white matter traced from these nodes, while correlation slopes vary among the brain regions. These results reveal substantial topological reorganization of human brain structural networks through infancy and childhood, which is likely to be the outcome of both heterogeneous strengthening of the major white matter tracts and pruning of other axonal fibers. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

Analysis of 2D Torus and Hub Topologies of 100Mb/s Ethernet for the Whitney Commodity Computing Testbed

NASA Technical Reports Server (NTRS)

Pedretti, Kevin T.; Fineberg, Samuel A.; Kutler, Paul (Technical Monitor)

1997-01-01

A variety of different network technologies and topologies are currently being evaluated as part of the Whitney Project. This paper reports on the implementation and performance of a Fast Ethernet network configured in a 4x4 2D torus topology in a testbed cluster of 'commodity' Pentium Pro PCs. Several benchmarks were used for performance evaluation: an MPI point to point message passing benchmark, an MPI collective communication benchmark, and the NAS Parallel Benchmarks version 2.2 (NPB2). Our results show that for point to point communication on an unloaded network, the hub and 1 hop routes on the torus have about the same bandwidth and latency. However, the bandwidth decreases and the latency increases on the torus for each additional route hop. Collective communication benchmarks show that the torus provides roughly four times more aggregate bandwidth and eight times faster MPI barrier synchronizations than a hub based network for 16 processor systems. Finally, the SOAPBOX benchmarks, which simulate real-world CFD applications, generally demonstrated substantially better performance on the torus than on the hub. In the few cases the hub was faster, the difference was negligible. In total, our experimental results lead to the conclusion that for Fast Ethernet networks, the torus topology has better performance and scales better than a hub based network.

Altered brain structural networks in attention deficit/hyperactivity disorder children revealed by cortical thickness.

PubMed

Liu, Tian; Chen, Yanni; Li, Chenxi; Li, Youjun; Wang, Jue

2017-07-04

This study investigated the cortical thickness and topological features of human brain anatomical networks related to attention deficit/hyperactivity disorder. Data were collected from 40 attention deficit/hyperactivity disorder children and 40 normal control children. Interregional correlation matrices were established by calculating the correlations of cortical thickness between all pairs of cortical regions (68 regions) of the whole brain. Further thresholds were applied to create binary matrices to construct a series of undirected and unweighted graphs, and global, local, and nodal efficiencies were computed as a function of the network cost. These experimental results revealed abnormal cortical thickness and correlations in attention deficit/hyperactivity disorder, and showed that the brain structural networks of attention deficit/hyperactivity disorder subjects had inefficient small-world topological features. Furthermore, their topological properties were altered abnormally. In particular, decreased global efficiency combined with increased local efficiency in attention deficit/hyperactivity disorder children led to a disorder-related shift of the network topological structure toward regular networks. In addition, nodal efficiency, cortical thickness, and correlation analyses revealed that several brain regions were altered in attention deficit/hyperactivity disorder patients. These findings are in accordance with a hypothesis of dysfunctional integration and segregation of the brain in patients with attention deficit/hyperactivity disorder and provide further evidence of brain dysfunction in attention deficit/hyperactivity disorder patients by observing cortical thickness on magnetic resonance imaging.

Developmental Changes in Topological Asymmetry Between Hemispheric Brain White Matter Networks from Adolescence to Young Adulthood.

PubMed

Zhong, Suyu; He, Yong; Shu, Hua; Gong, Gaolang

2017-04-01

Human brain asymmetries have been well described. Intriguingly, a number of asymmetries in brain phenotypes have been shown to change throughout the lifespan. Recent studies have revealed topological asymmetries between hemispheric white matter networks in the human brain. However, it remains unknown whether and how these topological asymmetries evolve from adolescence to young adulthood, a critical period that constitutes the second peak of human brain and cognitive development. To address this question, the present study included a large cohort of healthy adolescents and young adults. Diffusion and structural magnetic resonance imaging were acquired to construct hemispheric white matter networks, and graph-theory was applied to quantify topological parameters of the hemispheric networks. In both adolescents and young adults, rightward asymmetry in both global and local network efficiencies was consistently observed between the 2 hemispheres, but the degree of the asymmetry was significantly decreased in young adults. At the nodal level, the young adults exhibited less rightward asymmetry of nodal efficiency mainly around the parasylvian area, posterior tempo-parietal cortex, and fusiform gyrus. These developmental patterns of network asymmetry provide novel insight into the human brain structural development from adolescence to young adulthood and also likely relate to the maturation of language and social cognition that takes place during this period. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Resource Optimization Scheme for Multimedia-Enabled Wireless Mesh Networks

PubMed Central

Ali, Amjad; Ahmed, Muhammad Ejaz; Piran, Md. Jalil; Suh, Doug Young

2014-01-01

Wireless mesh networking is a promising technology that can support numerous multimedia applications. Multimedia applications have stringent quality of service (QoS) requirements, i.e., bandwidth, delay, jitter, and packet loss ratio. Enabling such QoS-demanding applications over wireless mesh networks (WMNs) require QoS provisioning routing protocols that lead to the network resource underutilization problem. Moreover, random topology deployment leads to have some unused network resources. Therefore, resource optimization is one of the most critical design issues in multi-hop, multi-radio WMNs enabled with multimedia applications. Resource optimization has been studied extensively in the literature for wireless Ad Hoc and sensor networks, but existing studies have not considered resource underutilization issues caused by QoS provisioning routing and random topology deployment. Finding a QoS-provisioned path in wireless mesh networks is an NP complete problem. In this paper, we propose a novel Integer Linear Programming (ILP) optimization model to reconstruct the optimal connected mesh backbone topology with a minimum number of links and relay nodes which satisfies the given end-to-end QoS demands for multimedia traffic and identification of extra resources, while maintaining redundancy. We further propose a polynomial time heuristic algorithm called Link and Node Removal Considering Residual Capacity and Traffic Demands (LNR-RCTD). Simulation studies prove that our heuristic algorithm provides near-optimal results and saves about 20% of resources from being wasted by QoS provisioning routing and random topology deployment. PMID:25111241

Network module detection: Affinity search technique with the multi-node topological overlap measure

PubMed Central

Li, Ai; Horvath, Steve

2009-01-01

Background Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. Findings We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Conclusion Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: PMID:19619323

Network module detection: Affinity search technique with the multi-node topological overlap measure.

PubMed

Li, Ai; Horvath, Steve

2009-07-20

Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/MTOM/

Resource optimization scheme for multimedia-enabled wireless mesh networks.

PubMed

Ali, Amjad; Ahmed, Muhammad Ejaz; Piran, Md Jalil; Suh, Doug Young

2014-08-08

Wireless mesh networking is a promising technology that can support numerous multimedia applications. Multimedia applications have stringent quality of service (QoS) requirements, i.e., bandwidth, delay, jitter, and packet loss ratio. Enabling such QoS-demanding applications over wireless mesh networks (WMNs) require QoS provisioning routing protocols that lead to the network resource underutilization problem. Moreover, random topology deployment leads to have some unused network resources. Therefore, resource optimization is one of the most critical design issues in multi-hop, multi-radio WMNs enabled with multimedia applications. Resource optimization has been studied extensively in the literature for wireless Ad Hoc and sensor networks, but existing studies have not considered resource underutilization issues caused by QoS provisioning routing and random topology deployment. Finding a QoS-provisioned path in wireless mesh networks is an NP complete problem. In this paper, we propose a novel Integer Linear Programming (ILP) optimization model to reconstruct the optimal connected mesh backbone topology with a minimum number of links and relay nodes which satisfies the given end-to-end QoS demands for multimedia traffic and identification of extra resources, while maintaining redundancy. We further propose a polynomial time heuristic algorithm called Link and Node Removal Considering Residual Capacity and Traffic Demands (LNR-RCTD). Simulation studies prove that our heuristic algorithm provides near-optimal results and saves about 20% of resources from being wasted by QoS provisioning routing and random topology deployment.

Evidence for fish dispersal from spatial analysis of stream network topology

USGS Publications Warehouse

Hitt, N.P.; Angermeier, P.L.

2008-01-01

Developing spatially explicit conservation strategies for stream fishes requires an understanding of the spatial structure of dispersal within stream networks. We explored spatial patterns of stream fish dispersal by evaluating how the size and proximity of connected streams (i.e., stream network topology) explained variation in fish assemblage structure and how this relationship varied with local stream size. We used data from the US Environmental Protection Agency's Environmental Monitoring and Assessment Program in wadeable streams of the Mid-Atlantic Highlands region (n = 308 sites). We quantified stream network topology with a continuous analysis based on the rate of downstream flow accumulation from sites and with a discrete analysis based on the presence of mainstem river confluences (i.e., basin area >250 km2) within 20 fluvial km (fkm) from sites. Continuous variation in stream network topology was related to local species richness within a distance of ???10 fkm, suggesting an influence of fish dispersal within this spatial grain. This effect was explained largely by catostomid species, cyprinid species, and riverine species, but was not explained by zoogeographic regions, ecoregions, sampling period, or spatial autocorrelation. Sites near mainstem river confluences supported greater species richness and abundance of catostomid, cyprinid, and ictalurid fishes than did sites >20 fkm from such confluences. Assemblages at sites on the smallest streams were not related to stream network topology, consistent with the hypothesis that local stream size regulates the influence of regional dispersal. These results demonstrate that the size and proximity of connected streams influence the spatial distribution of fish and suggest that these influences can be incorporated into the designs of stream bioassessments and reserves to enhance management efficacy. ?? 2008 by The North American Benthological Society.

Age Related Changes in Topological Properties of Brain Functional Network and Structural Connectivity.

PubMed

Shah, Chandan; Liu, Jia; Lv, Peilin; Sun, Huaiqiang; Xiao, Yuan; Liu, Jieke; Zhao, Youjin; Zhang, Wenjing; Yao, Li; Gong, Qiyong; Lui, Su

2018-01-01

Introduction: There are still uncertainties about the true nature of age related changes in topological properties of the brain functional network and its structural connectivity during various developmental stages. In this cross- sectional study, we investigated the effects of age and its relationship with regional nodal properties of the functional brain network and white matter integrity. Method: DTI and fMRI data were acquired from 458 healthy Chinese participants ranging from age 8 to 81 years. Tractography was conducted on the DTI data using FSL. Graph Theory analyses were conducted on the functional data yielding topological properties of the functional network using SPM and GRETNA toolbox. Two multiple regressions were performed to investigate the effects of age on nodal topological properties of the functional brain network and white matter integrity. Result: For the functional studies, we observed that regional nodal characteristics such as node betweenness were decreased while node degree and node efficiency was increased in relation to increasing age. Perversely, we observed that the relationship between nodal topological properties and fasciculus structures were primarily positive for nodal betweenness but negative for nodal degree and nodal efficiency. Decrease in functional nodal betweenness was primarily located in superior frontal lobe, right occipital lobe and the global hubs. These brain regions also had both direct and indirect anatomical relationships with the 14 fiber bundles. A linear age related decreases in the Fractional anisotropy (FA) value was found in the callosum forceps minor. Conclusion: These results suggests that age related differences were more pronounced in the functional than in structural measure indicating these measures do not have direct one-to-one mapping. Our study also indicates that the fiber bundles with longer fibers exhibited a more pronounced effect on the properties of functional network.

Inference of neuronal network spike dynamics and topology from calcium imaging data

PubMed Central

Lütcke, Henry; Gerhard, Felipe; Zenke, Friedemann; Gerstner, Wulfram; Helmchen, Fritjof

2013-01-01

Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence (“spike trains”) from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR) and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties. PMID:24399936

Limitation of degree information for analyzing the interaction evolution in online social networks

NASA Astrophysics Data System (ADS)

Shang, Ke-Ke; Yan, Wei-Sheng; Xu, Xiao-Ke

2014-04-01

Previously many studies on online social networks simply analyze the static topology in which the friend relationship once established, then the links and nodes will not disappear, but this kind of static topology may not accurately reflect temporal interactions on online social services. In this study, we define four types of users and interactions in the interaction (dynamic) network. We found that active, disappeared, new and super nodes (users) have obviously different strength distribution properties and this result also can be revealed by the degree characteristics of the unweighted interaction and friendship (static) networks. However, the active, disappeared, new and super links (interactions) only can be reflected by the strength distribution in the weighted interaction network. This result indicates the limitation of the static topology data on analyzing social network evolutions. In addition, our study uncovers the approximately stable statistics for the dynamic social network in which there are a large variation for users and interaction intensity. Our findings not only verify the correctness of our definitions, but also helped to study the customer churn and evaluate the commercial value of valuable customers in online social networks.

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.

A hierarchical model of metabolic machinery based on the kcore decomposition of plant metabolic networks.

PubMed

Filho, Humberto A; Machicao, Jeaneth; Bruno, Odemir M

2018-01-01

Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the kcore decomposition. The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology.

A hierarchical model of metabolic machinery based on the kcore decomposition of plant metabolic networks

PubMed Central

Filho, Humberto A.; Machicao, Jeaneth

2018-01-01

Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the kcore decomposition. The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology. PMID:29734359

Aberrant topology of striatum's connectivity is associated with the number of episodes in depression.

PubMed

Meng, Chun; Brandl, Felix; Tahmasian, Masoud; Shao, Junming; Manoliu, Andrei; Scherr, Martin; Schwerthöffer, Dirk; Bäuml, Josef; Förstl, Hans; Zimmer, Claus; Wohlschläger, Afra M; Riedl, Valentin; Sorg, Christian

2014-02-01

In major depressive disorder, depressive episodes reoccur in ∼60% of cases; however, neural mechanisms of depressive relapse are poorly understood. Depressive episodes are characterized by aberrant topology of the brain's intrinsic functional connectivity network, and the number of episodes is one of the most important predictors for depressive relapse. In this study we hypothesized that specific changes of the topology of intrinsic connectivity interact with the course of episodes in recurrent depressive disorder. To address this hypothesis, we investigated which changes of connectivity topology are associated with the number of episodes in patients, independently of current symptoms and disease duration. Fifty subjects were recruited including 25 depressive patients (two to 10 episodes) and 25 gender- and age-matched control subjects. Resting-state functional magnetic resonance imaging, Harvard-Oxford brain atlas, wavelet-transformation of atlas-shaped regional time-series, and their pairwise Pearson's correlation were used to define individual connectivity matrices. Matrices were analysed by graph-based methods, resulting in outcome measures that were used as surrogates of intrinsic network topology. Topological scores were subsequently compared across groups, and, for patients only, related with the number of depressive episodes and current symptoms by partial correlation analysis. Concerning the whole brain connectivity network of patients, small-world topology was preserved but global efficiency was reduced and global betweenness-centrality increased. Aberrant nodal efficiency and centrality of regional connectivity was found in the dorsal striatum, inferior frontal and orbitofrontal cortex as well as in the occipital and somatosensory cortex. Inferior frontal changes were associated with current symptoms, whereas aberrant right putamen network topology was associated with the number of episodes. Results were controlled for effects of total grey matter volume, medication, and total disease duration. This finding provides first evidence that in major depressive disorder aberrant topology of the right putamen's intrinsic connectivity pattern is associated with the course of depressive episodes, independently of current symptoms, medication status and disease duration. Data suggest that the reorganization of striatal connectivity may interact with the course of episodes in depression thereby contributing to depressive relapse risk.

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.

Aberrant functional brain connectome in people with antisocial personality disorder

PubMed Central

Tang, Yan; Long, Jun; Wang, Wei; Liao, Jian; Xie, Hua; Zhao, Guihu; Zhang, Hao

2016-01-01

Antisocial personality disorder (ASPD) is characterised by a disregard for social obligations and callous unconcern for the feelings of others. Studies have demonstrated that ASPD is associated with abnormalities in brain regions and aberrant functional connectivity. In this paper, topological organisation was examined in resting-state fMRI data obtained from 32 ASPD patients and 32 non-ASPD controls. The frequency-dependent functional networks were constructed using wavelet-based correlations over 90 brain regions. The topology of the functional networks of ASPD subjects was analysed via graph theoretical analysis. Furthermore, the abnormal functional connectivity was determined with a network-based statistic (NBS) approach. Our results revealed that, compared with the controls, the ASPD patients exhibited altered topological configuration of the functional connectome in the frequency interval of 0.016–0.031 Hz, as indicated by the increased clustering coefficient and decreased betweenness centrality in the medial superior frontal gyrus, precentral gyrus, Rolandic operculum, superior parietal gyrus, angular gyrus, and middle temporal pole. In addition, the ASPD patients showed increased functional connectivity mainly located in the default-mode network. The present study reveals an aberrant topological organisation of the functional brain network in individuals with ASPD. Our findings provide novel insight into the neuropathological mechanisms of ASPD. PMID:27257047

Aberrant functional brain connectome in people with antisocial personality disorder.

PubMed

Tang, Yan; Long, Jun; Wang, Wei; Liao, Jian; Xie, Hua; Zhao, Guihu; Zhang, Hao

2016-06-03

Antisocial personality disorder (ASPD) is characterised by a disregard for social obligations and callous unconcern for the feelings of others. Studies have demonstrated that ASPD is associated with abnormalities in brain regions and aberrant functional connectivity. In this paper, topological organisation was examined in resting-state fMRI data obtained from 32 ASPD patients and 32 non-ASPD controls. The frequency-dependent functional networks were constructed using wavelet-based correlations over 90 brain regions. The topology of the functional networks of ASPD subjects was analysed via graph theoretical analysis. Furthermore, the abnormal functional connectivity was determined with a network-based statistic (NBS) approach. Our results revealed that, compared with the controls, the ASPD patients exhibited altered topological configuration of the functional connectome in the frequency interval of 0.016-0.031 Hz, as indicated by the increased clustering coefficient and decreased betweenness centrality in the medial superior frontal gyrus, precentral gyrus, Rolandic operculum, superior parietal gyrus, angular gyrus, and middle temporal pole. In addition, the ASPD patients showed increased functional connectivity mainly located in the default-mode network. The present study reveals an aberrant topological organisation of the functional brain network in individuals with ASPD. Our findings provide novel insight into the neuropathological mechanisms of ASPD.

Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market

PubMed Central

Long, Haiming; Tang, Nengyu

2017-01-01

This study considers the effect of an industry’s network topology on its systemic risk contribution to the stock market using data from the CSI 300 two-tier industry indices from the Chinese stock market. We first measure industry’s conditional-value-at-risk (CoVaR) and the systemic risk contribution (ΔCoVaR) using the fitted time-varying t-copula function. The network of the stock industry is established based on dynamic conditional correlations with the minimum spanning tree. Then, we investigate the connection characteristics and topology of the network. Finally, we utilize seemingly unrelated regression estimation (SUR) of panel data to analyze the relationship between network topology of the stock industry and the industry’s systemic risk contribution. The results show that the systemic risk contribution of small-scale industries such as real estate, food and beverage, software services, and durable goods and clothing, is higher than that of large-scale industries, such as banking, insurance and energy. Industries with large betweenness centrality, closeness centrality, and clustering coefficient and small node occupancy layer are associated with greater systemic risk contribution. In addition, further analysis using a threshold model confirms that the results are robust. PMID:28683130

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.

Origin of hyperbolicity in brain-to-brain coordination networks

NASA Astrophysics Data System (ADS)

Tadić, Bosiljka; Andjelković, Miroslav; Šuvakov, Milovan

2018-02-01

Hyperbolicity or negative curvature of complex networks is the intrinsic geometric proximity of nodes in the graph metric space, which implies an improved network function. Here, we investigate hidden combinatorial geometries in brain-to-brain coordination networks arising through social communications. The networks originate from correlations among EEG signals previously recorded during spoken communications comprising of 14 individuals with 24 speaker-listener pairs. We find that the corresponding networks are delta-hyperbolic with delta_max=1 and the graph diameter D=3 in each brain. While the emergent hyperbolicity in the two-brain networks satisfies delta_max/D/2 < 1 and can be attributed to the topology of the subgraph formed around the cross-brains linking channels. We identify these subgraphs in each studied two-brain network and decompose their structure into simple geometric descriptors (triangles, tetrahedra and cliques of higher orders) that contribute to hyperbolicity. Considering topologies that exceed two separate brain networks as a measure of coordination synergy between the brains, we identify different neuronal correlation patterns ranging from weak coordination to super-brain structure. These topology features are in qualitative agreement with the listener’s self-reported ratings of own experience and quality of the speaker, suggesting that studies of the cross-brain connector networks can reveal new insight into the neural mechanisms underlying human social behavior.

The impact of network characteristics on the diffusion of innovations

NASA Astrophysics Data System (ADS)

Peres, Renana

2014-05-01

This paper studies the influence of network topology on the speed and reach of new product diffusion. While previous research has focused on comparing network types, this paper explores explicitly the relationship between topology and measurements of diffusion effectiveness. We study simultaneously the effect of three network metrics: the average degree, the relative degree of social hubs (i.e., the ratio of the average degree of highly-connected individuals to the average degree of the entire population), and the clustering coefficient. A novel network-generation procedure based on random graphs with a planted partition is used to generate 160 networks with a wide range of values for these topological metrics. Using an agent-based model, we simulate diffusion on these networks and check the dependence of the net present value (NPV) of the number of adopters over time on the network metrics. We find that the average degree and the relative degree of social hubs have a positive influence on diffusion. This result emphasizes the importance of high network connectivity and strong hubs. The clustering coefficient has a negative impact on diffusion, a finding that contributes to the ongoing controversy on the benefits and disadvantages of transitivity. These results hold for both monopolistic and duopolistic markets, and were also tested on a sample of 12 real networks.

Experimental demonstration of topologically protected efficient sound propagation in an acoustic waveguide network

NASA Astrophysics Data System (ADS)

Wei, Qi; Tian, Ye; Zuo, Shu-Yu; Cheng, Ying; Liu, Xiao-Jun

2017-03-01

Acoustic topological states support sound propagation along the boundary in a one-way direction with inherent robustness against defects and disorders, leading to the revolution of the manipulation on acoustic waves. A variety of acoustic topological states relying on circulating fluid, chiral coupling, or temporal modulation have been proposed theoretically. However, experimental demonstration has so far remained a significant challenge, due to the critical limitations such as structural complexity and high losses. Here, we experimentally demonstrate an acoustic anomalous Floquet topological insulator in a waveguide network. The acoustic gapless edge states can be found in the band gap when the waveguides are strongly coupled. The scheme features simple structure and high-energy throughput, leading to the experimental demonstration of efficient and robust topologically protected sound propagation along the boundary. The proposal may offer a unique, promising application for design of acoustic devices in acoustic guiding, switching, isolating, filtering, etc.

Topological Nodal-Net Semimetal in a Graphene Network Structure

NASA Astrophysics Data System (ADS)

Wang, Jian-Tao; Nie, Simin; Weng, Hongming; Kawazoe, Yoshiyuki; Chen, Changfeng

2018-01-01

Topological semimetals are characterized by the nodal points in their electronic structure near the Fermi level, either discrete or forming a continuous line or ring, which are responsible for exotic properties related to the topology of bulk bands. Here we identify by ab initio calculations a distinct topological semimetal that exhibits nodal nets comprising multiple interconnected nodal lines in bulk and have two coupled drumheadlike flat bands around the Fermi level on its surface. This nodal net semimetal state is proposed to be realized in a graphene network structure that can be constructed by inserting a benzene ring into each C- C bond in the bct-C4 lattice or by a crystalline modification of the (5,5) carbon nanotube. These results expand the realm of nodal manifolds in topological semimetals, offering a new platform for exploring novel physics in these fascinating materials.

Evolving neural networks through augmenting topologies.

PubMed

Stanley, Kenneth O; Miikkulainen, Risto

2002-01-01

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.

Latency-Efficient Communication in Wireless Mesh Networks under Consideration of Large Interference Range

NASA Astrophysics Data System (ADS)

Xin, Qin; Yao, Xiaolan; Engelstad, Paal E.

2010-09-01

Wireless Mesh Networking is an emerging communication paradigm to enable resilient, cost-efficient and reliable services for the future-generation wireless networks. We study here the minimum-latency communication primitive of gossiping (all-to-all communication) in multi-hop ad-hoc Wireless Mesh Networks (WMNs). Each mesh node in the WMN is initially given a message and the objective is to design a minimum-latency schedule such that each mesh node distributes its message to all other mesh nodes. Minimum-latency gossiping problem is well known to be NP-hard even for the scenario in which the topology of the WMN is known to all mesh nodes in advance. In this paper, we propose a new latency-efficient approximation scheme that can accomplish gossiping task in polynomial time units in any ad-hoc WMN under consideration of Large Interference Range (LIR), e.g., the interference range is much larger than the transmission range. To the best of our knowledge, it is first time to investigate such a scenario in ad-hoc WMNs under LIR, our algorithm allows the labels (e.g., identifiers) of the mesh nodes to be polynomially large in terms of the size of the WMN, which is the first time that the scenario of large labels has been considered in ad-hoc WMNs under LIR. Furthermore, our gossiping scheme can be considered as a framework which can be easily implied to the scenario under consideration of mobility-related issues since we assume that the mesh nodes have no knowledge on the network topology even for its neighboring mesh nodes.

Authorship attribution based on Life-Like Network Automata.

PubMed

Machicao, Jeaneth; Corrêa, Edilson A; Miranda, Gisele H B; Amancio, Diego R; Bruno, Odemir M

2018-01-01

The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over structural analysis relying only on topological measurements, such as clustering coefficient, betweenness and shortest paths. The optimized results obtained here pave the way for a better characterization of textual networks.

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV: structuring synaptic pathways among recurrent connections.

PubMed

Gilson, Matthieu; Burkitt, Anthony N; Grayden, David B; Thomas, Doreen A; van Hemmen, J Leo

2009-12-01

In neuronal networks, the changes of synaptic strength (or weight) performed by spike-timing-dependent plasticity (STDP) are hypothesized to give rise to functional network structure. This article investigates how this phenomenon occurs for the excitatory recurrent connections of a network with fixed input weights that is stimulated by external spike trains. We develop a theoretical framework based on the Poisson neuron model to analyze the interplay between the neuronal activity (firing rates and the spike-time correlations) and the learning dynamics, when the network is stimulated by correlated pools of homogeneous Poisson spike trains. STDP can lead to both a stabilization of all the neuron firing rates (homeostatic equilibrium) and a robust weight specialization. The pattern of specialization for the recurrent weights is determined by a relationship between the input firing-rate and correlation structures, the network topology, the STDP parameters and the synaptic response properties. We find conditions for feed-forward pathways or areas with strengthened self-feedback to emerge in an initially homogeneous recurrent network.

Resting-State Network Topology Differentiates Task Signals across the Adult Life Span.

PubMed

Chan, Micaela Y; Alhazmi, Fahd H; Park, Denise C; Savalia, Neil K; Wig, Gagan S

2017-03-08

Brain network connectivity differs across individuals. For example, older adults exhibit less segregated resting-state subnetworks relative to younger adults (Chan et al., 2014). It has been hypothesized that individual differences in network connectivity impact the recruitment of brain areas during task execution. While recent studies have described the spatial overlap between resting-state functional correlation (RSFC) subnetworks and task-evoked activity, it is unclear whether individual variations in the connectivity pattern of a brain area (topology) relates to its activity during task execution. We report data from 238 cognitively normal participants (humans), sampled across the adult life span (20-89 years), to reveal that RSFC-based network organization systematically relates to the recruitment of brain areas across two functionally distinct tasks (visual and semantic). The functional activity of brain areas (network nodes) were characterized according to their patterns of RSFC: nodes with relatively greater connections to nodes in their own functional system ("non-connector" nodes) exhibited greater activity than nodes with relatively greater connections to nodes in other systems ("connector" nodes). This "activation selectivity" was specific to those brain systems that were central to each of the tasks. Increasing age was accompanied by less differentiated network topology and a corresponding reduction in activation selectivity (or differentiation) across relevant network nodes. The results provide evidence that connectional topology of brain areas quantified at rest relates to the functional activity of those areas during task. Based on these findings, we propose a novel network-based theory for previous reports of the "dedifferentiation" in brain activity observed in aging. SIGNIFICANCE STATEMENT Similar to other real-world networks, the organization of brain networks impacts their function. As brain network connectivity patterns differ across individuals, we hypothesized that individual differences in network connectivity would relate to differences in brain activity. Using functional MRI in a group of individuals sampled across the adult life span (20-89 years), we measured correlations at rest and related the functional connectivity patterns to measurements of functional activity during two independent tasks. Brain activity varied in relation to connectivity patterns revealed by large-scale network analysis. This relationship tracked the differences in connectivity patterns accompanied by older age, providing important evidence for a link between the topology of areal connectivity measured at rest and the functional recruitment of these areas during task performance. Copyright © 2017 Chan et al.

A financial network perspective of financial institutions' systemic risk contributions

NASA Astrophysics Data System (ADS)

Huang, Wei-Qiang; Zhuang, Xin-Tian; Yao, Shuang; Uryasev, Stan

2016-08-01

This study considers the effects of the financial institutions' local topology structure in the financial network on their systemic risk contribution using data from the Chinese stock market. We first measure the systemic risk contribution with the Conditional Value-at-Risk (CoVaR) which is estimated by applying dynamic conditional correlation multivariate GARCH model (DCC-MVGARCH). Financial networks are constructed from dynamic conditional correlations (DCC) with graph filtering method of minimum spanning trees (MSTs). Then we investigate dynamics of systemic risk contributions of financial institution. Also we study dynamics of financial institution's local topology structure in the financial network. Finally, we analyze the quantitative relationships between the local topology structure and systemic risk contribution with panel data regression analysis. We find that financial institutions with greater node strength, larger node betweenness centrality, larger node closeness centrality and larger node clustering coefficient tend to be associated with larger systemic risk contributions.

Output synchronization of discrete-time dynamical networks based on geometrically incremental dissipativity.

PubMed

Li, Chensong; Zhao, Jun

2017-01-01

In this work, we investigate the output synchronization problem for discrete-time dynamical networks with identical nodes. Firstly, if each node of a network is geometrically incrementally dissipative, the entire network can be viewed as a geometrically dissipative nonlinear system by choosing a particular input-output pair. Then, based on the geometrical dissipativity property, we consider two cases: output synchronization under arbitrary topology and switching topology, respectively. For the first case, we establish several criteria of output synchronization under arbitrary switching between a set of connection topologies by employing a common Lyapunov function. For the other case, we give the design method of a switching signal to achieve output synchronization even if all subnetworks are not synchronous. Finally, an example is provided to illustrate the effectiveness of the main results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

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.

Aberrant topological organization of the functional brain network associated with prior overt hepatic encephalopathy in cirrhotic patients.

PubMed

Chen, Hua-Jun; Chen, Qiu-Feng; Yang, Zhe-Ting; Shi, Hai-Bin

2018-05-30

A higher risk of cognitive impairments has been found after an overt hepatic encephalopathy (OHE) episode in cirrhotic patients. We investigated the effect of prior OHE episodes on the topological organization of the functional brain network and its association with the relevant cognitive impairments. Resting-state functional MRI data were acquired from 41 cirrhotic patients (19 with prior OHE (Prior-OHE) and 22 without (Non-Prior-OHE)) and 21 healthy controls (HC). A Psychometric Hepatic Encephalopathy Score (PHES) assessed cognition. The whole-brain functional network was constructed by thresholding functional correlation matrices of 90 brain regions (derived from the Automated Anatomic Labeling atlas). The topological properties of the brain network, including small-worldness, network efficiency, and nodal efficiency, were examined using graph theory-based analysis. Globally, the Prior-OHE group had a significantly decreased clustering coefficient and local efficiency, compared with the controls. Locally, the nodal efficiency in the bilateral medial superior frontal gyrus and the right postcentral gyrus decreased in the Prior-OHE group, while the nodal efficiency in the bilateral anterior cingulate/paracingulate gyri and right superior parietal gyrus increased in the Prior-OHE group. The alterations of global and regional network parameters progressed from Non-Prior-OHE to Prior-OHE and the clustering coefficient and local efficiency values were significantly correlated with PHES results. In conclusion, cirrhosis leads to the reduction of brain functional network efficiency, which could be aggravated by a prior OHE episode. Aberrant topological organization of the functional brain network may contribute to a higher risk of cognitive impairments in Prior-OHE patients.

Bluetooth Low Energy Mesh Networks: A Survey.

PubMed

Darroudi, Seyed Mahdi; Gomez, Carles

2017-06-22

Bluetooth Low Energy (BLE) has gained significant momentum. However, the original design of BLE focused on star topology networking, which limits network coverage range and precludes end-to-end path diversity. In contrast, other competing technologies overcome such constraints by supporting the mesh network topology. For these reasons, academia, industry, and standards development organizations have been designing solutions to enable BLE mesh networks. Nevertheless, the literature lacks a consolidated view on this emerging area. This paper comprehensively surveys state of the art BLE mesh networking. We first provide a taxonomy of BLE mesh network solutions. We then review the solutions, describing the variety of approaches that leverage existing BLE functionality to enable BLE mesh networks. We identify crucial aspects of BLE mesh network solutions and discuss their advantages and drawbacks. Finally, we highlight currently open issues.

Characterizing granular networks using topological metrics

NASA Astrophysics Data System (ADS)

Dijksman, Joshua A.; Kovalcinova, Lenka; Ren, Jie; Behringer, Robert P.; Kramar, Miroslav; Mischaikow, Konstantin; Kondic, Lou

2018-04-01

We carry out a direct comparison of experimental and numerical realizations of the exact same granular system as it undergoes shear jamming. We adjust the numerical methods used to optimally represent the experimental settings and outcomes up to microscopic contact force dynamics. Measures presented here range from microscopic through mesoscopic to systemwide characteristics of the system. Topological properties of the mesoscopic force networks provide a key link between microscales and macroscales. We report two main findings: (1) The number of particles in the packing that have at least two contacts is a good predictor for the mechanical state of the system, regardless of strain history and packing density. All measures explored in both experiments and numerics, including stress-tensor-derived measures and contact numbers depend in a universal manner on the fraction of nonrattler particles, fNR. (2) The force network topology also tends to show this universality, yet the shape of the master curve depends much more on the details of the numerical simulations. In particular we show that adding force noise to the numerical data set can significantly alter the topological features in the data. We conclude that both fNR and topological metrics are useful measures to consider when quantifying the state of a granular system.

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.

Morphological self-organizing feature map neural network with applications to automatic target recognition

NASA Astrophysics Data System (ADS)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

The Social Origins of Networks and Diffusion.

PubMed

Centola, Damon

2015-03-01

Recent research on social contagion has demonstrated significant effects of network topology on the dynamics of diffusion. However, network topologies are not given a priori. Rather, they are patterns of relations that emerge from individual and structural features of society, such as population composition, group heterogeneity, homophily, and social consolidation. Following Blau and Schwartz, the author develops a model of social network formation that explores how social and structural constraints on tie formation generate emergent social topologies and then explores the effectiveness of these social networks for the dynamics of social diffusion. Results show that, at one extreme, high levels of consolidation can create highly balkanized communities with poor integration of shared norms and practices. As suggested by Blau and Schwartz, reducing consolidation creates more crosscutting circles and significantly improves the dynamics of social diffusion across the population. However, the author finds that further reducing consolidation creates highly intersecting social networks that fail to support the widespread diffusion of norms and practices, indicating that successful social diffusion can depend on moderate to high levels of structural consolidation.

Gene essentiality and the topology of protein interaction networks

PubMed Central

Coulomb, Stéphane; Bauer, Michel; Bernard, Denis; Marsolier-Kergoat, Marie-Claude

2005-01-01

The mechanistic bases for gene essentiality and for cell mutational resistance have long been disputed. The recent availability of large protein interaction databases has fuelled the analysis of protein interaction networks and several authors have proposed that gene dispensability could be strongly related to some topological parameters of these networks. However, many results were based on protein interaction data whose biases were not taken into account. In this article, we show that the essentiality of a gene in yeast is poorly related to the number of interactants (or degree) of the corresponding protein and that the physiological consequences of gene deletions are unrelated to several other properties of proteins in the interaction networks, such as the average degrees of their nearest neighbours, their clustering coefficients or their relative distances. We also found that yeast protein interaction networks lack degree correlation, i.e. a propensity for their vertices to associate according to their degrees. Gene essentiality and more generally cell resistance against mutations thus seem largely unrelated to many parameters of protein network topology. PMID:16087428

Dynamical behaviours and control measures of rumour-spreading model with consideration of network topology

NASA Astrophysics Data System (ADS)

Zhu, Linhe; Zhao, Hongyong

2017-07-01

A series of online rumours have seriously influenced the normal production and living of people. This paper aims to study the combined impact of psychological factor, propagation delay, network topology and control strategy on rumour diffusion over the online social networks. Based on an online social network, which is seen as a scale-free network, we model the spread of rumours by using a delayed SIS (Susceptible and Infected) epidemic-like model with consideration of psychological factor and network topology. First, through theoretical analysis, we illustrate the boundedness of the density of rumour-susceptible individuals and rumour-infected individuals. Second, we obtain the basic reproduction number R0 and prove the stability of the non-rumour equilibrium point and the rumour-spreading equilibrium point. Third, control strategies, such as uniform immunisation control, proportional immunisation control, targeted immunisation control and optimum control, are put forward to restrain rumour diffusion. Meanwhile, we have compared the differences of these control strategies. Finally, some representative numerical simulations are performed to verify the theoretical analysis results.

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

Redline, Erica Marie; Bolintineanu, Dan S.; Lane, J. Matthew

The aim of this study was to alter polymerization chemistry to improve network homogeneity in free-radical crosslinked systems. It was hypothesized that a reduction in heterogeneity of the network would lead to improved mechanical performance. Experiments and simulations were carried out to investigate the connection between polymerization chemistry, network structure and mechanical properties. Experiments were conducted on two different monomer systems - the first is a single monomer system, urethane dimethacrylate (UDMA), and the second is a two-monomer system consisting of bisphenol A glycidyl dimethacrylate (BisGMA) and triethylene glycol dimethacrylate (TEGDMA) in a ratio of 70/30 BisGMA/TEGDMA by weight. Themore » methacrylate systems were crosslinked using traditional radical polymeriza- tion (TRP) with azobisisobutyronitrile (AIBN) or benzoyl peroxide (BPO) as an initiator; TRP systems were used as the control. The monomers were also cross-linked using activator regenerated by electron transfer atom transfer radical polymerization (ARGET ATRP) as a type of controlled radical polymerization (CRP). FTIR and DSC were used to monitor reac- tion kinetics of the systems. The networks were analyzed using NMR, DSC, X-ray diffraction (XRD), atomic force microscopy (AFM), and small angle X-ray scattering (SAXS). These techniques were employed in an attempt to quantify differences between the traditional and controlled radical polymerizations. While a quantitative methodology for characterizing net- work morphology was not established, SAXS and AFM have shown some promising initial results. Additionally, differences in mechanical behavior were observed between traditional and controlled radical polymerized thermosets in the BisGMA/TEGDMA system but not in the UDMA materials; this finding may be the result of network ductility variations between the two materials. Coarse-grained molecular dynamics simulations employing a novel model of the CRP reaction were carried out for the UDMA system, with parameters calibrated based on fully atomistic simulations of the UDMA monomer in the liquid state. Detailed metrics based on network graph theoretical approaches were implemented to quantify the bond network topology resulting from simulations. For a broad range of polymerization parameters, no discernible differences were seen between TRP and CRP UDMA simulations at equal conversions, although clear differences exist as a function of conversion. Both findings are consistent with experiments. Despite a number of shortcomings, these models have demonstrated the potential of molecular simulations for studying network topology in these systems.« less

Effects of spatial constraints on channel network topology: Implications for geomorphological inference

NASA Astrophysics Data System (ADS)

Cabral, Mariza Castanheira De Moura Da Costa

In the fifty-two years since Robert Horton's 1945 pioneering quantitative description of channel network planform (or plan view morphology), no conclusive findings have been presented that permit inference of geomorphological processes from any measures of network planform. All measures of network planform studied exhibit limited geographic variability across different environments. Horton (1945), Langbein et al. (1947), Schumm (1956), Hack (1957), Melton (1958), and Gray (1961) established various "laws" of network planform, that is, statistical relationships between different variables which have limited variability. A wide variety of models which have been proposed to simulate the growth of channel networks in time over a landsurface are generally also in agreement with the above planform laws. An explanation is proposed for the generality of the channel network planform laws. Channel networks must be space filling, that is, they must extend over the landscape to drain every hillslope, leaving no large undrained areas, and with no crossing of channels, often achieving a roughly uniform drainage density in a given environment. It is shown that the space-filling constraint can reduce the sensitivity of planform variables to different network growth models, and it is proposed that this constraint may determine the planform laws. The "Q model" of network growth of Van Pelt and Verwer (1985) is used to generate samples of networks. Sensitivity to the model parameter Q is markedly reduced when the networks generated are required to be space filling. For a wide variety of Q values, the space-filling networks are in approximate agreement with the various channel network planform laws. Additional constraints, including of energy efficiency, were not studied but may further reduce the variability of planform laws. Inference of model parameter Q from network topology is successful only in networks not subject to spatial constraints. In space-filling networks, for a wide range of Q values, the maximal-likelihood Q parameter value is generally in the vicinity of 1/2, which yields topological randomness. It is proposed that space filling originates the appearance of randomness in channel network topology, and may cause difficulties to geomorphological inference from network planform.

Animal transportation networks

PubMed Central

Perna, Andrea; Latty, Tanya

2014-01-01

Many group-living animals construct transportation networks of trails, galleries and burrows by modifying the environment to facilitate faster, safer or more efficient movement. Animal transportation networks can have direct influences on the fitness of individuals, whereas the shape and structure of transportation networks can influence community dynamics by facilitating contacts between different individuals and species. In this review, we discuss three key areas in the study of animal transportation networks: the topological properties of networks, network morphogenesis and growth, and the behaviour of network users. We present a brief primer on elements of network theory, and then discuss the different ways in which animal groups deal with the fundamental trade-off between the competing network properties of travel efficiency, robustness and infrastructure cost. We consider how the behaviour of network users can impact network efficiency, and call for studies that integrate both network topology and user behaviour. We finish with a prospectus for future research. PMID:25165598

Effective centrality and explosive synchronization in complex networks

NASA Astrophysics Data System (ADS)

Navas, A.; Villacorta-Atienza, J. A.; Leyva, I.; Almendral, J. A.; Sendiña-Nadal, I.; Boccaletti, S.

2015-12-01

Synchronization of networked oscillators is known to depend fundamentally on the interplay between the dynamics of the graph's units and the microscopic arrangement of the network's structure. We here propose an effective network whose topological properties reflect the interplay between the topology and dynamics of the original network. On that basis, we are able to introduce the effective centrality, a measure that quantifies the role and importance of each network's node in the synchronization process. In particular, in the context of explosive synchronization, we use such a measure to assess the propensity of a graph to sustain an irreversible transition to synchronization. We furthermore discuss a strategy to induce the explosive behavior in a generic network, by acting only upon a fraction of its nodes.

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.

Physarum polycephalum Percolation as a Paradigm for Topological Phase Transitions in Transportation Networks

NASA Astrophysics Data System (ADS)

Fessel, Adrian; Oettmeier, Christina; Bernitt, Erik; Gauthier, Nils C.; Döbereiner, Hans-Günther

2012-08-01

We study the formation of transportation networks of the true slime mold Physarum polycephalum after fragmentation by shear. Small fragments, called microplasmodia, fuse to form macroplasmodia in a percolation transition. At this topological phase transition, one single giant component forms, connecting most of the previously isolated microplasmodia. Employing the configuration model of graph theory for small link degree, we have found analytically an exact solution for the phase transition. It is generally applicable to percolation as seen, e.g., in vascular networks.

Age-related changes in brain structural covariance networks.

PubMed

Li, Xinwei; Pu, Fang; Fan, Yubo; Niu, Haijun; Li, Shuyu; Li, Deyu

2013-01-01

Previous neuroimaging studies have suggested that cerebral changes over normal aging are not simply characterized by regional alterations, but rather by the reorganization of cortical connectivity patterns. The investigation of structural covariance networks (SCNs) using voxel-based morphometry is an advanced approach to examining the pattern of covariance in gray matter (GM) volumes among different regions of the human cortex. To date, how the organization of critical SCNs change during normal aging remains largely unknown. In this study, we used an SCN mapping approach to investigate eight large-scale networks in 240 healthy participants aged 18-89 years. These participants were subdivided into young (18-23 years), middle aged (30-58 years), and older (61-89 years) subjects. Eight seed regions were chosen from widely reported functional intrinsic connectivity networks. The voxels showing significant positive associations with these seed regions were used to describe the topological organization of an SCN. All of these networks exhibited non-linear patterns in their spatial extent that were associated with normal aging. These networks, except the primary motor network, had a distributed topology in young participants, a sharply localized topology in middle aged participants, and were relatively stable in older participants. The structural covariance derived using the primary motor cortex was limited to the ipsilateral motor regions in the young and older participants, but included contralateral homologous regions in the middle aged participants. In addition, there were significant between-group differences in the structural networks associated with language-related speech and semantics processing, executive control, and the default-mode network (DMN). Taken together, the results of this study demonstrate age-related changes in the topological organization of SCNs, and provide insights into normal aging of the human brain.

Topology Design for Directional Range Extension Networks with Antenna Blockage

DTIC Science & Technology

2017-03-19

introduced by pod-based antenna blockages. Using certain modeling approximations, the paper presents a quantitative analysis showing design trade-offs...parameters. Sec- tion IV develops quantitative relationships among key design elements and performance metrics. Section V considers some implications of the...Topology Design for Directional Range Extension Networks with Antenna Blockage Thomas Shake MIT Lincoln Laboratory shake@ll.mit.edu Abstract

Reconfiguration and Search of Social Networks

PubMed Central

Zhang, Lianming; Peng, Aoyuan

2013-01-01

Social networks tend to exhibit some topological characteristics different from regular networks and random networks, such as shorter average path length and higher clustering coefficient, and the node degree of the majority of social networks obeys exponential distribution. Based on the topological characteristics of the real social networks, a new network model which suits to portray the structure of social networks was proposed, and the characteristic parameters of the model were calculated. To find out the relationship between two people in the social network, and using the local information of the social network and the parallel mechanism, a hybrid search strategy based on k-walker random and a high degree was proposed. Simulation results show that the strategy can significantly reduce the average number of search steps, so as to effectively improve the search speed and efficiency. PMID:24574861

Robust pinning control of heterogeneous complex networks with jointly connected topologies and time-varying parametric uncertainty

NASA Astrophysics Data System (ADS)

Manfredi, Sabato

2018-05-01

The pinning/leader control problems provide the design of the leader or pinning controller in order to guide a complex network to a desired trajectory or target (synchronisation or consensus). Let a time-invariant complex network, pinning/leader control problems include the design of the leader or pinning controller gain and number of nodes to pin in order to guide a network to a desired trajectory (synchronization or consensus). Usually, lower is the number of pinned nodes larger is the pinning gain required to assess network synchronisation. On the other side, realistic application scenario of complex networks is characterised by switching topologies, time-varying node coupling strength and link weight that make hard to solve the pinning/leader control problem. Additionally, the system dynamics at nodes can be heterogeneous. In this paper, we derive robust stabilisation conditions of time-varying heterogeneous complex networks with jointly connected topologies when coupling strength and link weight interactions are affected by time-varying uncertainties. By employing Lyapunov stability theory and linear matrix inequality (LMI) technique, we formulate low computationally demanding stabilisability conditions to design a pinning/leader control gain for robust network synchronisation. The effectiveness of the proposed approach is shown by several design examples applied to a paradigmatic well-known complex network composed of heterogeneous Chua's circuits.

Reconfigurable optical implementation of quantum complex networks

NASA Astrophysics Data System (ADS)

Nokkala, J.; Arzani, F.; Galve, F.; Zambrini, R.; Maniscalco, S.; Piilo, J.; Treps, N.; Parigi, V.

2018-05-01

Network theory has played a dominant role in understanding the structure of complex systems and their dynamics. Recently, quantum complex networks, i.e. collections of quantum systems arranged in a non-regular topology, have been theoretically explored leading to significant progress in a multitude of diverse contexts including, e.g., quantum transport, open quantum systems, quantum communication, extreme violation of local realism, and quantum gravity theories. Despite important progress in several quantum platforms, the implementation of complex networks with arbitrary topology in quantum experiments is still a demanding task, especially if we require both a significant size of the network and the capability of generating arbitrary topology—from regular to any kind of non-trivial structure—in a single setup. Here we propose an all optical and reconfigurable implementation of quantum complex networks. The experimental proposal is based on optical frequency combs, parametric processes, pulse shaping and multimode measurements allowing the arbitrary control of the number of the nodes (optical modes) and topology of the links (interactions between the modes) within the network. Moreover, we also show how to simulate quantum dynamics within the network combined with the ability to address its individual nodes. To demonstrate the versatility of these features, we discuss the implementation of two recently proposed probing techniques for quantum complex networks and structured environments.

Topological Alterations and Symptom-Relevant Modules in the Whole-Brain Structural Network in Semantic Dementia.

PubMed

Ding, Junhua; Chen, Keliang; Zhang, Weibin; Li, Ming; Chen, Yan; Yang, Qing; Lv, Yingru; Guo, Qihao; Han, Zaizhu

2017-01-01

Semantic dementia (SD) is characterized by a selective decline in semantic processing. Although the neuropsychological pattern of this disease has been identified, its topological global alterations and symptom-relevant modules in the whole-brain anatomical network have not been fully elucidated. This study aims to explore the topological alteration of anatomical network in SD and reveal the modules associated with semantic deficits in this disease. We first constructed the whole-brain white-matter networks of 20 healthy controls and 19 patients with SD. Then, the network metrics of graph theory were compared between these two groups. Finally, we separated the network of SD patients into different modules and correlated the structural integrity of each module with the severity of the semantic deficits across patients. The network of the SD patients presented a significantly reduced global efficiency, indicating that the long-distance connections were damaged. The network was divided into the following four distinctive modules: the left temporal/occipital/parietal, frontal, right temporal/occipital, and frontal/parietal modules. The first two modules were associated with the semantic deficits of SD. These findings illustrate the skeleton of the neuroanatomical network of SD patients and highlight the key role of the left temporal/occipital/parietal module and the left frontal module in semantic processing.

Spontaneous repulsion in the A +B →0 reaction on coupled networks

NASA Astrophysics Data System (ADS)

Lazaridis, Filippos; Gross, Bnaya; Maragakis, Michael; Argyrakis, Panos; Bonamassa, Ivan; Havlin, Shlomo; Cohen, Reuven

2018-04-01

We study the transient dynamics of an A +B →0 process on a pair of randomly coupled networks, where reactants are initially separated. We find that, for sufficiently small fractions q of cross couplings, the concentration of A (or B ) particles decays linearly in a first stage and crosses over to a second linear decrease at a mixing time tx. By numerical and analytical arguments, we show that for symmetric and homogeneous structures tx∝(/q)log(/q) where is the mean degree of both networks. Being this behavior is in marked contrast with a purely diffusive process, where the mixing time would go simply like /q , we identify the logarithmic slowing down in tx to be the result of a spontaneous mechanism of repulsion between the reactants A and B due to the interactions taking place at the networks' interface. We show numerically how this spontaneous repulsion effect depends on the topology of the underlying networks.

An energy-efficient MAC protocol using dynamic queue management for delay-tolerant mobile sensor networks.

PubMed

Li, Jie; Li, Qiyue; Qu, Yugui; Zhao, Baohua

2011-01-01

Conventional MAC protocols for wireless sensor network perform poorly when faced with a delay-tolerant mobile network environment. Characterized by a highly dynamic and sparse topology, poor network connectivity as well as data delay-tolerance, delay-tolerant mobile sensor networks exacerbate the severe power constraints and memory limitations of nodes. This paper proposes an energy-efficient MAC protocol using dynamic queue management (EQ-MAC) for power saving and data queue management. Via data transfers initiated by the target sink and the use of a dynamic queue management strategy based on priority, EQ-MAC effectively avoids untargeted transfers, increases the chance of successful data transmission, and makes useful data reach the target terminal in a timely manner. Experimental results show that EQ-MAC has high energy efficiency in comparison with a conventional MAC protocol. It also achieves a 46% decrease in packet drop probability, 79% increase in system throughput, and 25% decrease in mean packet delay.

An Energy-Efficient MAC Protocol Using Dynamic Queue Management for Delay-Tolerant Mobile Sensor Networks

PubMed Central

Li, Jie; Li, Qiyue; Qu, Yugui; Zhao, Baohua

2011-01-01

Conventional MAC protocols for wireless sensor network perform poorly when faced with a delay-tolerant mobile network environment. Characterized by a highly dynamic and sparse topology, poor network connectivity as well as data delay-tolerance, delay-tolerant mobile sensor networks exacerbate the severe power constraints and memory limitations of nodes. This paper proposes an energy-efficient MAC protocol using dynamic queue management (EQ-MAC) for power saving and data queue management. Via data transfers initiated by the target sink and the use of a dynamic queue management strategy based on priority, EQ-MAC effectively avoids untargeted transfers, increases the chance of successful data transmission, and makes useful data reach the target terminal in a timely manner. Experimental results show that EQ-MAC has high energy efficiency in comparison with a conventional MAC protocol. It also achieves a 46% decrease in packet drop probability, 79% increase in system throughput, and 25% decrease in mean packet delay. PMID:22319385

Optimizing topological cascade resilience based on the structure of terrorist networks.

PubMed

Gutfraind, Alexander

2010-11-10

Complex socioeconomic networks such as information, finance and even terrorist networks need resilience to cascades--to prevent the failure of a single node from causing a far-reaching domino effect. We show that terrorist and guerrilla networks are uniquely cascade-resilient while maintaining high efficiency, but they become more vulnerable beyond a certain threshold. We also introduce an optimization method for constructing networks with high passive cascade resilience. The optimal networks are found to be based on cells, where each cell has a star topology. Counterintuitively, we find that there are conditions where networks should not be modified to stop cascades because doing so would come at a disproportionate loss of efficiency. Implementation of these findings can lead to more cascade-resilient networks in many diverse areas.

Small-World Network Spectra in Mean-Field Theory

NASA Astrophysics Data System (ADS)

Grabow, Carsten; Grosskinsky, Stefan; Timme, Marc

2012-05-01

Collective dynamics on small-world networks emerge in a broad range of systems with their spectra characterizing fundamental asymptotic features. Here we derive analytic mean-field predictions for the spectra of small-world models that systematically interpolate between regular and random topologies by varying their randomness. These theoretical predictions agree well with the actual spectra (obtained by numerical diagonalization) for undirected and directed networks and from fully regular to strongly random topologies. These results may provide analytical insights to empirically found features of dynamics on small-world networks from various research fields, including biology, physics, engineering, and social science.

Hamiltonian dynamics for complex food webs

NASA Astrophysics Data System (ADS)

Kozlov, Vladimir; Vakulenko, Sergey; Wennergren, Uno

2016-03-01

We investigate stability and dynamics of large ecological networks by introducing classical methods of dynamical system theory from physics, including Hamiltonian and averaging methods. Our analysis exploits the topological structure of the network, namely the existence of strongly connected nodes (hubs) in the networks. We reveal new relations between topology, interaction structure, and network dynamics. We describe mechanisms of catastrophic phenomena leading to sharp changes of dynamics and hence completely altering the ecosystem. We also show how these phenomena depend on the structure of interaction between species. We can conclude that a Hamiltonian structure of biological interactions leads to stability and large biodiversity.

Evaluating North American Electric Grid Reliability Using the Barabasi-Albert Network Model

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

Chassin, David P.; Posse, Christian

2005-09-15

The reliability of electric transmission systems is examined using a scale-free model of network topology and failure propagation. The topologies of the North American eastern and western electric grids are analyzed to estimate their reliability based on the Barabási-Albert network model. A commonly used power system reliability index is computed using a simple failure propagation model. The results are compared to the values of power system reliability indices previously obtained using other methods and they suggest that scale-free network models are usable to estimate aggregate electric grid reliability.

Evaluating North American Electric Grid Reliability Using the Barabasi-Albert Network Model

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

Chassin, David P.; Posse, Christian

2005-09-15

The reliability of electric transmission systems is examined using a scale-free model of network topology and failure propagation. The topologies of the North American eastern and western electric grids are analyzed to estimate their reliability based on the Barabasi-Albert network model. A commonly used power system reliability index is computed using a simple failure propagation model. The results are compared to the values of power system reliability indices previously obtained using standard power engineering methods, and they suggest that scale-free network models are usable to estimate aggregate electric grid reliability.

The effect of the neural activity on topological properties of growing neural networks.

PubMed

Gafarov, F M; Gafarova, V R

2016-09-01

The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

Finding overlapping communities in multilayer networks

PubMed Central

Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

2018-01-01

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks. PMID:29694387

Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach.

PubMed

Tranchevent, Léon-Charles; Nazarov, Petr V; Kaoma, Tony; Schmartz, Georges P; Muller, Arnaud; Kim, Sang-Yoon; Rajapakse, Jagath C; Azuaje, Francisco

2018-06-07

One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models. We demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data. This article was reviewed by Yang-Yu Liu, Tomislav Smuc and Isabel Nepomuceno.

Finding overlapping communities in multilayer networks.

PubMed

Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

2018-01-01

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks.

Unveiling correlations between financial variables and topological metrics of trading networks: Evidence from a stock and its warrant

NASA Astrophysics Data System (ADS)

Li, Ming-Xia; Jiang, Zhi-Qiang; Xie, Wen-Jie; Xiong, Xiong; Zhang, Wei; Zhou, Wei-Xing

2015-02-01

Traders develop and adopt different trading strategies attempting to maximize their profits in financial markets. These trading strategies not only result in specific topological structures in trading networks, which connect the traders with the pairwise buy-sell relationships, but also have potential impacts on market dynamics. Here, we present a detailed analysis on how the market behaviors are correlated with the structures of traders in trading networks based on audit trail data for the Baosteel stock and its warrant at the transaction level from 22 August 2005 to 23 August 2006. In our investigation, we divide each trade day into 48 rolling time windows with a length of 5 min, construct a trading network within each window, and obtain a time series of over 11,600 trading networks. We find that there are strongly simultaneous correlations between the topological metrics (including network centralization, assortative index, and average path length) of trading networks that characterize the patterns of order execution and the financial variables (including return, volatility, intertrade duration, and trading volume) for the stock and its warrant. Our analysis may shed new lights on how the microscopic interactions between elements within complex system affect the system's performance.

Dynamic changes in neural circuit topology following mild mechanical injury in vitro.

PubMed

Patel, Tapan P; Ventre, Scott C; Meaney, David F

2012-01-01

Despite its enormous incidence, mild traumatic brain injury is not well understood. One aspect that needs more definition is how the mechanical energy during injury affects neural circuit function. Recent developments in cellular imaging probes provide an opportunity to assess the dynamic state of neural networks with single-cell resolution. In this article, we developed imaging methods to assess the state of dissociated cortical networks exposed to mild injury. We estimated the imaging conditions needed to achieve accurate measures of network properties, and applied these methodologies to evaluate if mild mechanical injury to cortical neurons produces graded changes to either spontaneous network activity or altered network topology. We found that modest injury produced a transient increase in calcium activity that dissipated within 1 h after injury. Alternatively, moderate mechanical injury produced immediate disruption in network synchrony, loss in excitatory tone, and increased modular topology. A calcium-activated neutral protease (calpain) was a key intermediary in these changes; blocking calpain activation restored the network nearly completely to its pre-injury state. Together, these findings show a more complex change in neural circuit behavior than previously reported for mild mechanical injury, and highlight at least one important early mechanism responsible for these changes.

Time and Energy Efficient Relay Transmission for Multi-Hop Wireless Sensor Networks.

PubMed

Kim, Jin-Woo; Barrado, José Ramón Ramos; Jeon, Dong-Keun

2016-06-27

The IEEE 802.15.4 standard is widely recognized as one of the most successful enabling technologies for short range low rate wireless communications and it is used in IoT applications. It covers all the details related to the MAC and PHY layers of the IoT protocol stack. Due to the nature of IoT, the wireless sensor networks are autonomously self-organized networks without infrastructure support. One of the issues in IoT is the network scalability. To address this issue, it is necessary to support the multi-hop topology. The IEEE 802.15.4 network can support a star, peer-to-peer, or cluster-tree topology. One of the IEEE 802.15.4 topologies suited for the high predictability of performance guarantees and energy efficient behavior is a cluster-tree topology where sensor nodes can switch off their transceivers and go into a sleep state to save energy. However, the IEEE 802.15.4 cluster-tree topology may not be able to provide sufficient bandwidth for the increased traffic load and the additional information may not be delivered successfully. The common drawback of the existing approaches is that they do not address the poor bandwidth utilization problem in IEEE 802.15.4 cluster-tree networks, so it is difficult to increase the network performance. Therefore, to solve this problem in this paper we study a relay transmission protocol based on the standard protocol in the IEEE 802.15.4 MAC. In the proposed scheme, the coordinators can relay data frames to their parent devices or their children devices without contention and can provide bandwidth for the increased traffic load or the number of devices. We also evaluate the performance of the proposed scheme through simulation. The simulation results demonstrate that the proposed scheme can improve the reliability, the end-to-end delay, and the energy consumption.

Time and Energy Efficient Relay Transmission for Multi-Hop Wireless Sensor Networks

PubMed Central

Kim, Jin-Woo; Barrado, José Ramón Ramos; Jeon, Dong-Keun

2016-01-01

The IEEE 802.15.4 standard is widely recognized as one of the most successful enabling technologies for short range low rate wireless communications and it is used in IoT applications. It covers all the details related to the MAC and PHY layers of the IoT protocol stack. Due to the nature of IoT, the wireless sensor networks are autonomously self-organized networks without infrastructure support. One of the issues in IoT is the network scalability. To address this issue, it is necessary to support the multi-hop topology. The IEEE 802.15.4 network can support a star, peer-to-peer, or cluster-tree topology. One of the IEEE 802.15.4 topologies suited for the high predictability of performance guarantees and energy efficient behavior is a cluster-tree topology where sensor nodes can switch off their transceivers and go into a sleep state to save energy. However, the IEEE 802.15.4 cluster-tree topology may not be able to provide sufficient bandwidth for the increased traffic load and the additional information may not be delivered successfully. The common drawback of the existing approaches is that they do not address the poor bandwidth utilization problem in IEEE 802.15.4 cluster-tree networks, so it is difficult to increase the network performance. Therefore, to solve this problem in this paper we study a relay transmission protocol based on the standard protocol in the IEEE 802.15.4 MAC. In the proposed scheme, the coordinators can relay data frames to their parent devices or their children devices without contention and can provide bandwidth for the increased traffic load or the number of devices. We also evaluate the performance of the proposed scheme through simulation. The simulation results demonstrate that the proposed scheme can improve the reliability, the end-to-end delay, and the energy consumption. PMID:27355952

An Adaptive Complex Network Model for Brain Functional Networks

PubMed Central

Gomez Portillo, Ignacio J.; Gleiser, Pablo M.

2009-01-01

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution. PMID:19738902

Stimulus-induced transitions between spike-wave discharges and spindles with the modulation of thalamic reticular nucleus.

PubMed

Fan, Denggui; Wang, Qingyun; Su, Jianzhong; Xi, Hongguang

2017-12-01

It is believed that thalamic reticular nucleus (TRN) controls spindles and spike-wave discharges (SWD) in seizure or sleeping processes. The dynamical mechanisms of spatiotemporal evolutions between these two types of activity, however, are not well understood. In light of this, we first use a single-compartment thalamocortical neural field model to investigate the effects of TRN on occurrence of SWD and its transition. Results show that the increasing inhibition from TRN to specific relay nuclei (SRN) can lead to the transition of system from SWD to slow-wave oscillation. Specially, it is shown that stimulations applied in the cortical neuronal populations can also initiate the SWD and slow-wave oscillation from the resting states under the typical inhibitory intensity from TRN to SRN. Then, we expand into a 3-compartment coupled thalamocortical model network in linear and circular structures, respectively, to explore the spatiotemporal evolutions of wave states in different compartments. The main results are: (i) for the open-ended model network, SWD induced by stimulus in the first compartment can be transformed into sleep-like slow UP-DOWN and spindle states as it propagates into the downstream compartments; (ii) for the close-ended model network, weak stimulations performed in the first compartment can result in the consistent experimentally observed spindle oscillations in all three compartments; in contrast, stronger periodic single-pulse stimulations applied in the first compartment can induce periodic transitions between SWD and spindle oscillations. Detailed investigations reveal that multi-attractor coexistence mechanism composed of SWD, spindles and background state underlies these state evolutions. What's more, in order to demonstrate the state evolution stability with respect to the topological structures of neural network, we further expand the 3-compartment coupled network into 10-compartment coupled one, with linear and circular structures, and nearest-neighbor (NN) coupled network as well as its realization of small-world (SW) topology via random rewiring, respectively. Interestingly, for the cases of linear and circular connetivities, qualitatively similar results were obtained in addition to the more irregularity of firings. However, SWD can be eventually transformed into the consistent low-amplitude oscillations for both NN and SW networks. In particular, SWD evolves into the slow spindling oscillations and background tonic oscillations within the NN and SW network, respectively. Our modeling and simulation studies highlight the effect of network topology in the evolutions of SWD and spindling oscillations, which provides new insights into the mechanisms of cortical seizures development.

Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

NASA Technical Reports Server (NTRS)

Mengshoel, Ole J.; Wilkins, David C.; Roth, Dan

2010-01-01

For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which algorithms are effective for initialization? When should the search process be restarted? In the present work we investigate these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary topologies. We also give a novel formalization of stochastic local search, with focus on initialization and restart, using probability theory and mixture models. Experimentally, we apply our methods to the problem of MPE computation, using a stochastic local search algorithm known as Stochastic Greedy Search. By carefully optimizing both initialization and restart, we reduce the MPE search time for application BNs by several orders of magnitude compared to using uniform at random initialization without restart. On several BNs from applications, the performance of Stochastic Greedy Search is competitive with clique tree clustering, a state-of-the-art exact algorithm used for MPE computation in BNs.

Bluetooth Low Energy Mesh Networks: A Survey

PubMed Central

Darroudi, Seyed Mahdi; Gomez, Carles

2017-01-01

Bluetooth Low Energy (BLE) has gained significant momentum. However, the original design of BLE focused on star topology networking, which limits network coverage range and precludes end-to-end path diversity. In contrast, other competing technologies overcome such constraints by supporting the mesh network topology. For these reasons, academia, industry, and standards development organizations have been designing solutions to enable BLE mesh networks. Nevertheless, the literature lacks a consolidated view on this emerging area. This paper comprehensively surveys state of the art BLE mesh networking. We first provide a taxonomy of BLE mesh network solutions. We then review the solutions, describing the variety of approaches that leverage existing BLE functionality to enable BLE mesh networks. We identify crucial aspects of BLE mesh network solutions and discuss their advantages and drawbacks. Finally, we highlight currently open issues. PMID:28640183

Resilience of networks to environmental stress: From regular to random networks

NASA Astrophysics Data System (ADS)

Eom, Young-Ho

2018-04-01

Despite the huge interest in network resilience to stress, most of the studies have concentrated on internal stress damaging network structure (e.g., node removals). Here we study how networks respond to environmental stress deteriorating their external conditions. We show that, when regular networks gradually disintegrate as environmental stress increases, disordered networks can suddenly collapse at critical stress with hysteresis and vulnerability to perturbations. We demonstrate that this difference results from a trade-off between node resilience and network resilience to environmental stress. The nodes in the disordered networks can suppress their collapses due to the small-world topology of the networks but eventually collapse all together in return. Our findings indicate that some real networks can be highly resilient against environmental stress to a threshold yet extremely vulnerable to the stress above the threshold because of their small-world topology.

Key node selection in minimum-cost control of complex networks

NASA Astrophysics Data System (ADS)

Ding, Jie; Wen, Changyun; Li, Guoqi

2017-11-01

Finding the key node set that is connected with a given number of external control sources for driving complex networks from initial state to any predefined state with minimum cost, known as minimum-cost control problem, is critically important but remains largely open. By defining an importance index for each node, we propose revisited projected gradient method extension (R-PGME) in Monte-Carlo scenario to determine key node set. It is found that the importance index of a node is strongly correlated to occurrence rate of that node to be selected as a key node in Monte-Carlo realizations for three elementary topologies, Erdős-Rényi and scale-free networks. We also discover the distribution patterns of key nodes when the control cost reaches its minimum. Specifically, the importance indices of all nodes in an elementary stem show a quasi-periodic distribution with high peak values in the beginning and end of a quasi-period while they approach to a uniform distribution in an elementary cycle. We further point out that an elementary dilation can be regarded as two elementary stems whose lengths are the closest, and the importance indices in each stem present similar distribution as in an elementary stem. Our results provide a better understanding and deep insight of locating the key nodes in different topologies with minimum control cost.

Disrupted topological organization of resting-state functional brain network in subcortical vascular mild cognitive impairment.

PubMed

Yi, Li-Ye; Liang, Xia; Liu, Da-Ming; Sun, Bo; Ying, Sun; Yang, Dong-Bo; Li, Qing-Bin; Jiang, Chuan-Lu; Han, Ying

2015-10-01

Neuroimaging studies have demonstrated both structural and functional abnormalities in widespread brain regions in patients with subcortical vascular mild cognitive impairment (svMCI). However, whether and how these changes alter functional brain network organization remains largely unknown. We recruited 21 patients with svMCI and 26 healthy control (HC) subjects who underwent resting-state functional magnetic resonance imaging scans. Graph theory-based network analyses were used to investigate alterations in the topological organization of functional brain networks. Compared with the HC individuals, the patients with svMCI showed disrupted global network topology with significantly increased path length and modularity. Modular structure was also impaired in the svMCI patients with a notable rearrangement of the executive control module, where the parietal regions were split out and grouped as a separate module. The svMCI patients also revealed deficits in the intra- and/or intermodule connectivity of several brain regions. Specifically, the within-module degree was decreased in the middle cingulate gyrus while it was increased in the left anterior insula, medial prefrontal cortex and cuneus. Additionally, increased intermodule connectivity was observed in the inferior and superior parietal gyrus, which was associated with worse cognitive performance in the svMCI patients. Together, our results indicate that svMCI patients exhibit dysregulation of the topological organization of functional brain networks, which has important implications for understanding the pathophysiological mechanism of svMCI. © 2015 John Wiley & Sons Ltd.

Emergence of Rich-Club Topology and Coordinated Dynamics in Development of Hippocampal Functional Networks In Vitro

PubMed Central

Charlesworth, Paul; Kitzbichler, Manfred G.; Paulsen, Ole

2015-01-01

Recent studies demonstrated that the anatomical network of the human brain shows a “rich-club” organization. This complex topological feature implies that highly connected regions, hubs of the large-scale brain network, are more densely interconnected with each other than expected by chance. Rich-club nodes were traversed by a majority of short paths between peripheral regions, underlining their potential importance for efficient global exchange of information between functionally specialized areas of the brain. Network hubs have also been described at the microscale of brain connectivity (so-called “hub neurons”). Their role in shaping synchronous dynamics and forming microcircuit wiring during development, however, is not yet fully understood. The present study aimed to investigate the role of hubs during network development, using multi-electrode arrays and functional connectivity analysis during spontaneous multi-unit activity (MUA) of dissociated primary mouse hippocampal neurons. Over the first 4 weeks in vitro, functional connectivity significantly increased in strength, density, and size, with mature networks demonstrating a robust modular and small-world topology. As expected by a “rich-get-richer” growth rule of network evolution, MUA graphs were found to form rich-clubs at an early stage in development (14 DIV). Later on, rich-club nodes were a consistent topological feature of MUA graphs, demonstrating high nodal strength, efficiency, and centrality. Rich-club nodes were also found to be crucial for MUA dynamics. They often served as broker of spontaneous activity flow, confirming that hub nodes and rich-clubs may play an important role in coordinating functional dynamics at the microcircuit level. PMID:25855164

3D Architecture of Trabecular Bone in the Pig Mandible and Femur: Inter-Trabecular Angle Distributions.

NASA Astrophysics Data System (ADS)

Ben-Zvi, Yehonatan; Reznikov, Natalie; Shahar, Ron; Weiner, Steve

2017-09-01

Cancellous bone is an intricate network of interconnected trabeculae, to which analysis of network topology can be applied. The inter-trabecular angle (ITA) analysis - an analysis of network topological parameters and regularity of network-forming nodes, was previously carried out on human proximal femora and showed that trabecular bone follows two main principles: sparsity of the network connectedness (prevalence of nodes with low connectivity in the network) and maximal space spanning (angular offset of connected elements is maximal for their number and approximates the values of geometrically symmetric shapes). These observations suggest that 3D organization of trabecular bone, irrespective of size and shape of individual elements, reflects a tradeoff between minimal metabolic cost of maintenance and maximal network stability under conditions of multidirectional loading. In this study we validate the ITA application using additional 3D structures (cork and 3D-printed metal lattices), analyze the ITA parameters in porcine proximal femora and mandibles and carry out a spatial analysis of the most common node type in the porcine mandibular condyle. The validation shows that the ITA application reliably detects designed or evolved topological parameters. The ITA parameters of porcine trabecular bones are similar to those of human bones. We demonstrate functional adaptation in the pig mandibular condyle by showing that the planar nodes with 3 edges are preferentially aligned in relation to the muscle forces that are applied to the condyle. We conclude that the ITA topological parameters are remarkable conserved, but locally do adapt to applied stresses.

The Probability of a Gene Tree Topology within a Phylogenetic Network with Applications to Hybridization Detection

PubMed Central

Yu, Yun; Degnan, James H.; Nakhleh, Luay

2012-01-01

Gene tree topologies have proven a powerful data source for various tasks, including species tree inference and species delimitation. Consequently, methods for computing probabilities of gene trees within species trees have been developed and widely used in probabilistic inference frameworks. All these methods assume an underlying multispecies coalescent model. However, when reticulate evolutionary events such as hybridization occur, these methods are inadequate, as they do not account for such events. Methods that account for both hybridization and deep coalescence in computing the probability of a gene tree topology currently exist for very limited cases. However, no such methods exist for general cases, owing primarily to the fact that it is currently unknown how to compute the probability of a gene tree topology within the branches of a phylogenetic network. Here we present a novel method for computing the probability of gene tree topologies on phylogenetic networks and demonstrate its application to the inference of hybridization in the presence of incomplete lineage sorting. We reanalyze a Saccharomyces species data set for which multiple analyses had converged on a species tree candidate. Using our method, though, we show that an evolutionary hypothesis involving hybridization in this group has better support than one of strict divergence. A similar reanalysis on a group of three Drosophila species shows that the data is consistent with hybridization. Further, using extensive simulation studies, we demonstrate the power of gene tree topologies at obtaining accurate estimates of branch lengths and hybridization probabilities of a given phylogenetic network. Finally, we discuss identifiability issues with detecting hybridization, particularly in cases that involve extinction or incomplete sampling of taxa. PMID:22536161

Topological Reorganization of the Default Mode Network in Severe Male Obstructive Sleep Apnea

PubMed Central

Chen, Liting; Fan, Xiaole; Li, Haijun; Ye, Chenglong; Yu, Honghui; Gong, Honghan; Zeng, Xianjun; Peng, Dechang; Yan, Liping

2018-01-01

Impaired spontaneous regional activity and altered topology of the brain network have been observed in obstructive sleep apnea (OSA). However, the mechanisms of disrupted functional connectivity (FC) and topological reorganization of the default mode network (DMN) in patients with OSA remain largely unknown. We explored whether the FC is altered within the DMN and examined topological changes occur in the DMN in patients with OSA using a graph theory analysis of resting-state functional magnetic resonance imaging data and evaluated the relationship between neuroimaging measures and clinical variables. Resting-state data were obtained from 46 male patients with untreated severe OSA and 46 male good sleepers (GSs). We specifically selected 20 DMN subregions to construct the DMN architecture. The disrupted FC and topological properties of the DMN in patients with OSA were characterized using graph theory. The OSA group showed significantly decreased FC of the anterior–posterior DMN and within the posterior DMN, and also showed increased FC within the DMN. The DMN exhibited small-world topology in both OSA and GS groups. Compared to GSs, patients with OSA showed a decreased clustering coefficient (Cp) and local efficiency, and decreased nodal centralities in the left posterior cingulate cortex and dorsal medial prefrontal cortex, and increased nodal centralities in the ventral medial prefrontal cortex and the right parahippocampal cortex. Finally, the abnormal DMN FC was significantly related to Cp, path length, global efficiency, and Montreal cognitive assessment score. OSA showed disrupted FC within the DMN, which may have contributed to the observed topological reorganization. These findings may provide further evidence of cognitive deficits in patients with OSA.

Inference of gene regulatory networks from time series by Tsallis entropy

PubMed Central

2011-01-01

Background The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 ≤ q ≤ 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/. PMID:21545720

Alignment-free protein interaction network comparison

PubMed Central

Ali, Waqar; Rito, Tiago; Reinert, Gesine; Sun, Fengzhu; Deane, Charlotte M.

2014-01-01

Motivation: Biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this article, we describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction. Results: We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. The biological applicability of the method is then shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks. Our results provide new evidence that the topology of protein interaction networks contains information about evolutionary processes, despite the lack of conservation of individual interactions. As Netdis is applicable to all networks because of its speed and simplicity, we apply it to a large collection of biological and non-biological networks where it clusters diverse networks by type. Availability and implementation: The source code of the program is freely available at http://www.stats.ox.ac.uk/research/proteins/resources. Contact: w.ali@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25161230

Designing, Describing and Disseminating New Materials by using the Network Topology Approach.

PubMed

Öhrström, Lars

2016-09-19

This Concept article describes how network topology analysis is applied to different fields of solid-state chemistry. Its usefulness is demonstrated by examples from metal-organic frameworks, group 14 allotropes and related compounds, ice polymorphs, zeolites, supramolecular (organic) solid-state chemistry, Zintl phases, and cathode materials for Li-ion batteries. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The topology of the federal funds market

NASA Astrophysics Data System (ADS)

Bech, Morten L.; Atalay, Enghin

2010-11-01

We explore the network topology of the federal funds market. This market is important for distributing liquidity throughout the financial system and for the implementation of monetary policy. The recent turmoil in global financial markets underscores its importance. We find that the network is sparse, exhibits the small-world phenomenon, and is disassortative. Centrality measures are useful predictors of the interest rate of a loan.

Stability of whole brain and regional network topology within and between resting and cognitive states.

PubMed

Rzucidlo, Justyna K; Roseman, Paige L; Laurienti, Paul J; Dagenbach, Dale

2013-01-01

Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.

Frequency specific brain networks in Parkinson's disease and comorbid depression.

PubMed

Qian, Long; Zhang, Yi; Zheng, Li; Fu, Xuemei; Liu, Weiguo; Shang, Yuqing; Zhang, Yaoyu; Xu, Yuanyuan; Liu, Yijun; Zhu, Huaiqiu; Gao, Jia-Hong

2017-02-01

The topological organization underlying the human brain was extensively investigated using resting-state functional magnetic resonance imaging, focusing on a low frequency of signal oscillation from 0.01 to 0.1 Hz. However, the frequency specificities with regard to the topological properties of the brain networks have not been fully revealed. In this study, a novel complementary ensemble empirical mode decomposition (CEEMD) method was used to separate the fMRI time series into five characteristic oscillations with distinct frequencies. Then, the small world properties of brain networks were analyzed for each of these five oscillations in patients (n = 67) with depressed Parkinson's disease (DPD, n = 20) , non-depressed Parkinson's disease (NDPD, n = 47) and healthy controls (HC, n = 46). Compared with HC, the results showed decreased network efficiency in characteristic oscillations from 0.05 to 0.12 Hz and from 0.02 to 0.05 Hz for the DPD and NDPD patients, respectively. Furthermore, compared with HC, the most significant inter-group difference across five brain oscillations was found in the basal ganglia (0.01 to 0.05 Hz) and paralimbic-limbic network (0.02 to 0.22 Hz) for the DPD patients, and in the visual cortex (0.02 to 0.05 Hz) for the NDPD patients. Compared with NDPD, the DPD patients showed reduced efficiency of nodes in the basal ganglia network (0.01 to 0.05 Hz). Our results demonstrated that DPD is characterized by a disrupted topological organization in large-scale brain functional networks. Moreover, the CEEMD analysis suggested a prominent dissociation in the topological organization of brain networks between DPD and NDPD in both space and frequency domains. Our findings indicated that these characteristic oscillatory activities in different functional circuits may contribute to distinct motor and non-motor components of clinical impairments in Parkinson's disease.

Topological Rényi Entropy after a Quantum Quench

NASA Astrophysics Data System (ADS)

Halász, Gábor B.; Hamma, Alioscia

2013-04-01

We present an analytical study on the resilience of topological order after a quantum quench. The system is initially prepared in the ground state of the toric-code model, and then quenched by switching on an external magnetic field. During the subsequent time evolution, the variation in topological order is detected via the topological Rényi entropy of order 2. We consider two different quenches: the first one has an exact solution, while the second one requires perturbation theory. In both cases, we find that the long-term time average of the topological Rényi entropy in the thermodynamic limit is the same as its initial value. Based on our results, we argue that topological order is resilient against a wide range of quenches.

Topological Rényi entropy after a quantum quench.

PubMed

Halász, Gábor B; Hamma, Alioscia

2013-04-26

We present an analytical study on the resilience of topological order after a quantum quench. The system is initially prepared in the ground state of the toric-code model, and then quenched by switching on an external magnetic field. During the subsequent time evolution, the variation in topological order is detected via the topological Rényi entropy of order 2. We consider two different quenches: the first one has an exact solution, while the second one requires perturbation theory. In both cases, we find that the long-term time average of the topological Rényi entropy in the thermodynamic limit is the same as its initial value. Based on our results, we argue that topological order is resilient against a wide range of quenches.

ClueNet: Clustering a temporal network based on topological similarity rather than denseness

PubMed Central

Milenković, Tijana

2018-01-01

Network clustering is a very popular topic in the network science field. Its goal is to divide (partition) the network into groups (clusters or communities) of “topologically related” nodes, where the resulting topology-based clusters are expected to “correlate” well with node label information, i.e., metadata, such as cellular functions of genes/proteins in biological networks, or age or gender of people in social networks. Even for static data, the problem of network clustering is complex. For dynamic data, the problem is even more complex, due to an additional dimension of the data—their temporal (evolving) nature. Since the problem is computationally intractable, heuristic approaches need to be sought. Existing approaches for dynamic network clustering (DNC) have drawbacks. First, they assume that nodes should be in the same cluster if they are densely interconnected within the network. We hypothesize that in some applications, it might be of interest to cluster nodes that are topologically similar to each other instead of or in addition to requiring the nodes to be densely interconnected. Second, they ignore temporal information in their early steps, and when they do consider this information later on, they do so implicitly. We hypothesize that capturing temporal information earlier in the clustering process and doing so explicitly will improve results. We test these two hypotheses via our new approach called ClueNet. We evaluate ClueNet against six existing DNC methods on both social networks capturing evolving interactions between individuals (such as interactions between students in a high school) and biological networks capturing interactions between biomolecules in the cell at different ages. We find that ClueNet is superior in over 83% of all evaluation tests. As more real-world dynamic data are becoming available, DNC and thus ClueNet will only continue to gain importance. PMID:29738568

Exploring the topological sources of robustness against invasion in biological and technological networks.

PubMed

Alcalde Cuesta, Fernando; González Sequeiros, Pablo; Lozano Rojo, Álvaro

2016-02-10

For a network, the accomplishment of its functions despite perturbations is called robustness. Although this property has been extensively studied, in most cases, the network is modified by removing nodes. In our approach, it is no longer perturbed by site percolation, but evolves after site invasion. The process transforming resident/healthy nodes into invader/mutant/diseased nodes is described by the Moran model. We explore the sources of robustness (or its counterpart, the propensity to spread favourable innovations) of the US high-voltage power grid network, the Internet2 academic network, and the C. elegans connectome. We compare them to three modular and non-modular benchmark networks, and samples of one thousand random networks with the same degree distribution. It is found that, contrary to what happens with networks of small order, fixation probability and robustness are poorly correlated with most of standard statistics, but they depend strongly on the degree distribution. While community detection techniques are able to detect the existence of a central core in Internet2, they are not effective in detecting hierarchical structures whose topological complexity arises from the repetition of a few rules. Box counting dimension and Rent's rule are applied to show a subtle trade-off between topological and wiring complexity.

Exploring the topological sources of robustness against invasion in biological and technological networks

PubMed Central

Alcalde Cuesta, Fernando; González Sequeiros, Pablo; Lozano Rojo, Álvaro

2016-01-01

For a network, the accomplishment of its functions despite perturbations is called robustness. Although this property has been extensively studied, in most cases, the network is modified by removing nodes. In our approach, it is no longer perturbed by site percolation, but evolves after site invasion. The process transforming resident/healthy nodes into invader/mutant/diseased nodes is described by the Moran model. We explore the sources of robustness (or its counterpart, the propensity to spread favourable innovations) of the US high-voltage power grid network, the Internet2 academic network, and the C. elegans connectome. We compare them to three modular and non-modular benchmark networks, and samples of one thousand random networks with the same degree distribution. It is found that, contrary to what happens with networks of small order, fixation probability and robustness are poorly correlated with most of standard statistics, but they depend strongly on the degree distribution. While community detection techniques are able to detect the existence of a central core in Internet2, they are not effective in detecting hierarchical structures whose topological complexity arises from the repetition of a few rules. Box counting dimension and Rent’s rule are applied to show a subtle trade-off between topological and wiring complexity. PMID:26861189

Construction of ontology augmented networks for protein complex prediction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

2013-01-01

Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

Probing the Topological Properties of Complex Networks Modeling Short Written Texts

PubMed Central

Amancio, Diego R.

2015-01-01

In recent years, graph theory has been widely employed to probe several language properties. More specifically, the so-called word adjacency model has been proven useful for tackling several practical problems, especially those relying on textual stylistic analysis. The most common approach to treat texts as networks has simply considered either large pieces of texts or entire books. This approach has certainly worked well—many informative discoveries have been made this way—but it raises an uncomfortable question: could there be important topological patterns in small pieces of texts? To address this problem, the topological properties of subtexts sampled from entire books was probed. Statistical analyses performed on a dataset comprising 50 novels revealed that most of the traditional topological measurements are stable for short subtexts. When the performance of the authorship recognition task was analyzed, it was found that a proper sampling yields a discriminability similar to the one found with full texts. Surprisingly, the support vector machine classification based on the characterization of short texts outperformed the one performed with entire books. These findings suggest that a local topological analysis of large documents might improve its global characterization. Most importantly, it was verified, as a proof of principle, that short texts can be analyzed with the methods and concepts of complex networks. As a consequence, the techniques described here can be extended in a straightforward fashion to analyze texts as time-varying complex networks. PMID:25719799

Functional Alignment of Metabolic Networks.

PubMed

Mazza, Arnon; Wagner, Allon; Ruppin, Eytan; Sharan, Roded

2016-05-01

Network alignment has become a standard tool in comparative biology, allowing the inference of protein function, interaction, and orthology. However, current alignment techniques are based on topological properties of networks and do not take into account their functional implications. Here we propose, for the first time, an algorithm to align two metabolic networks by taking advantage of their coupled metabolic models. These models allow us to assess the functional implications of genes or reactions, captured by the metabolic fluxes that are altered following their deletion from the network. Such implications may spread far beyond the region of the network where the gene or reaction lies. We apply our algorithm to align metabolic networks from various organisms, ranging from bacteria to humans, showing that our alignment can reveal functional orthology relations that are missed by conventional topological alignments.

Information transfer network of global market indices

NASA Astrophysics Data System (ADS)

Kim, Yup; Kim, Jinho; Yook, Soon-Hyung

2015-07-01

We study the topological properties of the information transfer networks (ITN) of the global financial market indices for six different periods. ITN is a directed weighted network, in which the direction and weight are determined by the transfer entropy between market indices. By applying the threshold method, it is found that ITN undergoes a crossover from the complete graph to a small-world (SW) network. SW regime of ITN for a global crisis is found to be much more enhanced than that for ordinary periods. Furthermore, when ITN is in SW regime, the average clustering coefficient is found to be synchronized with average volatility of markets. We also compare the results with the topological properties of correlation networks.

Drainage fracture networks in elastic solids with internal fluid generation

NASA Astrophysics Data System (ADS)

Kobchenko, Maya; Hafver, Andreas; Jettestuen, Espen; Galland, Olivier; Renard, François; Meakin, Paul; Jamtveit, Bjørn; Dysthe, Dag K.

2013-06-01

Experiments in which CO2 gas was generated by the yeast fermentation of sugar in an elastic layer of gelatine gel confined between two glass plates are described and analyzed theoretically. The CO2 gas pressure causes the gel layer to fracture. The gas produced is drained on short length scales by diffusion and on long length scales by flow in a fracture network, which has topological properties that are intermediate between river networks and hierarchical-fracture networks. A simple model for the experimental system with two parameters that characterize the disorder and the intermediate (river-fracture) topology of the network was developed and the results of the model were compared with the experimental results.

Dynamic properties of epidemic spreading on finite size complex networks

NASA Astrophysics Data System (ADS)

Li, Ying; Liu, Yang; Shan, Xiu-Ming; Ren, Yong; Jiao, Jian; Qiu, Ben

2005-11-01

The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS (susceptible-infected-susceptible) model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.

An Automatic Networking and Routing Algorithm for Mesh Network in PLC System

NASA Astrophysics Data System (ADS)

Liu, Xiaosheng; Liu, Hao; Liu, Jiasheng; Xu, Dianguo

2017-05-01

Power line communication (PLC) is considered to be one of the best communication technologies in smart grid. However, the topology of low voltage distribution network is complex, meanwhile power line channel has characteristics of time varying and attenuation, which lead to the unreliability of power line communication. In this paper, an automatic networking and routing algorithm is introduced which can be adapted to the "blind state" topology. The results of simulation and test show that the scheme is feasible, the routing overhead is small, and the load balance performance is good, which can achieve the establishment and maintenance of network quickly and effectively. The scheme is of great significance to improve the reliability of PLC.