Science.gov

Sample records for network complexity measures

  1. Hierarchy measure for complex networks.

    PubMed

    Mones, Enys; Vicsek, Lilla; Vicsek, Tamás

    2012-01-01

    Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure.

  2. Hierarchy Measure for Complex Networks

    PubMed Central

    Mones, Enys; Vicsek, Lilla; Vicsek, Tamás

    2012-01-01

    Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure. PMID:22470477

  3. Measure of robustness for complex networks

    NASA Astrophysics Data System (ADS)

    Youssef, Mina Nabil

    Critical infrastructures are repeatedly attacked by external triggers causing tremendous amount of damages. Any infrastructure can be studied using the powerful theory of complex networks. A complex network is composed of extremely large number of different elements that exchange commodities providing significant services. The main functions of complex networks can be damaged by different types of attacks and failures that degrade the network performance. These attacks and failures are considered as disturbing dynamics, such as the spread of viruses in computer networks, the spread of epidemics in social networks, and the cascading failures in power grids. Depending on the network structure and the attack strength, every network differently suffers damages and performance degradation. Hence, quantifying the robustness of complex networks becomes an essential task. In this dissertation, new metrics are introduced to measure the robustness of technological and social networks with respect to the spread of epidemics, and the robustness of power grids with respect to cascading failures. First, we introduce a new metric called the Viral Conductance (VCSIS ) to assess the robustness of networks with respect to the spread of epidemics that are modeled through the susceptible/infected/susceptible (SIS) epidemic approach. In contrast to assessing the robustness of networks based on a classical metric, the epidemic threshold, the new metric integrates the fraction of infected nodes at steady state for all possible effective infection strengths. Through examples, VCSIS provides more insights about the robustness of networks than the epidemic threshold. In addition, both the paradoxical robustness of Barabasi-Albert preferential attachment networks and the effect of the topology on the steady state infection are studied, to show the importance of quantifying the robustness of networks. Second, a new metric VCSIR is introduced to assess the robustness of networks with respect

  4. Measuring multiple evolution mechanisms of complex networks

    PubMed Central

    Zhang, Qian-Ming; Xu, Xiao-Ke; Zhu, Yu-Xiao; Zhou, Tao

    2015-01-01

    Numerous concise models such as preferential attachment have been put forward to reveal the evolution mechanisms of real-world networks, which show that real-world networks are usually jointly driven by a hybrid mechanism of multiplex features instead of a single pure mechanism. To get an accurate simulation for real networks, some researchers proposed a few hybrid models by mixing multiple evolution mechanisms. Nevertheless, how a hybrid mechanism of multiplex features jointly influence the network evolution is not very clear. In this study, we introduce two methods (link prediction and likelihood analysis) to measure multiple evolution mechanisms of complex networks. Through tremendous experiments on artificial networks, which can be controlled to follow multiple mechanisms with different weights, we find the method based on likelihood analysis performs much better and gives very accurate estimations. At last, we apply this method to some real-world networks which are from different domains (including technology networks and social networks) and different countries (e.g., USA and China), to see how popularity and clustering co-evolve. We find most of them are affected by both popularity and clustering, but with quite different weights. PMID:26065382

  5. Spectral measures of bipartivity in complex networks.

    PubMed

    Estrada, Ernesto; Rodríguez-Velázquez, Juan A

    2005-10-01

    We introduce a quantitative measure of network bipartivity as a proportion of even to total number of closed walks in the network. Spectral graph theory is used to quantify how close to bipartite a network is and the extent to which individual nodes and edges contribute to the global network bipartivity. It is shown that the bipartivity characterizes the network structure and can be related to the efficiency of semantic or communication networks, trophic interactions in food webs, construction principles in metabolic networks, or communities in social networks.

  6. Riemannian-geometric entropy for measuring network complexity

    NASA Astrophysics Data System (ADS)

    Franzosi, Roberto; Felice, Domenico; Mancini, Stefano; Pettini, Marco

    2016-06-01

    A central issue in the science of complex systems is the quantitative characterization of complexity. In the present work we address this issue by resorting to information geometry. Actually we propose a constructive way to associate with a—in principle, any—network a differentiable object (a Riemannian manifold) whose volume is used to define the entropy. The effectiveness of the latter in measuring network complexity is successfully proved through its capability of detecting a classical phase transition occurring in both random graphs and scale-free networks, as well as of characterizing small exponential random graphs, configuration models, and real networks.

  7. Pruning artificial neural networks using neural complexity measures.

    PubMed

    Jorgensen, Thomas D; Haynes, Barry P; Norlund, Charlotte C F

    2008-10-01

    This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the problem it is meant to solve. This novel pruning technique is tested in a robot control domain, simulating a racecar. It is shown, that the proposed pruning method is a significant improvement over the most commonly used pruning method Magnitude Based Pruning. Furthermore, some of the pruned networks prove to be faster learners than the benchmark network that they originate from. This means that this pruning method can also help to unleash hidden potential in a network, because the learning time decreases substantially for a pruned a network, due to the reduction of dimensionality of the network.

  8. A complex network-based importance measure for mechatronics systems

    NASA Astrophysics Data System (ADS)

    Wang, Yanhui; Bi, Lifeng; Lin, Shuai; Li, Man; Shi, Hao

    2017-01-01

    In view of the negative impact of functional dependency, this paper attempts to provide an alternative importance measure called Improved-PageRank (IPR) for measuring the importance of components in mechatronics systems. IPR is a meaningful extension of the centrality measures in complex network, which considers usage reliability of components and functional dependency between components to increase importance measures usefulness. Our work makes two important contributions. First, this paper integrates the literature of mechatronic architecture and complex networks theory to define component network. Second, based on the notion of component network, a meaningful IPR is brought into the identifying of important components. In addition, the IPR component importance measures, and an algorithm to perform stochastic ordering of components due to the time-varying nature of usage reliability of components and functional dependency between components, are illustrated with a component network of bogie system that consists of 27 components.

  9. Measuring robustness of community structure in complex networks

    NASA Astrophysics Data System (ADS)

    Li, Hui-Jia; Wang, Hao; Chen, Luonan

    2014-12-01

    The theory of community structure is a powerful tool for real networks, which can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks derived from complex systems always contain error edges, evaluating the robustness of community structure is an urgent and important task. In this letter, we employ the critical threshold of resolution parameter in Hamiltonian function, γC , to measure the robustness of a network. According to spectral theory, a rigorous proof shows that the index we proposed is inversely proportional to robustness of community structure. Furthermore, by utilizing the co-evolution model, we provides a new efficient method for computing the value of γC . The research can be applied to broad clustering problems in network analysis and data mining due to its solid mathematical basis and experimental effects.

  10. Measurement of organization in complex and co-evolving networks

    NASA Astrophysics Data System (ADS)

    Georgiev, Georgi

    2012-02-01

    We apply a new method for measurement of organization of complex and co-evolving networks using the quantity of physical action. We consider simple arrangements of elements in a network and constraints to their motion along paths and calculate the amount of organization in each system using the following measure: organization is the inverse of the average sum of physical actions of all elements in a system per unit motion multiplied by the Planck's constant. The meaning of quantity of organization here is the number of quanta of action per one unit motion along a path of an element. A unit motion along a path for a network, such as internet, is the transmission of one bit of information. The calculation can be expanded to systems consisting of many elements and constraints and also can be followed as a function of time with improvement of the organization of a system or connected systems and networks. Thus, the principle of least action becomes the driving force, and the least action state of the system, the attractor for all of the paths of its elements and states of its constraints. We consider also the rate of constraint minimization, or decrease of action per element and motion, as a function of the number of elements i.e. quality as a function of quantity. Increase of quantity, within specified limits, leads to increase of level of organization and vice versa.

  11. Mitigating wildland fire hazard using complex network centrality measures

    NASA Astrophysics Data System (ADS)

    Russo, Lucia; Russo, Paola; Siettos, Constantinos I.

    2016-12-01

    We show how to distribute firebreaks in heterogeneous forest landscapes in the presence of strong wind using complex network centrality measures. The proposed framework is essentially a two-tire one: at the inner part a state-of- the-art Cellular Automata model is used to compute the weights of the underlying lattice network while at the outer part the allocation of the fire breaks is scheduled in terms of a hierarchy of centralities which influence the most the spread of fire. For illustration purposes we applied the proposed framework to a real-case wildfire that broke up in Spetses Island, Greece in 1990. We evaluate the scheme against the benchmark of random allocation of firebreaks under the weather conditions of the real incident i.e. in the presence of relatively strong winds.

  12. Unraveling chaotic attractors by complex networks and measurements of stock market complexity

    NASA Astrophysics Data System (ADS)

    Cao, Hongduo; Li, Ying

    2014-03-01

    We present a novel method for measuring the complexity of a time series by unraveling a chaotic attractor modeled on complex networks. The complexity index R, which can potentially be exploited for prediction, has a similar meaning to the Kolmogorov complexity (calculated from the Lempel-Ziv complexity), and is an appropriate measure of a series' complexity. The proposed method is used to research the complexity of the world's major capital markets. None of these markets are completely random, and they have different degrees of complexity, both over the entire length of their time series and at a level of detail. However, developing markets differ significantly from mature markets. Specifically, the complexity of mature stock markets is stronger and more stable over time, whereas developing markets exhibit relatively low and unstable complexity over certain time periods, implying a stronger long-term price memory process.

  13. Unraveling chaotic attractors by complex networks and measurements of stock market complexity.

    PubMed

    Cao, Hongduo; Li, Ying

    2014-03-01

    We present a novel method for measuring the complexity of a time series by unraveling a chaotic attractor modeled on complex networks. The complexity index R, which can potentially be exploited for prediction, has a similar meaning to the Kolmogorov complexity (calculated from the Lempel-Ziv complexity), and is an appropriate measure of a series' complexity. The proposed method is used to research the complexity of the world's major capital markets. None of these markets are completely random, and they have different degrees of complexity, both over the entire length of their time series and at a level of detail. However, developing markets differ significantly from mature markets. Specifically, the complexity of mature stock markets is stronger and more stable over time, whereas developing markets exhibit relatively low and unstable complexity over certain time periods, implying a stronger long-term price memory process.

  14. An application of a measure for organization of complex networks

    NASA Astrophysics Data System (ADS)

    Georgiev, Georgi; Daly, Michael

    2013-03-01

    In order to measure self-organization in complex networks a quantitative measure for organization is necessary. This will allow us to measure their degree of organization and rate of self-organization. We apply as a measure for quantity of organization the inverse of the average sum of physical actions of all elements in a system per unit motion multiplied by the Planck's constant, using the principle of least action. The meaning of quantity of organization here is the inverse of average number of quanta of action per one node crossing of an element of the system. We apply this measure to the central processing unit (CPU) of computers. The organization for several generations of CPUs shows a double exponential rate of change of organization with time. The exact functional dependence has, S-shaped structure, suggesting some of the mechanisms of self-organization. We also study the dependence of organization on the number of transistors. This method helps us explain the mechanism of increase of organization through quantity accumulation and constraint and curvature minimization with an attractor, the least average sum of actions of all elements and for all motions. This approach can help to describe, quantify, measure, manage, design and predict future behavior of complex systems to achieve the highest rates of self-organization to improve their quality.

  15. Measure for degree heterogeneity in complex networks and its application to recurrence network analysis

    NASA Astrophysics Data System (ADS)

    Jacob, Rinku; Harikrishnan, K. P.; Misra, R.; Ambika, G.

    2017-01-01

    We propose a novel measure of degree heterogeneity, for unweighted and undirected complex networks, which requires only the degree distribution of the network for its computation. We show that the proposed measure can be applied to all types of network topology with ease and increases with the diversity of node degrees in the network. The measure is applied to compute the heterogeneity of synthetic (both random and scale free (SF)) and real-world networks with its value normalized in the interval [0 ,1 ]. To define the measure, we introduce a limiting network whose heterogeneity can be expressed analytically with the value tending to 1 as the size of the network N tends to infinity. We numerically study the variation of heterogeneity for random graphs (as a function of p and N) and for SF networks with γ and N as variables. Finally, as a specific application, we show that the proposed measure can be used to compare the heterogeneity of recurrence networks constructed from the time series of several low-dimensional chaotic attractors, thereby providing a single index to compare the structural complexity of chaotic attractors.

  16. Measure for degree heterogeneity in complex networks and its application to recurrence network analysis

    PubMed Central

    Jacob, Rinku; Misra, R.

    2017-01-01

    We propose a novel measure of degree heterogeneity, for unweighted and undirected complex networks, which requires only the degree distribution of the network for its computation. We show that the proposed measure can be applied to all types of network topology with ease and increases with the diversity of node degrees in the network. The measure is applied to compute the heterogeneity of synthetic (both random and scale free (SF)) and real-world networks with its value normalized in the interval [0,1]. To define the measure, we introduce a limiting network whose heterogeneity can be expressed analytically with the value tending to 1 as the size of the network N tends to infinity. We numerically study the variation of heterogeneity for random graphs (as a function of p and N) and for SF networks with γ and N as variables. Finally, as a specific application, we show that the proposed measure can be used to compare the heterogeneity of recurrence networks constructed from the time series of several low-dimensional chaotic attractors, thereby providing a single index to compare the structural complexity of chaotic attractors. PMID:28280579

  17. Offdiagonal complexity: A computationally quick complexity measure for graphs and networks

    NASA Astrophysics Data System (ADS)

    Claussen, Jens Christian

    2007-02-01

    A vast variety of biological, social, and economical networks shows topologies drastically differing from random graphs; yet the quantitative characterization remains unsatisfactory from a conceptual point of view. Motivated from the discussion of small scale-free networks, a biased link distribution entropy is defined, which takes an extremum for a power-law distribution. This approach is extended to the node-node link cross-distribution, whose nondiagonal elements characterize the graph structure beyond link distribution, cluster coefficient and average path length. From here a simple (and computationally cheap) complexity measure can be defined. This offdiagonal complexity (OdC) is proposed as a novel measure to characterize the complexity of an undirected graph, or network. While both for regular lattices and fully connected networks OdC is zero, it takes a moderately low value for a random graph and shows high values for apparently complex structures as scale-free networks and hierarchical trees. The OdC approach is applied to the Helicobacter pylori protein interaction network and randomly rewired surrogates.

  18. Measuring the significance of community structure in complex networks

    NASA Astrophysics Data System (ADS)

    Hu, Yanqing; Nie, Yuchao; Yang, Hua; Cheng, Jie; Fan, Ying; di, Zengru

    2010-12-01

    Many complex systems can be represented as networks, and separating a network into communities could simplify functional analysis considerably. Many approaches have recently been proposed to detect communities, but a method to determine whether the detected communities are significant is still lacking. In this paper, an index to evaluate the significance of communities in networks is proposed based on perturbation of the network. In contrast to previous approaches, the network is disturbed gradually, and the index is defined by integrating all of the similarities between the community structures before and after perturbation. Moreover, by taking the null model into account, the index eliminates scale effects. Thus, it can evaluate and compare the significance of communities in different networks. The method has been tested in many artificial and real-world networks. The results show that the index is in fact independent of the size of the network and the number of communities. With this approach, clear communities are found to always exist in social networks, but significant communities cannot be found in protein interactions and metabolic networks.

  19. Node-weighted interacting network measures improve the representation of real-world complex systems

    NASA Astrophysics Data System (ADS)

    Wiedermann, M.; Donges, J. F.; Heitzig, J.; Kurths, J.

    2013-04-01

    Many real-world complex systems are adequately represented by networks of interacting or interdependent networks. Additionally, it is often reasonable to take into account node weights such as surface area in climate networks, volume in brain networks, or economic capacity in trade networks to reflect the varying size or importance of subsystems. Combining both ideas, we derive a novel class of statistical measures for analysing the structure of networks of interacting networks with heterogeneous node weights. Using a prototypical spatial network model, we show that the newly introduced node-weighted interacting network measures provide an improved representation of the underlying system's properties as compared to their unweighted analogues. We apply our method to study the complex network structure of cross-boundary trade between European Union (EU) and non-EU countries finding that it provides relevant information on trade balance and economic robustness.

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

    NASA Astrophysics Data System (ADS)

    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.

  1. An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

    PubMed Central

    Cabessa, Jérémie; Villa, Alessandro E. P.

    2014-01-01

    We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits. PMID:24727866

  2. An attractor-based complexity measurement for Boolean recurrent neural networks.

    PubMed

    Cabessa, Jérémie; Villa, Alessandro E P

    2014-01-01

    We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of ω-automata, and then translating the most refined classification of ω-automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.

  3. FALCON or how to compute measures time efficiently on dynamically evolving dense complex networks?

    PubMed

    Franke, R; Ivanova, G

    2014-02-01

    A large number of topics in biology, medicine, neuroscience, psychology and sociology can be generally described via complex networks in order to investigate fundamental questions of structure, connectivity, information exchange and causality. Especially, research on biological networks like functional spatiotemporal brain activations and changes, caused by neuropsychiatric pathologies, is promising. Analyzing those so-called complex networks, the calculation of meaningful measures can be very long-winded depending on their size and structure. Even worse, in many labs only standard desktop computers are accessible to perform those calculations. Numerous investigations on complex networks regard huge but sparsely connected network structures, where most network nodes are connected to only a few others. Currently, there are several libraries available to tackle this kind of networks. A problem arises when not only a few big and sparse networks have to be analyzed, but hundreds or thousands of smaller and conceivably dense networks (e.g. in measuring brain activation over time). Then every minute per network is crucial. For these cases there several possibilities to use standard hardware more efficiently. It is not sufficient to apply just standard algorithms for dense graph characteristics. This article introduces the new library FALCON developed especially for the exploration of dense complex networks. Currently, it offers 12 different measures (like clustering coefficients), each for undirected-unweighted, undirected-weighted and directed-unweighted networks. It uses a multi-core approach in combination with comprehensive code and hardware optimizations. There is an alternative massively parallel GPU implementation for the most time-consuming measures, too. Finally, a comparing benchmark is integrated to support the choice of the most suitable library for a particular network issue.

  4. Directed clustering coefficient as a measure of systemic risk in complex banking networks

    NASA Astrophysics Data System (ADS)

    Tabak, Benjamin M.; Takami, Marcelo; Rocha, Jadson M. C.; Cajueiro, Daniel O.; Souza, Sergio R. S.

    2014-01-01

    Recent literature has focused on the study of systemic risk in complex networks. It is clear now, after the crisis of 2008, that the aggregate behavior of the interaction among agents is not straightforward and it is very difficult to predict. Contributing to this debate, this paper shows that the directed clustering coefficient may be used as a measure of systemic risk in complex networks. Furthermore, using data from the Brazilian interbank network, we show that the directed clustering coefficient is negatively correlated with domestic interest rates.

  5. Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project

    PubMed Central

    McDonough, Ian M.; Nashiro, Kaoru

    2014-01-01

    An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity—a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity. PMID:24959130

  6. Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

    PubMed Central

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

    2015-01-01

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

  7. Correlation dimension of complex networks.

    PubMed

    Lacasa, Lucas; Gómez-Gardeñes, Jesús

    2013-04-19

    We propose a new measure to characterize the dimension of complex networks based on the ergodic theory of dynamical systems. This measure is derived from the correlation sum of a trajectory generated by a random walker navigating the network, and extends the classical Grassberger-Procaccia algorithm to the context of complex networks. The method is validated with reliable results for both synthetic networks and real-world networks such as the world air-transportation network or urban networks, and provides a computationally fast way for estimating the dimensionality of networks which only relies on the local information provided by the walkers.

  8. Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks.

    PubMed

    Zhou, Renjie; Yang, Chen; Wan, Jian; Zhang, Wei; Guan, Bo; Xiong, Naixue

    2017-04-06

    Measurement of time series complexity and predictability is sometimes the cornerstone for proposing solutions to topology and congestion control problems in sensor networks. As a method of measuring time series complexity and predictability, multiscale entropy (MSE) has been widely applied in many fields. However, sample entropy, which is the fundamental component of MSE, measures the similarity of two subsequences of a time series with either zero or one, but without in-between values, which causes sudden changes of entropy values even if the time series embraces small changes. This problem becomes especially severe when the length of time series is getting short. For solving such the problem, we propose flexible multiscale entropy (FMSE), which introduces a novel similarity function measuring the similarity of two subsequences with full-range values from zero to one, and thus increases the reliability and stability of measuring time series complexity. The proposed method is evaluated on both synthetic and real time series, including white noise, 1/f noise and real vibration signals. The evaluation results demonstrate that FMSE has a significant improvement in reliability and stability of measuring complexity of time series, especially when the length of time series is short, compared to MSE and composite multiscale entropy (CMSE). The proposed method FMSE is capable of improving the performance of time series analysis based topology and traffic congestion control techniques.

  9. Eigencentrality based on dissimilarity measures reveals central nodes in complex networks.

    PubMed

    Alvarez-Socorro, A J; Herrera-Almarza, G C; González-Díaz, L A

    2015-11-25

    One of the most important problems in complex network's theory is the location of the entities that are essential or have a main role within the network. For this purpose, the use of dissimilarity measures (specific to theory of classification and data mining) to enrich the centrality measures in complex networks is proposed. The centrality method used is the eigencentrality which is based on the heuristic that the centrality of a node depends on how central are the nodes in the immediate neighbourhood (like rich get richer phenomenon). This can be described by an eigenvalues problem, however the information of the neighbourhood and the connections between neighbours is not taken in account, neglecting their relevance when is one evaluates the centrality/importance/influence of a node. The contribution calculated by the dissimilarity measure is parameter independent, making the proposed method is also parameter independent. Finally, we perform a comparative study of our method versus other methods reported in the literature, obtaining more accurate and less expensive computational results in most cases.

  10. Growing complex network of citations of scientific papers: Modeling and measurements

    NASA Astrophysics Data System (ADS)

    Golosovsky, Michael; Solomon, Sorin

    2017-01-01

    We consider the network of citations of scientific papers and use a combination of the theoretical and experimental tools to uncover microscopic details of this network growth. Namely, we develop a stochastic model of citation dynamics based on the copying-redirection-triadic closure mechanism. In a complementary and coherent way, the model accounts both for statistics of references of scientific papers and for their citation dynamics. Originating in empirical measurements, the model is cast in such a way that it can be verified quantitatively in every aspect. Such validation is performed by measuring citation dynamics of physics papers. The measurements revealed nonlinear citation dynamics, the nonlinearity being intricately related to network topology. The nonlinearity has far-reaching consequences including nonstationary citation distributions, diverging citation trajectories of similar papers, runaways or "immortal papers" with infinite citation lifetime, etc. Thus nonlinearity in complex network growth is our most important finding. In a more specific context, our results can be a basis for quantitative probabilistic prediction of citation dynamics of individual papers and of the journal impact factor.

  11. Eigencentrality based on dissimilarity measures reveals central nodes in complex networks

    PubMed Central

    Alvarez-Socorro, A. J.; Herrera-Almarza, G. C.; González-Díaz, L. A.

    2015-01-01

    One of the most important problems in complex network’s theory is the location of the entities that are essential or have a main role within the network. For this purpose, the use of dissimilarity measures (specific to theory of classification and data mining) to enrich the centrality measures in complex networks is proposed. The centrality method used is the eigencentrality which is based on the heuristic that the centrality of a node depends on how central are the nodes in the immediate neighbourhood (like rich get richer phenomenon). This can be described by an eigenvalues problem, however the information of the neighbourhood and the connections between neighbours is not taken in account, neglecting their relevance when is one evaluates the centrality/importance/influence of a node. The contribution calculated by the dissimilarity measure is parameter independent, making the proposed method is also parameter independent. Finally, we perform a comparative study of our method versus other methods reported in the literature, obtaining more accurate and less expensive computational results in most cases. PMID:26603652

  12. Eigencentrality based on dissimilarity measures reveals central nodes in complex networks

    NASA Astrophysics Data System (ADS)

    Alvarez-Socorro, A. J.; Herrera-Almarza, G. C.; González-Díaz, L. A.

    2015-11-01

    One of the most important problems in complex network’s theory is the location of the entities that are essential or have a main role within the network. For this purpose, the use of dissimilarity measures (specific to theory of classification and data mining) to enrich the centrality measures in complex networks is proposed. The centrality method used is the eigencentrality which is based on the heuristic that the centrality of a node depends on how central are the nodes in the immediate neighbourhood (like rich get richer phenomenon). This can be described by an eigenvalues problem, however the information of the neighbourhood and the connections between neighbours is not taken in account, neglecting their relevance when is one evaluates the centrality/importance/influence of a node. The contribution calculated by the dissimilarity measure is parameter independent, making the proposed method is also parameter independent. Finally, we perform a comparative study of our method versus other methods reported in the literature, obtaining more accurate and less expensive computational results in most cases.

  13. Measuring microscopic evolution processes of complex networks based on empirical data

    NASA Astrophysics Data System (ADS)

    Chi, Liping

    2015-04-01

    Aiming at understanding the microscopic mechanism of complex systems in real world, we perform the measurement that characterizes the evolution properties on two empirical data sets. In the Autonomous Systems Internet data, the network size keeps growing although the system suffers a high rate of node deletion (r = 0.4) and link deletion (q = 0.81). However, the average degree keeps almost unchanged during the whole time range. At each time step the external links attached to a new node are about c = 1.1 and the internal links added between existing nodes are approximately m = 8. For the Scientific Collaboration data, it is a cumulated result of all the authors from 1893 up to the considered year. There is no deletion of nodes and links, r = q = 0. The external and internal links at each time step are c = 1.04 and m = 0, correspondingly. The exponents of degree distribution p(k) ∼ k-γ of these two empirical datasets γdata are in good agreement with that obtained theoretically γtheory. The results indicate that these evolution quantities may provide an insight into capturing the microscopic dynamical processes that govern the network topology.

  14. Forman curvature for complex networks

    NASA Astrophysics Data System (ADS)

    Sreejith, R. P.; Mohanraj, Karthikeyan; Jost, Jürgen; Saucan, Emil; Samal, Areejit

    2016-06-01

    We adapt Forman’s discretization of Ricci curvature to the case of undirected networks, both weighted and unweighted, and investigate the measure in a variety of model and real-world networks. We find that most nodes and edges in model and real networks have a negative curvature. Furthermore, the distribution of Forman curvature of nodes and edges is narrow in random and small-world networks, while the distribution is broad in scale-free and real-world networks. In most networks, Forman curvature is found to display significant negative correlation with degree and centrality measures. However, Forman curvature is uncorrelated with clustering coefficient in most networks. Importantly, we find that both model and real networks are vulnerable to targeted deletion of nodes with highly negative Forman curvature. Our results suggest that Forman curvature can be employed to gain novel insights on the organization of complex networks.

  15. Matrix measure method for global exponential stability of complex-valued recurrent neural networks with time-varying delays.

    PubMed

    Gong, Weiqiang; Liang, Jinling; Cao, Jinde

    2015-10-01

    In this paper, based on the matrix measure method and the Halanay inequality, global exponential stability problem is investigated for the complex-valued recurrent neural networks with time-varying delays. Without constructing any Lyapunov functions, several sufficient criteria are obtained to ascertain the global exponential stability of the addressed complex-valued neural networks under different activation functions. Here, the activation functions are no longer assumed to be derivative which is always demanded in relating references. In addition, the obtained results are easy to be verified and implemented in practice. Finally, two examples are given to illustrate the effectiveness of the obtained results.

  16. Determining electron temperature for small spherical probes from network analyzer measurements of complex impedance

    NASA Astrophysics Data System (ADS)

    Walker, D. N.; Fernsler, R. F.; Blackwell, D. D.; Amatucci, W. E.

    2008-12-01

    In earlier work, using a network analyzer, it was shown that collisionless resistance (CR) exists in the sheath of a spherical probe when driven by a small rf signal. The CR is inversely proportional to the plasma density gradient at the location where the applied angular frequency equals the plasma frequency ωpe. Recently, efforts have concentrated on a study of the low-to-intermediate frequency response of the probe to the rf signal. At sufficiently low frequencies, the CR is beyond cutoff, i.e., below the plasma frequency at the surface of the probe. Since the electron density at the probe surface decreases as a function of applied (negative) bias, the CR will extend to lower frequencies as the magnitude of negative bias increases. Therefore to eliminate both CR and ion current contributions, the frequencies presently being considered are much greater than the ion plasma frequency, ωpi, but less than the plasma frequency, ωpe(r0), where r0 is the probe radius. It is shown that, in this frequency regime, the complex impedance measurements made with a network analyzer can be used to determine electron temperature. An overview of the theory is presented along with comparisons to data sets made using three stainless steel spherical probes of different sizes in different experimental environments and different plasma parameter regimes. The temperature measurements made by this method are compared to those made by conventional Langmuir probe sweeps; the method shown here requires no curve fitting as is the usual procedure with Langmuir probes when a Maxwell-Boltzmann electron distribution is assumed. The new method requires, however, a solution of the Poisson equation to determine the approximate sheath dimensions and integrals to determine approximate plasma and sheath inductances. The solution relies on the calculation of impedance for a spherical probe immersed in a collisionless plasma and is based on a simple circuit analogy for the plasma. Finally, the

  17. Emergent Complex Network Geometry

    PubMed Central

    Wu, Zhihao; Menichetti, Giulia; Rahmede, Christoph; Bianconi, Ginestra

    2015-01-01

    Networks are mathematical structures that are universally used to describe a large variety of complex systems such as the brain or the Internet. Characterizing the geometrical properties of these networks has become increasingly relevant for routing problems, inference and data mining. In real growing networks, topological, structural and geometrical properties emerge spontaneously from their dynamical rules. Nevertheless we still miss a model in which networks develop an emergent complex geometry. Here we show that a single two parameter network model, the growing geometrical network, can generate complex network geometries with non-trivial distribution of curvatures, combining exponential growth and small-world properties with finite spectral dimensionality. In one limit, the non-equilibrium dynamical rules of these networks can generate scale-free networks with clustering and communities, in another limit planar random geometries with non-trivial modularity. Finally we find that these properties of the geometrical growing networks are present in a large set of real networks describing biological, social and technological systems. PMID:25985280

  18. Complexity measures of the central respiratory networks during wakefulness and sleep

    NASA Astrophysics Data System (ADS)

    Dragomir, Andrei; Akay, Yasemin; Curran, Aidan K.; Akay, Metin

    2008-06-01

    Since sleep is known to influence respiratory activity we studied whether the sleep state would affect the complexity value of the respiratory network output. Specifically, we tested the hypothesis that the complexity values of the diaphragm EMG (EMGdia) activity would be lower during REM compared to NREM. Furthermore, since REM is primarily generated by a homogeneous population of neurons in the medulla, the possibility that REM-related respiratory output would be less complex than that of the awake state was also considered. Additionally, in order to examine the influence of neuron vulnerabilities within the rostral ventral medulla (RVM) on the complexity of the respiratory network output, we inhibited respiratory neurons in the RVM by microdialysis of GABAA receptor agonist muscimol. Diaphragm EMG, nuchal EMG, EEG, EOG as well as other physiological signals (tracheal pressure, blood pressure and respiratory volume) were recorded from five unanesthetized chronically instrumented intact piglets (3-10 days old). Complexity of the diaphragm EMG (EMGdia) signal during wakefulness, NREM and REM was evaluated using the approximate entropy method (ApEn). ApEn values of the EMGdia during NREM and REM sleep were found significantly (p < 0.05 and p < 0.001, respectively) lower than those of awake EMGdia after muscimol inhibition. In the absence of muscimol, only the differences between REM and wakefulness ApEn values were found to be significantly different.

  19. Organization of complex networks

    NASA Astrophysics Data System (ADS)

    Kitsak, Maksim

    Many large complex systems can be successfully analyzed using the language of graphs and networks. Interactions between the objects in a network are treated as links connecting nodes. This approach to understanding the structure of networks is an important step toward understanding the way corresponding complex systems function. Using the tools of statistical physics, we analyze the structure of networks as they are found in complex systems such as the Internet, the World Wide Web, and numerous industrial and social networks. In the first chapter we apply the concept of self-similarity to the study of transport properties in complex networks. Self-similar or fractal networks, unlike non-fractal networks, exhibit similarity on a range of scales. We find that these fractal networks have transport properties that differ from those of non-fractal networks. In non-fractal networks, transport flows primarily through the hubs. In fractal networks, the self-similar structure requires any transport to also flow through nodes that have only a few connections. We also study, in models and in real networks, the crossover from fractal to non-fractal networks that occurs when a small number of random interactions are added by means of scaling techniques. In the second chapter we use k-core techniques to study dynamic processes in networks. The k-core of a network is the network's largest component that, within itself, exhibits all nodes with at least k connections. We use this k-core analysis to estimate the relative leadership positions of firms in the Life Science (LS) and Information and Communication Technology (ICT) sectors of industry. We study the differences in the k-core structure between the LS and the ICT sectors. We find that the lead segment (highest k-core) of the LS sector, unlike that of the ICT sector, is remarkably stable over time: once a particular firm enters the lead segment, it is likely to remain there for many years. In the third chapter we study how

  20. Oscillations of complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Xingang; Lai, Ying-Cheng; Lai, Choy Heng

    2006-12-01

    A complex network processing information or physical flows is usually characterized by a number of macroscopic quantities such as the diameter and the betweenness centrality. An issue of significant theoretical and practical interest is how such quantities respond to sudden changes caused by attacks or disturbances in recoverable networks, i.e., functions of the affected nodes are only temporarily disabled or partially limited. By introducing a model to address this issue, we find that, for a finite-capacity network, perturbations can cause the network to oscillate persistently in the sense that the characterizing quantities vary periodically or randomly with time. We provide a theoretical estimate of the critical capacity-parameter value for the onset of the network oscillation. The finding is expected to have broad implications as it suggests that complex networks may be structurally highly dynamic.

  1. Hyperbolic geometry of complex networks.

    PubMed

    Krioukov, Dmitri; Papadopoulos, Fragkiskos; Kitsak, Maksim; Vahdat, Amin; Boguñá, Marián

    2010-09-01

    We develop a geometric framework to study the structure and function of complex networks. We assume that hyperbolic geometry underlies these networks, and we show that with this assumption, heterogeneous degree distributions and strong clustering in complex networks emerge naturally as simple reflections of the negative curvature and metric property of the underlying hyperbolic geometry. Conversely, we show that if a network has some metric structure, and if the network degree distribution is heterogeneous, then the network has an effective hyperbolic geometry underneath. We then establish a mapping between our geometric framework and statistical mechanics of complex networks. This mapping interprets edges in a network as noninteracting fermions whose energies are hyperbolic distances between nodes, while the auxiliary fields coupled to edges are linear functions of these energies or distances. The geometric network ensemble subsumes the standard configuration model and classical random graphs as two limiting cases with degenerate geometric structures. Finally, we show that targeted transport processes without global topology knowledge, made possible by our geometric framework, are maximally efficient, according to all efficiency measures, in networks with strongest heterogeneity and clustering, and that this efficiency is remarkably robust with respect to even catastrophic disturbances and damages to the network structure.

  2. Directed weighted network structure analysis of complex impedance measurements for characterizing oil-in-water bubbly flow

    NASA Astrophysics Data System (ADS)

    Gao, Zhong-Ke; Dang, Wei-Dong; Xue, Le; Zhang, Shan-Shan

    2017-03-01

    Characterizing the flow structure underlying the evolution of oil-in-water bubbly flow remains a contemporary challenge of great interests and complexity. In particular, the oil droplets dispersing in a water continuum with diverse size make the study of oil-in-water bubbly flow really difficult. To study this issue, we first design a novel complex impedance sensor and systematically conduct vertical oil-water flow experiments. Based on the multivariate complex impedance measurements, we define modalities associated with the spatial transient flow structures and construct modality transition-based network for each flow condition to study the evolution of flow structures. In order to reveal the unique flow structures underlying the oil-in-water bubbly flow, we filter the inferred modality transition-based network by removing the edges with small weight and resulting isolated nodes. Then, the weighted clustering coefficient entropy and weighted average path length are employed for quantitatively assessing the original network and filtered network. The differences in network measures enable to efficiently characterize the evolution of the oil-in-water bubbly flow structures.

  3. Multiscale vulnerability of complex networks.

    PubMed

    Boccaletti, Stefano; Buldú, Javier; Criado, Regino; Flores, Julio; Latora, Vito; Pello, Javier; Romance, Miguel

    2007-12-01

    We present a novel approach to quantify the vulnerability of a complex network, i.e., the capacity of a graph to maintain its functional performance under random damages or malicious attacks. The proposed measure represents a multiscale evaluation of vulnerability, and makes use of combined powers of the links' betweenness. We show that the proposed approach is able to properly describe some cases for which earlier measures of vulnerability fail. The relevant applications of our method for technological network design are outlined.

  4. Compressively sensed complex networks.

    SciTech Connect

    Dunlavy, Daniel M.; Ray, Jaideep; Pinar, Ali

    2010-07-01

    The aim of this project is to develop low dimension parametric (deterministic) models of complex networks, to use compressive sensing (CS) and multiscale analysis to do so and to exploit the structure of complex networks (some are self-similar under coarsening). CS provides a new way of sampling and reconstructing networks. The approach is based on multiresolution decomposition of the adjacency matrix and its efficient sampling. It requires preprocessing of the adjacency matrix to make it 'blocky' which is the biggest (combinatorial) algorithm challenge. Current CS reconstruction algorithm makes no use of the structure of a graph, its very general (and so not very efficient/customized). Other model-based CS techniques exist, but not yet adapted to networks. Obvious starting point for future work is to increase the efficiency of reconstruction.

  5. Synchronization in complex networks

    SciTech Connect

    Arenas, A.; Diaz-Guilera, A.; Moreno, Y.; Zhou, C.; Kurths, J.

    2007-12-12

    Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.

  6. Complex Semantic Networks

    NASA Astrophysics Data System (ADS)

    Teixeira, G. M.; Aguiar, M. S. F.; Carvalho, C. F.; Dantas, D. R.; Cunha, M. V.; Morais, J. H. M.; Pereira, H. B. B.; Miranda, J. G. V.

    Verbal language is a dynamic mental process. Ideas emerge by means of the selection of words from subjective and individual characteristics throughout the oral discourse. The goal of this work is to characterize the complex network of word associations that emerge from an oral discourse from a discourse topic. Because of that, concepts of associative incidence and fidelity have been elaborated and represented the probability of occurrence of pairs of words in the same sentence in the whole oral discourse. Semantic network of words associations were constructed, where the words are represented as nodes and the edges are created when the incidence-fidelity index between pairs of words exceeds a numerical limit (0.001). Twelve oral discourses were studied. The networks generated from these oral discourses present a typical behavior of complex networks and their indices were calculated and their topologies characterized. The indices of these networks obtained from each incidence-fidelity limit exhibit a critical value in which the semantic network has maximum conceptual information and minimum residual associations. Semantic networks generated by this incidence-fidelity limit depict a pattern of hierarchical classes that represent the different contexts used in the oral discourse.

  7. Calibration of a Hall effect displacement measurement system for complex motion analysis using a neural network.

    PubMed

    Northey, G W; Oliver, M L; Rittenhouse, D M

    2006-01-01

    Biomechanics studies often require the analysis of position and orientation. Although a variety of transducer and camera systems can be utilized, a common inexpensive alternative is the Hall effect sensor. Hall effect sensors have been used extensively for one-dimensional position analysis but their non-linear behavior and cross-talk effects make them difficult to calibrate for effective and accurate two- and three-dimensional position and orientation analysis. The aim of this study was to develop and calibrate a displacement measurement system for a hydraulic-actuation joystick used for repetitive motion analysis of heavy equipment operators. The system utilizes an array of four Hall effect sensors that are all active during any joystick movement. This built-in redundancy allows the calibration to utilize fully connected feed forward neural networks in conjunction with a Microscribe 3D digitizer. A fully connected feed forward neural network with one hidden layer containing five neurons was developed. Results indicate that the ability of the neural network to accurately predict the x, y and z coordinates of the joystick handle was good with r(2) values of 0.98 and higher. The calibration technique was found to be equally as accurate when used on data collected 5 days after the initial calibration, indicating the system is robust and stable enough to not require calibration every time the joystick is used. This calibration system allowed an infinite number of joystick orientations and positions to be found within the range of joystick motion.

  8. 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.

  9. Target recovery in complex networks

    NASA Astrophysics Data System (ADS)

    Sun, Weiman; Zeng, An

    2017-01-01

    The invulnerability of complex networks is an important issue which has been widely analyzed in different fields. A lot of works have been done to measure and improve the stability of complex networks when being attacked. Recently, how to recover networks after attack was intensively studied. The existing methods are mainly designed to recover the overall functionality of networks, yet in many real cases the recovery of important nodes should be given priority, to which we refer target recovery. For example, when the cold wave paralyses the railway networks, target recovery means to repair those stations or railways such that the transport capacity of densely-populated cities can be recovered as fast as possible. In this paper, we first compare the impact of attacks on the whole network and target nodes respectively, and then study the efficiency of traditional recovery methods that are proposed based on global centrality metrics. Furthermore, based on target centrality metrics, we introduce a local betweenness recovery method and we find it has better performance than the traditional methods. We finally propose a hybrid recovery method which includes local betweenness metric and local closeness metric. The performance of the hybrid method is shown to be similar to that of the greedy algorithm.

  10. Articulation points in complex networks

    PubMed Central

    Tian, Liang; Bashan, Amir; Shi, Da-Ning; Liu, Yang-Yu

    2017-01-01

    An articulation point in a network is a node whose removal disconnects the network. Those nodes play key roles in ensuring connectivity of many real-world networks, from infrastructure networks to protein interaction networks and terrorist communication networks. Despite their fundamental importance, a general framework of studying articulation points in complex networks is lacking. Here we develop analytical tools to study key issues pertinent to articulation points, such as the expected number of them and the network vulnerability against their removal, in an arbitrary complex network. We find that a greedy articulation point removal process provides us a different perspective on the organizational principles of complex networks. Moreover, this process results in a rich phase diagram with two fundamentally different types of percolation transitions. Our results shed light on the design of more resilient infrastructure networks and the effective destruction of terrorist communication networks. PMID:28139697

  11. Articulation points in complex networks

    NASA Astrophysics Data System (ADS)

    Tian, Liang; Bashan, Amir; Shi, Da-Ning; Liu, Yang-Yu

    2017-01-01

    An articulation point in a network is a node whose removal disconnects the network. Those nodes play key roles in ensuring connectivity of many real-world networks, from infrastructure networks to protein interaction networks and terrorist communication networks. Despite their fundamental importance, a general framework of studying articulation points in complex networks is lacking. Here we develop analytical tools to study key issues pertinent to articulation points, such as the expected number of them and the network vulnerability against their removal, in an arbitrary complex network. We find that a greedy articulation point removal process provides us a different perspective on the organizational principles of complex networks. Moreover, this process results in a rich phase diagram with two fundamentally different types of percolation transitions. Our results shed light on the design of more resilient infrastructure networks and the effective destruction of terrorist communication networks.

  12. Physical controllability of complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Le-Zhi; Chen, Yu-Zhong; Wang, Wen-Xu; Lai, Ying-Cheng

    2017-01-01

    A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.

  13. Physical controllability of complex networks

    PubMed Central

    Wang, Le-Zhi; Chen, Yu-Zhong; Wang, Wen-Xu; Lai, Ying-Cheng

    2017-01-01

    A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control. PMID:28074900

  14. Complex Networks and Socioeconomic Applications

    NASA Astrophysics Data System (ADS)

    Almendral, Juan A.; López, Luis; Mendes, Jose F.; Sanjuán, Miguel A. F.

    2003-04-01

    The study and characterization of complex systems is a fruitful research area nowadays. Special attention has been paid recently to complex networks, where graph and network analysis plays an important role since they reduce a given system to a simpler problem. Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders because the global influence each actor has within the network is completely determined by the hierarchical level occupied. The effect on the efficiency caused by a change in a traditional hierarchical topology is also analyzed. In particular, by introducing the possibility of communication on the same level of the hierarchy.

  15. A microfluidic device for simultaneous measurement of viscosity and flow rate of blood in a complex fluidic network

    PubMed Central

    Jun Kang, Yang; Yeom, Eunseop; Lee, Sang-Joon

    2013-01-01

    Blood viscosity has been considered as one of important biophysical parameters for effectively monitoring variations in physiological and pathological conditions of circulatory disorders. Standard previous methods make it difficult to evaluate variations of blood viscosity under cardiopulmonary bypass procedures or hemodialysis. In this study, we proposed a unique microfluidic device for simultaneously measuring viscosity and flow rate of whole blood circulating in a complex fluidic network including a rat, a reservoir, a pinch valve, and a peristaltic pump. To demonstrate the proposed method, a twin-shaped microfluidic device, which is composed of two half-circular chambers, two side channels with multiple indicating channels, and one bridge channel, was carefully designed. Based on the microfluidic device, three sequential flow controls were applied to identify viscosity and flow rate of blood, with label-free and sensorless detection. The half-circular chamber was employed to achieve mechanical membrane compliance for flow stabilization in the microfluidic device. To quantify the effect of flow stabilization on flow fluctuations, a formula of pulsation index (PI) was analytically derived using a discrete fluidic circuit model. Using the PI formula, the time constant contributed by the half-circular chamber is estimated to be 8 s. Furthermore, flow fluctuations resulting from the peristaltic pumps are completely removed, especially under periodic flow conditions within short periods (T < 10 s). For performance demonstrations, the proposed method was applied to evaluate blood viscosity with respect to varying flow rate conditions [(a) known blood flow rate via a syringe pump, (b) unknown blood flow rate via a peristaltic pump]. As a result, the flow rate and viscosity of blood can be simultaneously measured with satisfactory accuracy. In addition, the proposed method was successfully applied to identify the viscosity of rat blood, which circulates in a

  16. Dynamics of and on complex networks

    NASA Astrophysics Data System (ADS)

    Halu, Arda

    Complex networks are dynamic, evolving structures that can host a great number of dynamical processes. In this thesis, we address current challenges regarding the dynamics of and dynamical processes on complex networks. First, we study complex network dynamics from the standpoint of network growth. As a quantitative measure of the complexity and information content of networks generated by growing network models, we define and evaluate their entropy rate. We propose stochastic growth models inspired by the duplication-divergence mechanism to generate epistatic interaction networks and find that they exhibit the property of monochromaticity as a result of their dynamical evolution. Second, we explore the dynamics of quantum mechanical processes on complex networks. We investigate the Bose-Hubbard model on annealed and quenched scale-free networks as well as Apollonian networks and show that their phase diagram changes significantly in the presence of complex topologies, depending on the second degree of the degree distribution and the maximal eigenvalue of the adjacency matrix. We then study the Jaynes-Cummings-Hubbard model on various complex topologies and demonstrate the importance of the maximal eigenvalue of the hopping matrix in determining the phase diagram of the model. Third, we investigate dynamical processes on interacting and multiplex networks. We study opinion dynamics in a simulated setting of two antagonistically interacting networks and recover the importance of connectivity and committed agents. We propose a multiplex centrality measure that takes into account the connectivity patterns within and across different layers and find that the dynamics of biased random walks on multiplex networks gives rise to a centrality ranking that is different from univariate centrality measures. Finally, we study the statistical mechanics of multilayered spatial networks and demonstrate the emergence of significant link overlap and improved navigability in

  17. Role models for complex networks

    NASA Astrophysics Data System (ADS)

    Reichardt, J.; White, D. R.

    2007-11-01

    We present a framework for automatically decomposing (“block-modeling”) the functional classes of agents within a complex network. These classes are represented by the nodes of an image graph (“block model”) depicting the main patterns of connectivity and thus functional roles in the network. Using a first principles approach, we derive a measure for the fit of a network to any given image graph allowing objective hypothesis testing. From the properties of an optimal fit, we derive how to find the best fitting image graph directly from the network and present a criterion to avoid overfitting. The method can handle both two-mode and one-mode data, directed and undirected as well as weighted networks and allows for different types of links to be dealt with simultaneously. It is non-parametric and computationally efficient. The concepts of structural equivalence and modularity are found as special cases of our approach. We apply our method to the world trade network and analyze the roles individual countries play in the global economy.

  18. Online Community Detection for Large Complex Networks

    PubMed Central

    Pan, Gang; Zhang, Wangsheng; Wu, Zhaohui; Li, Shijian

    2014-01-01

    Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance. PMID:25061683

  19. Graph distance for complex networks

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

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

  20. Graph distance for complex networks

    PubMed Central

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

    2016-01-01

    Networks are widely used as a tool for describing diverse real complex systems and have been successfully applied to many fields. The distance between networks is one of the most fundamental concepts for properly classifying real networks, detecting temporal changes in network structures, and effectively predicting their temporal evolution. However, this distance has rarely been discussed in the theory of complex networks. Here, we propose a graph distance between networks based on a Laplacian matrix that reflects the structural and dynamical properties of networked dynamical systems. Our results indicate that the Laplacian-based graph distance effectively quantifies the structural difference between complex networks. We further show that our approach successfully elucidates the temporal properties underlying temporal networks observed in the context of face-to-face human interactions. PMID:27725690

  1. Control Capacity in Complex Networks

    NASA Astrophysics Data System (ADS)

    Jia, Tao; Liu, Yang-Yu; Slotine, Jean-Jacques; Barabasi, Albert-Laszlo

    2012-02-01

    By combining tools from control theory and network science, an efficient methodology was proposed to identify the minimum sets of driver nodes, whose time-dependent control can guide the whole network to any desired final state. Yet, this minimum driver set (MDS) is usually not unique, but one can often achieve multiple potential control configurations with the same number of driver nodes. Given that some nodes may appear in some MDSs but not in other, a crucial question remain unanswered: what is the role of individual node in controlling a complex system? We first classify a node as critical, redundant, or ordinary if it appears in all, no, or some MDSs. Then we introduce the concept of control capacity as a measure of the frequency that a node is in the MDSs, which quantifies the importance of a given node in maintaining Controllability. To avoid impractical enumeration of all MDSs, we propose an algorithm that uniformly samples the MDS. We use it to explore the control capacity of nodes in complex networks and study how it is related to other characteristics of the network topology.

  2. Exact controllability of complex networks

    PubMed Central

    Yuan, Zhengzhong; Zhao, Chen; Di, Zengru; Wang, Wen-Xu; Lai, Ying-Cheng

    2013-01-01

    Controlling complex networks is of paramount importance in science and engineering. Despite the recent development of structural controllability theory, we continue to lack a framework to control undirected complex networks, especially given link weights. Here we introduce an exact controllability paradigm based on the maximum multiplicity to identify the minimum set of driver nodes required to achieve full control of networks with arbitrary structures and link-weight distributions. The framework reproduces the structural controllability of directed networks characterized by structural matrices. We explore the controllability of a large number of real and model networks, finding that dense networks with identical weights are difficult to be controlled. An efficient and accurate tool is offered to assess the controllability of large sparse and dense networks. The exact controllability framework enables a comprehensive understanding of the impact of network properties on controllability, a fundamental problem towards our ultimate control of complex systems. PMID:24025746

  3. Complex networks analysis of language complexity

    NASA Astrophysics Data System (ADS)

    Amancio, Diego R.; Aluisio, Sandra M.; Oliveira, Osvaldo N., Jr.; Costa, Luciano da F.

    2012-12-01

    Methods from statistical physics, such as those involving complex networks, have been increasingly used in the quantitative analysis of linguistic phenomena. In this paper, we represented pieces of text with different levels of simplification in co-occurrence networks and found that topological regularity correlated negatively with textual complexity. Furthermore, in less complex texts the distance between concepts, represented as nodes, tended to decrease. The complex networks metrics were treated with multivariate pattern recognition techniques, which allowed us to distinguish between original texts and their simplified versions. For each original text, two simplified versions were generated manually with increasing number of simplification operations. As expected, distinction was easier for the strongly simplified versions, where the most relevant metrics were node strength, shortest paths and diversity. Also, the discrimination of complex texts was improved with higher hierarchical network metrics, thus pointing to the usefulness of considering wider contexts around the concepts. Though the accuracy rate in the distinction was not as high as in methods using deep linguistic knowledge, the complex network approach is still useful for a rapid screening of texts whenever assessing complexity is essential to guarantee accessibility to readers with limited reading ability.

  4. Security of Complex Networks

    DTIC Science & Technology

    2010-02-18

    social network (DS), (8) network of American football games among colleges (AFC), (9) social network of friendships of a karate club (FKC), (10...Ax2 = 12 book karate football ’•■ electronic circuit dolphins a C. Elegans 102 Figure 8: For Universal scaling law for six real-world

  5. Approaching human language with complex networks

    NASA Astrophysics Data System (ADS)

    Cong, Jin; Liu, Haitao

    2014-12-01

    The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics).

  6. Approaching human language with complex networks.

    PubMed

    Cong, Jin; Liu, Haitao

    2014-12-01

    The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics).

  7. Aging, frailty and complex networks.

    PubMed

    Mitnitski, A B; Rutenberg, A D; Farrell, S; Rockwood, K

    2017-03-02

    When people age their mortality rate increases exponentially, following Gompertz's law. Even so, individuals do not die from old age. Instead, they accumulate age-related illnesses and conditions and so become increasingly vulnerable to death from various external and internal stressors. As a measure of such vulnerability, frailty can be quantified using the frailty index (FI). Larger values of the FI are strongly associated with mortality and other adverse health outcomes. This association, and the insensitivity of the FI to the particular health variables that are included in its construction, makes it a powerful, convenient, and increasingly popular integrative health measure. Still, little is known about why the FI works so well. Our group has recently developed a theoretical network model of health deficits to better understand how changes in health are captured by the FI. In our model, health-related variables are represented by the nodes of a complex network. The network has a scale-free shape or "topology": a few nodes have many connections with other nodes, whereas most nodes have few connections. These nodes can be in two states, either damaged or undamaged. Transitions between damaged and non-damaged states are governed by the stochastic environment of individual nodes. Changes in the degree of damage of connected nodes change the local environment and make further damage more likely. Our model shows how age-dependent acceleration of the FI and of mortality emerges, even without specifying an age-damage relationship or any other time-dependent parameter. We have also used our model to assess how informative individual deficits are with respect to mortality. We find that the information is larger for nodes that are well connected than for nodes that are not. The model supports the idea that aging occurs as an emergent phenomenon, and not as a result of age-specific programming. Instead, aging reflects how damage propagates through a complex network of

  8. A Measure of Systems Engineering Effectiveness in Government Acquisition of Complex Information Systems: A Bayesian Belief Network-Based Approach

    ERIC Educational Resources Information Center

    Doskey, Steven Craig

    2014-01-01

    This research presents an innovative means of gauging Systems Engineering effectiveness through a Systems Engineering Relative Effectiveness Index (SE REI) model. The SE REI model uses a Bayesian Belief Network to map causal relationships in government acquisitions of Complex Information Systems (CIS), enabling practitioners to identify and…

  9. Quantization Effects on Complex Networks

    PubMed Central

    Wang, Ying; Wang, Lin; Yang, Wen; Wang, Xiaofan

    2016-01-01

    Weights of edges in many complex networks we constructed are quantized values of the real weights. To what extent does the quantization affect the properties of a network? In this work, quantization effects on network properties are investigated based on the spectrum of the corresponding Laplacian. In contrast to the intuition that larger quantization level always implies a better approximation of the quantized network to the original one, we find a ubiquitous periodic jumping phenomenon with peak-value decreasing in a power-law relationship in all the real-world weighted networks that we investigated. We supply theoretical analysis on the critical quantization level and the power laws. PMID:27226049

  10. The physics of communicability in complex networks

    NASA Astrophysics Data System (ADS)

    Estrada, Ernesto; Hatano, Naomichi; Benzi, Michele

    2012-05-01

    A fundamental problem in the study of complex networks is to provide quantitative measures of correlation and information flow between different parts of a system. To this end, several notions of communicability have been introduced and applied to a wide variety of real-world networks in recent years. Several such communicability functions are reviewed in this paper. It is emphasized that communication and correlation in networks can take place through many more routes than the shortest paths, a fact that may not have been sufficiently appreciated in previously proposed correlation measures. In contrast to these, the communicability measures reviewed in this paper are defined by taking into account all possible routes between two nodes, assigning smaller weights to longer ones. This point of view naturally leads to the definition of communicability in terms of matrix functions, such as the exponential, resolvent, and hyperbolic functions, in which the matrix argument is either the adjacency matrix or the graph Laplacian associated with the network. Considerable insight on communicability can be gained by modeling a network as a system of oscillators and deriving physical interpretations, both classical and quantum-mechanical, of various communicability functions. Applications of communicability measures to the analysis of complex systems are illustrated on a variety of biological, physical and social networks. The last part of the paper is devoted to a review of the notion of locality in complex networks and to computational aspects that by exploiting sparsity can greatly reduce the computational efforts for the calculation of communicability functions for large networks.

  11. Language Networks as Complex Systems

    ERIC Educational Resources Information Center

    Lee, Max Kueiming; Ou, Sheue-Jen

    2008-01-01

    Starting in the late eighties, with a growing discontent with analytical methods in science and the growing power of computers, researchers began to study complex systems such as living organisms, evolution of genes, biological systems, brain neural networks, epidemics, ecology, economy, social networks, etc. In the early nineties, the research…

  12. "Conjectural" links in complex networks

    NASA Astrophysics Data System (ADS)

    Snarskii, A. A.; Zorinets, D. I.; Lande, D. V.

    2016-11-01

    This paper introduces the concept of Conjectural Link for Complex Networks, in particular, social networks. Conjectural Link we understand as an implicit link, not available in the network, but supposed to be present, based on the characteristics of its topology. It is possible, for example, when in the formal description of the network some connections are skipped due to errors, deliberately hidden or withdrawn (e.g. in the case of partial destruction of the network). Introduced a parameter that allows ranking the Conjectural Link. The more this parameter - the more likely that this connection should be present in the network. This paper presents a method of recovery of partially destroyed Complex Networks using Conjectural Links finding. Presented two methods of finding the node pairs that are not linked directly to one another, but have a great possibility of Conjectural Link communication among themselves: a method based on the determination of the resistance between two nodes, and method based on the computation of the lengths of routes between two nodes. Several examples of real networks are reviewed and performed a comparison to know network links prediction methods, not intended to find the missing links in already formed networks.

  13. Kleinberg Complex Networks

    DTIC Science & Technology

    2014-10-21

    networks. With economists Larry Blume and David Easley, and computer scientists Jon Kleinberg and Éva Tardos, we investigated so-called threshold...and Nicole Immorlica [13], we investigated Schelling’s segregation model, a famous model of residential segregation that has been observed, in...approximation ratio achieves the first provable improvement upon the approximation ratio of the famous algorithm introduced by Christofides in 1976 (which

  14. Control efficacy of complex networks

    PubMed Central

    Gao, Xin-Dong; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-01-01

    Controlling complex networks has become a forefront research area in network science and engineering. Recent efforts have led to theoretical frameworks of controllability to fully control a network through steering a minimum set of driver nodes. However, in realistic situations not every node is accessible or can be externally driven, raising the fundamental issue of control efficacy: if driving signals are applied to an arbitrary subset of nodes, how many other nodes can be controlled? We develop a framework to determine the control efficacy for undirected networks of arbitrary topology. Mathematically, based on non-singular transformation, we prove a theorem to determine rigorously the control efficacy of the network and to identify the nodes that can be controlled for any given driver nodes. Physically, we develop the picture of diffusion that views the control process as a signal diffused from input signals to the set of controllable nodes. The combination of mathematical theory and physical reasoning allows us not only to determine the control efficacy for model complex networks and a large number of empirical networks, but also to uncover phenomena in network control, e.g., hub nodes in general possess lower control centrality than an average node in undirected networks. PMID:27324438

  15. Control efficacy of complex networks

    NASA Astrophysics Data System (ADS)

    Gao, Xin-Dong; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-06-01

    Controlling complex networks has become a forefront research area in network science and engineering. Recent efforts have led to theoretical frameworks of controllability to fully control a network through steering a minimum set of driver nodes. However, in realistic situations not every node is accessible or can be externally driven, raising the fundamental issue of control efficacy: if driving signals are applied to an arbitrary subset of nodes, how many other nodes can be controlled? We develop a framework to determine the control efficacy for undirected networks of arbitrary topology. Mathematically, based on non-singular transformation, we prove a theorem to determine rigorously the control efficacy of the network and to identify the nodes that can be controlled for any given driver nodes. Physically, we develop the picture of diffusion that views the control process as a signal diffused from input signals to the set of controllable nodes. The combination of mathematical theory and physical reasoning allows us not only to determine the control efficacy for model complex networks and a large number of empirical networks, but also to uncover phenomena in network control, e.g., hub nodes in general possess lower control centrality than an average node in undirected networks.

  16. Information communication on complex networks

    NASA Astrophysics Data System (ADS)

    Igarashi, Akito; Kawamoto, Hiroki; Maruyama, Takahiro; Morioka, Atsushi; Naganuma, Yuki

    2013-02-01

    Since communication networks such as the Internet, which is regarded as a complex network, have recently become a huge scale and a lot of data pass through them, the improvement of packet routing strategies for transport is one of the most significant themes in the study of computer networks. It is especially important to find routing strategies which can bear as many traffic as possible without congestion in complex networks. First, using neural networks, we introduce a strategy for packet routing on complex networks, where path lengths and queue lengths in nodes are taken into account within a framework of statistical physics. Secondly, instead of using shortest paths, we propose efficient paths which avoid hubs, nodes with a great many degrees, on scale-free networks with a weight of each node. We improve the heuristic algorithm proposed by Danila et. al. which optimizes step by step routing properties on congestion by using the information of betweenness, the probability of paths passing through a node in all optimal paths which are defined according to a rule, and mitigates the congestion. We confirm the new heuristic algorithm which balances traffic on networks by achieving minimization of the maximum betweenness in much smaller number of iteration steps. Finally, We model virus spreading and data transfer on peer-to-peer (P2P) networks. Using mean-field approximation, we obtain an analytical formulation and emulate virus spreading on the network and compare the results with those of simulation. Moreover, we investigate the mitigation of information traffic congestion in the P2P networks.

  17. Spreading dynamics in complex networks

    NASA Astrophysics Data System (ADS)

    Pei, Sen; Makse, Hernán A.

    2013-12-01

    Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community—LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality.

  18. Attack Robustness and Centrality of Complex Networks

    PubMed Central

    Iyer, Swami; Killingback, Timothy; Sundaram, Bala; Wang, Zhen

    2013-01-01

    Many complex systems can be described by networks, in which the constituent components are represented by vertices and the connections between the components are represented by edges between the corresponding vertices. A fundamental issue concerning complex networked systems is the robustness of the overall system to the failure of its constituent parts. Since the degree to which a networked system continues to function, as its component parts are degraded, typically depends on the integrity of the underlying network, the question of system robustness can be addressed by analyzing how the network structure changes as vertices are removed. Previous work has considered how the structure of complex networks change as vertices are removed uniformly at random, in decreasing order of their degree, or in decreasing order of their betweenness centrality. Here we extend these studies by investigating the effect on network structure of targeting vertices for removal based on a wider range of non-local measures of potential importance than simply degree or betweenness. We consider the effect of such targeted vertex removal on model networks with different degree distributions, clustering coefficients and assortativity coefficients, and for a variety of empirical networks. PMID:23565156

  19. Dynamic and interacting complex networks

    NASA Astrophysics Data System (ADS)

    Dickison, Mark E.

    This thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N2/3 in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N1/2, compared to N1/3 in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible

  20. Porous Soil as Complex Network

    NASA Astrophysics Data System (ADS)

    Benito, R. M.; Santiago, A.; Cárdenas, J. P.; Tarquis, A. M.; Borondo, F.; Losada, J. C.

    2009-04-01

    We present a complex network model based on a heterogeneous preferential attachment scheme [1,2] to quantify the structure of porous soils [3]. Under this perspective pores are represented by nodes and the space for the flow of fluids between them are represented by links. Pore properties such as position and size are described by fixed states in a metric space, while an affinity function is introduced to bias the attachment probabilities of links according to these properties. We perform an analytical and numerical study of the degree distributions in the soil model and show that under reasonable conditions all the model variants yield a multiscaling behavior in the connectivity degrees, leaving a empirically testable signature of heterogeneity in the topology of pore networks. References [1] A. Santiago and R. M. Benito, "Emergence of multiscaling in heterogeneous complex networks". Int. J. Mod. Phys. C 18, 1591 (2007). [2] A. Santiago and R. M. Benito, "An extended formalism for preferential attachment in heterogeneous complex networks". Europhys. Lett. 82, 58004 (2008). [3] A. Santiago, R. M. Benito, J. P. Cárdenas, J. C. Losada, A. M. Tarquis and F. Borondo, "Multiscaling of porous soils as heterogeneous complex networks". Nonl. Proc. Geophys. 15, 1-10 (2008).

  1. Complex Networks in Psychological Models

    NASA Astrophysics Data System (ADS)

    Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

    We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

  2. Robustness Elasticity in Complex Networks

    PubMed Central

    Matisziw, Timothy C.; Grubesic, Tony H.; Guo, Junyu

    2012-01-01

    Network robustness refers to a network’s resilience to stress or damage. Given that most networks are inherently dynamic, with changing topology, loads, and operational states, their robustness is also likely subject to change. However, in most analyses of network structure, it is assumed that interaction among nodes has no effect on robustness. To investigate the hypothesis that network robustness is not sensitive or elastic to the level of interaction (or flow) among network nodes, this paper explores the impacts of network disruption, namely arc deletion, over a temporal sequence of observed nodal interactions for a large Internet backbone system. In particular, a mathematical programming approach is used to identify exact bounds on robustness to arc deletion for each epoch of nodal interaction. Elasticity of the identified bounds relative to the magnitude of arc deletion is assessed. Results indicate that system robustness can be highly elastic to spatial and temporal variations in nodal interactions within complex systems. Further, the presence of this elasticity provides evidence that a failure to account for nodal interaction can confound characterizations of complex networked systems. PMID:22808060

  3. Dynamic information routing in complex networks

    PubMed Central

    Kirst, Christoph; Timme, Marc; Battaglia, Demian

    2016-01-01

    Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function. PMID:27067257

  4. Dynamic information routing in complex networks

    NASA Astrophysics Data System (ADS)

    Kirst, Christoph; Timme, Marc; Battaglia, Demian

    2016-04-01

    Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function.

  5. Viral quasispecies complexity measures.

    PubMed

    Gregori, Josep; Perales, Celia; Rodriguez-Frias, Francisco; Esteban, Juan I; Quer, Josep; Domingo, Esteban

    2016-06-01

    Mutant spectrum dynamics (changes in the related mutants that compose viral populations) has a decisive impact on virus behavior. The several platforms of next generation sequencing (NGS) to study viral quasispecies offer a magnifying glass to study viral quasispecies complexity. Several parameters are available to quantify the complexity of mutant spectra, but they have limitations. Here we critically evaluate the information provided by several population diversity indices, and we propose the introduction of some new ones used in ecology. In particular we make a distinction between incidence, abundance and function measures of viral quasispecies composition. We suggest a multidimensional approach (complementary information contributed by adequately chosen indices), propose some guidelines, and illustrate the use of indices with a simple example. We apply the indices to three clinical samples of hepatitis C virus that display different population heterogeneity. Areas of virus biology in which population complexity plays a role are discussed.

  6. Cyberspace Assurance Metrics: Utilizing Models of Networks, Complex Systems Theory, Multidimensional Wavelet Analysis, and Generalized Entrophy Measures

    DTIC Science & Technology

    2005-04-01

    the topological specification of a network in the form of a connection matrix and the branches of mathematics known as continuous group theory and...Connection Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3 2.2 Dynamical Network Changes...essential aspects of the network into just a few metrics (functions of the network matrix values). It is well known that a network is characterized

  7. Quantum physics and complex networks

    NASA Astrophysics Data System (ADS)

    Biamonte, Jacob

    2014-03-01

    There is a widely used and successful theory of ``chemical reaction networks,'' which provides a framework describing systems governed by mass action kinetics. Computer science and population biology use the same ideas under a different name: ``stochastic Petri nets.'' But if we look at these theories from the perspective of quantum theory, they turn out to involve creation and annihilation operators, coherent states and other well-known ideas--yet in a context where probabilities replace amplitudes. I will explain this connection as part of a detailed analogy between quantum mechanics and stochastic mechanics which we've produced several results on recently, including the recent analytical results uniting quantum physics and complex networks. Our general idea is about merging concepts from quantum physics and complex network theory to provide a bidirectional bridge between both disciplines. Support is acknowledged from the Foundational Questions Institute (FQXi) and the Compagnia di San Paolo Foundation.

  8. Role of Edges in Complex Network Epidemiology

    NASA Astrophysics Data System (ADS)

    Zhang, Hao; Jiang, Zhi-Hong; Wang, Hui; Xie, Fei; Chen, Chao

    2012-09-01

    In complex network epidemiology, diseases spread along contacting edges between individuals and different edges may play different roles in epidemic outbreaks. Quantifying the efficiency of edges is an important step towards arresting epidemics. In this paper, we study the efficiency of edges in general susceptible-infected-recovered models, and introduce the transmission capability to measure the efficiency of edges. Results show that deleting edges with the highest transmission capability will greatly decrease epidemics on scale-free networks. Basing on the message passing approach, we get exact mathematical solution on configuration model networks with edge deletion in the large size limit.

  9. Multilevel Complex Networks and Systems

    NASA Astrophysics Data System (ADS)

    Caldarelli, Guido

    2014-03-01

    Network theory has been a powerful tool to model isolated complex systems. However, the classical approach does not take into account the interactions often present among different systems. Hence, the scientific community is nowadays concentrating the efforts on the foundations of new mathematical tools for understanding what happens when multiple networks interact. The case of economic and financial networks represents a paramount example of multilevel networks. In the case of trade, trade among countries the different levels can be described by the different granularity of the trading relations. Indeed, we have now data from the scale of consumers to that of the country level. In the case of financial institutions, we have a variety of levels at the same scale. For example one bank can appear in the interbank networks, ownership network and cds networks in which the same institution can take place. In both cases the systemically important vertices need to be determined by different procedures of centrality definition and community detection. In this talk I will present some specific cases of study related to these topics and present the regularities found. Acknowledged support from EU FET Project ``Multiplex'' 317532.

  10. Epidemic processes in complex networks

    NASA Astrophysics Data System (ADS)

    Pastor-Satorras, Romualdo; Castellano, Claudio; Van Mieghem, Piet; Vespignani, Alessandro

    2015-07-01

    In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.

  11. Competitive Dynamics on Complex Networks

    NASA Astrophysics Data System (ADS)

    Zhao, Jiuhua; Liu, Qipeng; Wang, Xiaofan

    2014-07-01

    We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition.

  12. Competitive Dynamics on Complex Networks

    PubMed Central

    Zhao, Jiuhua; Liu, Qipeng; Wang, Xiaofan

    2014-01-01

    We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition. PMID:25068622

  13. Statistical physics of complex networks

    NASA Astrophysics Data System (ADS)

    Xie, Huafeng

    We live in a connected world. It is of great practical importance and intellectual appeal to understand the networks surrounding us. In this work we study ranking of the nodes in complex networks. In large networks such as World Wide Web (WWW) and citation networks of scientific literature, searching by keywords is a common practice to retrieve useful information. On the WWW, apart from the contents of webpages, the topology of the network itself can be a rich source of information about their relative importance and relevancy to the search query. It is the effective utilization of this topological information [50] which advanced the Google search engine to its present position of the most popular tool on the WWW. The World-Wide Web (WWW) is characterized by a strong community structure in which communities of webpages are densely interconnected by hyperlinks. We study how such network architecture affects the average Google ranking of individual webpages in the community. Using a mean-field approximation, we quantify how the average Google rank of community's webpages depends on the degree to which it is isolated from the rest of the world in both incoming and outgoing directions, and alpha -- the only intrinsic parameter of Google's PageRank algorithm. We proceed with numerical study of simulated networks and empirical study of several internal web-communities within two US universities. The predictions of our mean-field treatment were qualitatively verified in those real-life networks. Furthermore, the value alpha = 0.15 used by Google seems to be optimized for the degree of isolation of communities as they exist in the actual WWW. We then extend Google's PageRank algorithm to citation networks of scientific literature. Unlike hyperlinks, citations cannot be updated after the point of publication. This results in strong aging characteristics of citation networks that affect the performance of the PageRank algorithm. To rectify this we modify the Page

  14. Congestion phenomena on complex networks.

    PubMed

    De Martino, Daniele; Dall'asta, Luca; Bianconi, Ginestra; Marsili, Matteo

    2009-01-01

    We define a minimal model of traffic flows in complex networks in order to study the trade-off between topological-based and traffic-based routing strategies. The resulting collective behavior is obtained analytically for an ensemble of uncorrelated networks and summarized in a rich phase diagram presenting second-order as well as first-order phase transitions between a free-flow phase and a congested phase. We find that traffic control improves global performance, enlarging the free-flow region in parameter space only in heterogeneous networks. Traffic control introduces nonlinear effects and, beyond a critical strength, may trigger the appearance of a congested phase in a discontinuous manner. The model also reproduces the crossover in the scaling of traffic fluctuations empirically observed on the Internet.

  15. Blockmodeling techniques for complex networks

    NASA Astrophysics Data System (ADS)

    Ball, Brian Joseph

    The class of network models known as stochastic blockmodels has recently been gaining popularity. In this dissertation, we present new work that uses blockmodels to answer questions about networks. We create a blockmodel based on the idea of link communities, which naturally gives rise to overlapping vertex communities. We derive a fast and accurate algorithm to fit the model to networks. This model can be related to another blockmodel, which allows the method to efficiently find nonoverlapping communities as well. We then create a heuristic based on the link community model whose use is to find the correct number of communities in a network. The heuristic is based on intuitive corrections to likelihood ratio tests. It does a good job finding the correct number of communities in both real networks and synthetic networks generated from the link communities model. Two commonly studied types of networks are citation networks, where research papers cite other papers, and coauthorship networks, where authors are connected if they've written a paper together. We study a multi-modal network from a large dataset of Physics publications that is the combination of the two, allowing for directed links between papers as citations, and an undirected edge between a scientist and a paper if they helped to write it. This allows for new insights on the relation between social interaction and scientific production. We also have the publication dates of papers, which lets us track our measures over time. Finally, we create a stochastic model for ranking vertices in a semi-directed network. The probability of connection between two vertices depends on the difference of their ranks. When this model is fit to high school friendship networks, the ranks appear to correspond with a measure of social status. Students have reciprocated and some unreciprocated edges with other students of closely similar rank that correspond to true friendship, and claim an aspirational friendship with a much

  16. Controlling centrality in complex networks

    PubMed Central

    Nicosia, V.; Criado, R.; Romance, M.; Russo, G.; Latora, V.

    2012-01-01

    Spectral centrality measures allow to identify influential individuals in social groups, to rank Web pages by popularity, and even to determine the impact of scientific researches. The centrality score of a node within a network crucially depends on the entire pattern of connections, so that the usual approach is to compute node centralities once the network structure is assigned. We face here with the inverse problem, that is, we study how to modify the centrality scores of the nodes by acting on the structure of a given network. We show that there exist particular subsets of nodes, called controlling sets, which can assign any prescribed set of centrality values to all the nodes of a graph, by cooperatively tuning the weights of their out-going links. We found that many large networks from the real world have surprisingly small controlling sets, containing even less than 5 – 10% of the nodes. PMID:22355732

  17. Network representations of immune system complexity

    PubMed Central

    Subramanian, Naeha; Torabi-Parizi, Parizad; Gottschalk, Rachel A.; Germain, Ronald N.; Dutta, Bhaskar

    2015-01-01

    The mammalian immune system is a dynamic multi-scale system composed of a hierarchically organized set of molecular, cellular and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein-protein interactions underlying intracellular signaling pathways and single cell responses to increasingly complex networks of in vivo cellular interaction, positioning and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather non-linear behaviors arising from dynamic, feedback-regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multi-scale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular and organism-level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. PMID:25625853

  18. Robustness surfaces of complex networks

    NASA Astrophysics Data System (ADS)

    Manzano, Marc; Sahneh, Faryad; Scoglio, Caterina; Calle, Eusebi; Marzo, Jose Luis

    2014-09-01

    Despite the robustness of complex networks has been extensively studied in the last decade, there still lacks a unifying framework able to embrace all the proposed metrics. In the literature there are two open issues related to this gap: (a) how to dimension several metrics to allow their summation and (b) how to weight each of the metrics. In this work we propose a solution for the two aforementioned problems by defining the R*-value and introducing the concept of robustness surface (Ω). The rationale of our proposal is to make use of Principal Component Analysis (PCA). We firstly adjust to 1 the initial robustness of a network. Secondly, we find the most informative robustness metric under a specific failure scenario. Then, we repeat the process for several percentage of failures and different realizations of the failure process. Lastly, we join these values to form the robustness surface, which allows the visual assessment of network robustness variability. Results show that a network presents different robustness surfaces (i.e., dissimilar shapes) depending on the failure scenario and the set of metrics. In addition, the robustness surface allows the robustness of different networks to be compared.

  19. Factors determining nestedness in complex networks.

    PubMed

    Jonhson, Samuel; Domínguez-García, Virginia; Muñoz, Miguel A

    2013-01-01

    Understanding the causes and effects of network structural features is a key task in deciphering complex systems. In this context, the property of network nestedness has aroused a fair amount of interest as regards ecological networks. Indeed, Bastolla et al. introduced a simple measure of network nestedness which opened the door to analytical understanding, allowing them to conclude that biodiversity is strongly enhanced in highly nested mutualistic networks. Here, we suggest a slightly refined version of such a measure of nestedness and study how it is influenced by the most basic structural properties of networks, such as degree distribution and degree-degree correlations (i.e. assortativity). We find that most of the empirically found nestedness stems from heterogeneity in the degree distribution. Once such an influence has been discounted - as a second factor - we find that nestedness is strongly correlated with disassortativity and hence - as random networks have been recently found to be naturally disassortative - they also tend to be naturally nested just as the result of chance.

  20. Factors Determining Nestedness in Complex Networks

    PubMed Central

    Jonhson, Samuel; Domínguez-García, Virginia; Muñoz, Miguel A.

    2013-01-01

    Understanding the causes and effects of network structural features is a key task in deciphering complex systems. In this context, the property of network nestedness has aroused a fair amount of interest as regards ecological networks. Indeed, Bastolla et al. introduced a simple measure of network nestedness which opened the door to analytical understanding, allowing them to conclude that biodiversity is strongly enhanced in highly nested mutualistic networks. Here, we suggest a slightly refined version of such a measure of nestedness and study how it is influenced by the most basic structural properties of networks, such as degree distribution and degree-degree correlations (i.e. assortativity). We find that most of the empirically found nestedness stems from heterogeneity in the degree distribution. Once such an influence has been discounted – as a second factor – we find that nestedness is strongly correlated with disassortativity and hence – as random networks have been recently found to be naturally disassortative – they also tend to be naturally nested just as the result of chance. PMID:24069264

  1. Characterizing the Community Structure of Complex Networks

    PubMed Central

    Lancichinetti, Andrea; Kivelä, Mikko; Saramäki, Jari; Fortunato, Santo

    2010-01-01

    Background Community structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of community detection, very little attention has been so far devoted to the investigation of communities in real networks. Methodology/Principal Findings We present a systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks. We find that the mesoscopic organization of networks of the same category is remarkably similar. This is reflected in several characteristics of community structure, which can be used as “fingerprints” of specific network categories. While community size distributions are always broad, certain categories of networks consist mainly of tree-like communities, while others have denser modules. Average path lengths within communities initially grow logarithmically with community size, but the growth saturates or slows down for communities larger than a characteristic size. This behaviour is related to the presence of hubs within communities, whose roles differ across categories. Also the community embeddedness of nodes, measured in terms of the fraction of links within their communities, has a characteristic distribution for each category. Conclusions/Significance Our findings, verified by the use of two fundamentally different community detection methods, allow for a classification of real networks and pave the way to a realistic modelling of networks' evolution. PMID:20711338

  2. Fuzzy Entropy Method for Quantifying Supply Chain Networks Complexity

    NASA Astrophysics Data System (ADS)

    Zhang, Jihui; Xu, Junqin

    Supply chain is a special kind of complex network. Its complexity and uncertainty makes it very difficult to control and manage. Supply chains are faced with a rising complexity of products, structures, and processes. Because of the strong link between a supply chain’s complexity and its efficiency the supply chain complexity management becomes a major challenge of today’s business management. The aim of this paper is to quantify the complexity and organization level of an industrial network working towards the development of a ‘Supply Chain Network Analysis’ (SCNA). By measuring flows of goods and interaction costs between different sectors of activity within the supply chain borders, a network of flows is built and successively investigated by network analysis. The result of this study shows that our approach can provide an interesting conceptual perspective in which the modern supply network can be framed, and that network analysis can handle these issues in practice.

  3. Anomalous Transport in Complex Networks

    NASA Astrophysics Data System (ADS)

    Lopez, Eduardo; Buldyrev, Sergey; Havlin, Shlomo; Stanley, H. Eugene

    2005-03-01

    To study transport properties of complex networks, we analyze the equivalent conductance G between two arbitrarily chosen nodes of random scale-free networks with degree distribution P(k)˜k^-λ in which each link has the same unit resistance. We predict a broad range of values of G, with a power-law tail distribution φSF(G)˜G^-gG, where gG=2λ-1, and confirm our predictions by simulations. The power-law tail in φSF(G) leads to large values of G, thereby significantly improving the transport in scale-free networks, compared to Erdos-R'enyi random graphs where the tail of the conductivity distribution decays exponentially. Based on a simple physical ``transport backbone'' picture we show that the conductances are well approximated by ckAkB/(kA+kB) for any pair of nodes A and B with degrees kA and kB. Thus, a single parameter c characterizes transport on scale-free networks.

  4. Kinetic analysis of complex metabolic networks

    SciTech Connect

    Stephanopoulos, G.

    1996-12-31

    A new methodology is presented for the analysis of complex metabolic networks with the goal of metabolite overproduction. The objective is to locate a small number of reaction steps in a network that have maximum impact on network flux amplification and whose rate can also be increased without functional network derangement. This method extends the concepts of Metabolic Control Analysis to groups of reactions and offers the means for calculating group control coefficients as measures of the control exercised by groups of reactions on the overall network fluxes and intracellular metabolite pools. It is further demonstrated that the optimal strategy for the effective increase of network fluxes, while maintaining an uninterrupted supply of intermediate metabolites, is through the coordinated amplification of multiple (as opposed to a single) reaction steps. Satisfying this requirement invokes the concept of the concentration control to coefficient, which emerges as a critical parameter in the identification of feasible enzymatic modifications with maximal impact on the network flux. A case study of aromatic aminoacid production is provided to illustrate these concepts.

  5. Advances in the Theory of Complex Networks

    NASA Astrophysics Data System (ADS)

    Peruani, Fernando

    An exhaustive and comprehensive review on the theory of complex networks would imply nowadays a titanic task, and it would result in a lengthy work containing plenty of technical details of arguable relevance. Instead, this chapter addresses very briefly the ABC of complex network theory, visiting only the hallmarks of the theoretical founding, to finally focus on two of the most interesting and promising current research problems: the study of dynamical processes on transportation networks and the identification of communities in complex networks.

  6. The Correlation Fractal Dimension of Complex Networks

    NASA Astrophysics Data System (ADS)

    Wang, Xingyuan; Liu, Zhenzhen; Wang, Mogei

    2013-05-01

    The fractality of complex networks is studied by estimating the correlation dimensions of the networks. Comparing with the previous algorithms of estimating the box dimension, our algorithm achieves a significant reduction in time complexity. For four benchmark cases tested, that is, the Escherichia coli (E. Coli) metabolic network, the Homo sapiens protein interaction network (H. Sapiens PIN), the Saccharomyces cerevisiae protein interaction network (S. Cerevisiae PIN) and the World Wide Web (WWW), experiments are provided to demonstrate the validity of our algorithm.

  7. Ranking important nodes in complex networks by simulated annealing

    NASA Astrophysics Data System (ADS)

    Sun, Yu; Yao, Pei-Yang; Wan, Lu-Jun; Shen, Jian; Zhong, Yun

    2017-02-01

    In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented. First, the concept of an importance sequence (IS) to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks. Project supported by the National Natural Science Foundation of China (Grant No. 61573017) and the Natural Science Foundation of Shaanxi Province, China (Grant No. 2016JQ6062).

  8. Robustness and structure of complex networks

    NASA Astrophysics Data System (ADS)

    Shao, Shuai

    This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks

  9. Autoscoring Essays Based on Complex Networks

    ERIC Educational Resources Information Center

    Ke, Xiaohua; Zeng, Yongqiang; Luo, Haijiao

    2016-01-01

    This article presents a novel method, the Complex Dynamics Essay Scorer (CDES), for automated essay scoring using complex network features. Texts produced by college students in China were represented as scale-free networks (e.g., a word adjacency model) from which typical network features, such as the in-/out-degrees, clustering coefficient (CC),…

  10. Structural measures for multiplex networks

    NASA Astrophysics Data System (ADS)

    Battiston, Federico; Nicosia, Vincenzo; Latora, Vito

    2014-03-01

    Many real-world complex systems consist of a set of elementary units connected by relationships of different kinds. All such systems are better described in terms of multiplex networks, where the links at each layer represent a different type of interaction between the same set of nodes rather than in terms of (single-layer) networks. In this paper we present a general framework to describe and study multiplex networks, whose links are either unweighted or weighted. In particular, we propose a series of measures to characterize the multiplexicity of the systems in terms of (i) basic node and link properties such as the node degree, and the edge overlap and reinforcement, (ii) local properties such as the clustering coefficient and the transitivity, and (iii) global properties related to the navigability of the multiplex across the different layers. The measures we introduce are validated on a genuinely multiplex data set of Indonesian terrorists, where information among 78 individuals are recorded with respect to mutual trust, common operations, exchanged communications, and business relationships.

  11. A Complexity Theory of Neural Networks

    DTIC Science & Technology

    1990-04-14

    Significant results have been obtained on the computation complexity of analog neural networks , and distribute voting. The computing power and...learning algorithms for limited precision analog neural networks have been investigated. Lower bounds for constant depth, polynomial size analog neural ... networks , and a limited version of discrete neural networks have been obtained. The work on distributed voting has important applications for distributed

  12. Benford’s Distribution in Complex Networks

    NASA Astrophysics Data System (ADS)

    Morzy, Mikołaj; Kajdanowicz, Tomasz; Szymański, Bolesław K.

    2016-10-01

    Many collections of numbers do not have a uniform distribution of the leading digit, but conform to a very particular pattern known as Benford’s distribution. This distribution has been found in numerous areas such as accounting data, voting registers, census data, and even in natural phenomena. Recently it has been reported that Benford’s law applies to online social networks. Here we introduce a set of rigorous tests for adherence to Benford’s law and apply it to verification of this claim, extending the scope of the experiment to various complex networks and to artificial networks created by several popular generative models. Our findings are that neither for real nor for artificial networks there is sufficient evidence for common conformity of network structural properties with Benford’s distribution. We find very weak evidence suggesting that three measures, degree centrality, betweenness centrality and local clustering coefficient, could adhere to Benford’s law for scalefree networks but only for very narrow range of their parameters.

  13. Benford’s Distribution in Complex Networks

    PubMed Central

    Morzy, Mikołaj; Kajdanowicz, Tomasz; Szymański, Bolesław K.

    2016-01-01

    Many collections of numbers do not have a uniform distribution of the leading digit, but conform to a very particular pattern known as Benford’s distribution. This distribution has been found in numerous areas such as accounting data, voting registers, census data, and even in natural phenomena. Recently it has been reported that Benford’s law applies to online social networks. Here we introduce a set of rigorous tests for adherence to Benford’s law and apply it to verification of this claim, extending the scope of the experiment to various complex networks and to artificial networks created by several popular generative models. Our findings are that neither for real nor for artificial networks there is sufficient evidence for common conformity of network structural properties with Benford’s distribution. We find very weak evidence suggesting that three measures, degree centrality, betweenness centrality and local clustering coefficient, could adhere to Benford’s law for scalefree networks but only for very narrow range of their parameters. PMID:27748398

  14. Complexity Characteristics of Currency Networks

    NASA Astrophysics Data System (ADS)

    Gorski, A. Z.; Drozdz, S.; Kwapien, J.; Oswiecimka, P.

    2006-11-01

    A large set of daily FOREX time series is analyzed. The corresponding correlation matrices (CM) are constructed for USD, EUR and PLN used as the base currencies. The triangle rule is interpreted as constraints reducing the number of independent returns. The CM spectrum is computed and compared with the cases of shuffled currencies and a fictitious random currency taken as a base currency. The Minimal Spanning Tree (MST) graphs are calculated and the clustering effects for strong currencies are found. It is shown that for MSTs the node rank has power like, scale free behavior. Finally, the scaling exponents are evaluated and found in the range analogous to those identified recently for various complex networks.

  15. Spatially Distributed Social Complex Networks

    NASA Astrophysics Data System (ADS)

    Frasco, Gerald F.; Sun, Jie; Rozenfeld, Hernán D.; ben-Avraham, Daniel

    2014-01-01

    We propose a bare-bones stochastic model that takes into account both the geographical distribution of people within a country and their complex network of connections. The model, which is designed to give rise to a scale-free network of social connections and to visually resemble the geographical spread seen in satellite pictures of the Earth at night, gives rise to a power-law distribution for the ranking of cities by population size (but for the largest cities) and reflects the notion that highly connected individuals tend to live in highly populated areas. It also yields some interesting insights regarding Gibrat's law for the rates of city growth (by population size), in partial support of the findings in a recent analysis of real data [Rozenfeld et al., Proc. Natl. Acad. Sci. U.S.A. 105, 18702 (2008).]. The model produces a nontrivial relation between city population and city population density and a superlinear relationship between social connectivity and city population, both of which seem quite in line with real data.

  16. Higher-order organization of complex networks

    PubMed Central

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

    2016-01-01

    Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks—at the level of small network subgraphs—remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns. PMID:27387949

  17. Network Motifs: Simple Building Blocks of Complex Networks

    NASA Astrophysics Data System (ADS)

    Milo, R.; Shen-Orr, S.; Itzkovitz, S.; Kashtan, N.; Chklovskii, D.; Alon, U.

    2002-10-01

    Complex networks are studied across many fields of science. To uncover their structural design principles, we defined ``network motifs,'' patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This approach may uncover the basic building blocks of most networks.

  18. Securing Information with Complex Optical Encryption Networks

    DTIC Science & Technology

    2015-08-11

    encryption networks, and to provide effective and reliable solutions for information security. 15. SUBJECT TERMS Optical Encryption...popularization of networking and internet , much research effort is made in the field of information security. Military communication system makes an...objective is to propose the architectures for a number of complex optical encryption networks so as to provide effective and reliable solutions for

  19. Realizing actual feedback control of complex network

    NASA Astrophysics Data System (ADS)

    Tu, Chengyi; Cheng, Yuhua

    2014-06-01

    In this paper, we present the concept of feedbackability and how to identify the Minimum Feedbackability Set of an arbitrary complex directed network. Furthermore, we design an estimator and a feedback controller accessing one MFS to realize actual feedback control, i.e. control the system to our desired state according to the estimated system internal state from the output of estimator. Last but not least, we perform numerical simulations of a small linear time-invariant dynamics network and a real simple food network to verify the theoretical results. The framework presented here could make an arbitrary complex directed network realize actual feedback control and deepen our understanding of complex systems.

  20. Chaos synchronization of general complex dynamical networks

    NASA Astrophysics Data System (ADS)

    Lü, Jinhu; Yu, Xinghuo; Chen, Guanrong

    2004-03-01

    Recently, it has been demonstrated that many large-scale complex dynamical networks display a collective synchronization motion. Here, we introduce a time-varying complex dynamical network model and further investigate its synchronization phenomenon. Based on this new complex network model, two network chaos synchronization theorems are proved. We show that the chaos synchronization of a time-varying complex network is determined by means of the inner coupled link matrix, the eigenvalues and the corresponding eigenvectors of the coupled configuration matrix, rather than the conventional eigenvalues of the coupled configuration matrix for a uniform network. Especially, we do not assume that the coupled configuration matrix is symmetric and its off-diagonal elements are nonnegative, which in a way generalizes the related results existing in the literature.

  1. Pinning impulsive control algorithms for complex network

    SciTech Connect

    Sun, Wen; Lü, Jinhu; Chen, Shihua; Yu, Xinghuo

    2014-03-15

    In this paper, we further investigate the synchronization of complex dynamical network via pinning control in which a selection of nodes are controlled at discrete times. Different from most existing work, the pinning control algorithms utilize only the impulsive signals at discrete time instants, which may greatly improve the communication channel efficiency and reduce control cost. Two classes of algorithms are designed, one for strongly connected complex network and another for non-strongly connected complex network. It is suggested that in the strongly connected network with suitable coupling strength, a single controller at any one of the network's nodes can always pin the network to its homogeneous solution. In the non-strongly connected case, the location and minimum number of nodes needed to pin the network are determined by the Frobenius normal form of the coupling matrix. In addition, the coupling matrix is not necessarily symmetric or irreducible. Illustrative examples are then given to validate the proposed pinning impulsive control algorithms.

  2. Contagion on complex networks with persuasion.

    PubMed

    Huang, Wei-Min; Zhang, Li-Jie; Xu, Xin-Jian; Fu, Xinchu

    2016-03-31

    The threshold model has been widely adopted as a classic model for studying contagion processes on social networks. We consider asymmetric individual interactions in social networks and introduce a persuasion mechanism into the threshold model. Specifically, we study a combination of adoption and persuasion in cascading processes on complex networks. It is found that with the introduction of the persuasion mechanism, the system may become more vulnerable to global cascades, and the effects of persuasion tend to be more significant in heterogeneous networks than those in homogeneous networks: a comparison between heterogeneous and homogeneous networks shows that under weak persuasion, heterogeneous networks tend to be more robust against random shocks than homogeneous networks; whereas under strong persuasion, homogeneous networks are more stable. Finally, we study the effects of adoption and persuasion threshold heterogeneity on systemic stability. Though both heterogeneities give rise to global cascades, the adoption heterogeneity has an overwhelmingly stronger impact than the persuasion heterogeneity when the network connectivity is sufficiently dense.

  3. Contagion on complex networks with persuasion

    NASA Astrophysics Data System (ADS)

    Huang, Wei-Min; Zhang, Li-Jie; Xu, Xin-Jian; Fu, Xinchu

    2016-03-01

    The threshold model has been widely adopted as a classic model for studying contagion processes on social networks. We consider asymmetric individual interactions in social networks and introduce a persuasion mechanism into the threshold model. Specifically, we study a combination of adoption and persuasion in cascading processes on complex networks. It is found that with the introduction of the persuasion mechanism, the system may become more vulnerable to global cascades, and the effects of persuasion tend to be more significant in heterogeneous networks than those in homogeneous networks: a comparison between heterogeneous and homogeneous networks shows that under weak persuasion, heterogeneous networks tend to be more robust against random shocks than homogeneous networks; whereas under strong persuasion, homogeneous networks are more stable. Finally, we study the effects of adoption and persuasion threshold heterogeneity on systemic stability. Though both heterogeneities give rise to global cascades, the adoption heterogeneity has an overwhelmingly stronger impact than the persuasion heterogeneity when the network connectivity is sufficiently dense.

  4. A Complexity Theory of Neural Networks

    DTIC Science & Technology

    1991-08-09

    Significant progress has been made in laying the foundations of a complexity theory of neural networks . The fundamental complexity classes have been...identified and studied. The class of problems solvable by small, shallow neural networks has been found to be the same class even if (1) probabilistic...behaviour (2)Multi-valued logic, and (3)analog behaviour, are allowed (subject to certain resonable technical assumptions). Neural networks can be

  5. Effective Augmentation of Complex Networks

    PubMed Central

    Wang, Jinjian; Yu, Xinghuo; Stone, Lewi

    2016-01-01

    Networks science plays an enormous role in many aspects of modern society from distributing electrical power across nations to spreading information and social networking amongst global populations. While modern networks constantly change in size, few studies have sought methods for the difficult task of optimising this growth. Here we study theoretical requirements for augmenting networks by adding source or sink nodes, without requiring additional driver-nodes to accommodate the change i.e., conserving structural controllability. Our “effective augmentation” algorithm takes advantage of clusters intrinsic to the network topology, and permits rapidly and efficient augmentation of a large number of nodes in one time-step. “Effective augmentation” is shown to work successfully on a wide range of model and real networks. The method has numerous applications (e.g. study of biological, social, power and technological networks) and potentially of significant practical and economic value. PMID:27165120

  6. Effective Augmentation of Complex Networks

    NASA Astrophysics Data System (ADS)

    Wang, Jinjian; Yu, Xinghuo; Stone, Lewi

    2016-05-01

    Networks science plays an enormous role in many aspects of modern society from distributing electrical power across nations to spreading information and social networking amongst global populations. While modern networks constantly change in size, few studies have sought methods for the difficult task of optimising this growth. Here we study theoretical requirements for augmenting networks by adding source or sink nodes, without requiring additional driver-nodes to accommodate the change i.e., conserving structural controllability. Our “effective augmentation” algorithm takes advantage of clusters intrinsic to the network topology, and permits rapidly and efficient augmentation of a large number of nodes in one time-step. “Effective augmentation” is shown to work successfully on a wide range of model and real networks. The method has numerous applications (e.g. study of biological, social, power and technological networks) and potentially of significant practical and economic value.

  7. CORRELATION PROFILES AND MOTIFS IN COMPLEX NETWORKS.

    SciTech Connect

    MASLOV,S.SNEPPEN,K.ALON,U.

    2004-01-16

    Networks have recently emerged as a unifying theme in complex systems research [1]. It is in fact no coincidence that networks and complexity are so heavily intertwined. Any future definition of a complex system should reflect the fact that such systems consist of many mutually interacting components. These components are far from being identical as say electrons in systems studied by condensed matter physics. In a truly complex system each of them has a unique identity allowing one to separate it from the others. The very first question one may ask about such a system is which other components a given component interacts with? This information system wide can be visualized as a graph, whose nodes correspond to individual components of the complex system in question and edges to their mutual interactions. Such a network can be thought of as a backbone of the complex system. Of course, system's dynamics depends not only on the topology of an underlying network but also on the exact form of interaction of components with each other, which can be very different in various complex systems. However, the underlying network may contain clues about the basic design principles and/or evolutionary history of the complex system in question. The goal of this article is to provide readers with a set of useful tools that would help to decide which features of a complex network are there by pure chance alone, and which of them were possibly designed or evolved to their present state.

  8. Jamming in complex networks with degree correlation

    NASA Astrophysics Data System (ADS)

    Pastore Y Piontti, Ana; Braunstein, Lidia; Macri, Pablo

    2012-02-01

    We study the effects of the degree-degree correlations on the pressure congestion J for a diffusive transport process on scale free complex networks. Using the gradient network approach we find that the pressure congestion for disassortative (assortative) networks is lower (bigger) than the one for uncorrelated networks which allow us to affirm that disassortative networks enhance transport through them. This result agree with the fact that many real world transportation networks naturally evolve to this kind of correlation. We explain our results showing that for the disassortative case the clusters in the gradient network turn out to be as much elongated as possible, reducing the pressure congestion J and observing the opposite behavior for the assortative case. Finally, we apply our transportation process to real world networks, and the results agree with our findings for model networks.

  9. Revealing the Hidden Language of Complex Networks

    NASA Astrophysics Data System (ADS)

    Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Davis, Darren; Levnajic, Zoran; Janjic, Vuk; Karapandza, Rasa; Stojmirovic, Aleksandar; Pržulj, Nataša

    2014-04-01

    Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists.

  10. Center of mass in complex networks

    NASA Astrophysics Data System (ADS)

    Fu, Chuanji; Gao, Yachun; Cai, Shimin; Yang, Hongchun; Yang, Chun

    2017-01-01

    Network dynamics is always a big challenge in nonlinear dynamics. Although great advancements have been made in various types of complex systems, an universal theoretical framework is required. In this paper, we introduce the concept of center of ‘mass’ of complex networks, where ‘mass’ stands for node importance or centrality in contrast to that of particle systems, and further prove that the phase transition and evolutionary state of the system can be characterized by the activity of center of ‘mass’. The steady states of several complex networks (gene regulatory networks and epidemic spreading systems) are then studied by analytically calculating the decoupled equation of the dynamic activity of center of ‘mass’, which is derived from the dynamic equation of the complex networks. The limitations of this method are also pointed out, such as the dynamical problems that related with the relative activities among components, and those systems that consist of oscillatory or chaotic motions.

  11. Detecting link failures in complex network processes using remote monitoring

    NASA Astrophysics Data System (ADS)

    Dhal, R.; Abad Torres, J.; Roy, S.

    2015-11-01

    We study whether local structural changes in a complex network can be distinguished from passive remote time-course measurements of the network's dynamics. Specifically the detection of link failures in a network synchronization process from noisy measurements at a single network component is considered. By phrasing the detection task as a Maximum A Posteriori Probability hypothesis testing problem, we are able to obtain conditions under which the detection is (1) improved over the a priori and (2) asymptotically perfect, in terms of the network spectrum and graph. We find that, in the case where the detector has knowledge of the network's state, perfect detection is possible under general connectivity conditions regardless of the measurement location. When the detector does not have state knowledge, a remote signature permits improved but not perfect detection, under the same connectivity conditions. At its essence, detectability is achieved because of the close connection between a network's topology, its eigenvalues and local response characteristics.

  12. Percolation of localized attack on complex networks

    NASA Astrophysics Data System (ADS)

    Shao, Shuai; Huang, Xuqing; Stanley, H. Eugene; Havlin, Shlomo

    2015-02-01

    The robustness of complex networks against node failure and malicious attack has been of interest for decades, while most of the research has focused on random attack or hub-targeted attack. In many real-world scenarios, however, attacks are neither random nor hub-targeted, but localized, where a group of neighboring nodes in a network are attacked and fail. In this paper we develop a percolation framework to analytically and numerically study the robustness of complex networks against such localized attack. In particular, we investigate this robustness in Erdős-Rényi networks, random-regular networks, and scale-free networks. Our results provide insight into how to better protect networks, enhance cybersecurity, and facilitate the design of more robust infrastructures.

  13. Controlling complex networks with conformity behavior

    NASA Astrophysics Data System (ADS)

    Wang, Xu-Wen; Nie, Sen; Wang, Wen-Xu; Wang, Bing-Hong

    2015-09-01

    Controlling complex networks accompanied by common conformity behavior is a fundamental problem in social and physical science. Conformity behavior that individuals tend to follow the majority in their neighborhood is common in human society and animal communities. Despite recent progress in understanding controllability of complex networks, the existent controllability theories cannot be directly applied to networks associated with conformity. Here we propose a simple model to incorporate conformity-based decision making into the evolution of a network system, which allows us to employ the exact controllability theory to explore the controllability of such systems. We offer rigorous theoretical results of controllability for representative regular networks. We also explore real networks in different fields and some typical model networks, finding some interesting results that are different from the predictions of structural and exact controllability theory in the absence of conformity. We finally present an example of steering a real social network to some target states to further validate our controllability theory and tools. Our work offers a more realistic understanding of network controllability with conformity behavior and can have potential applications in networked evolutionary games, opinion dynamics and many other complex networked systems.

  14. A random interacting network model for complex networks

    PubMed Central

    Goswami, Bedartha; Shekatkar, Snehal M.; Rheinwalt, Aljoscha; Ambika, G.; Kurths, Jürgen

    2015-01-01

    We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems. PMID:26657032

  15. Managing Complex Network Operation with Predictive Analytics

    SciTech Connect

    Huang, Zhenyu; Wong, Pak C.; Mackey, Patrick S.; Chen, Yousu; Ma, Jian; Schneider, Kevin P.; Greitzer, Frank L.

    2008-03-26

    Complex networks play an important role in modern societies. Their failures, such as power grid blackouts, would lead to significant disruption of people’s life, industry and commercial activities, and result in massive economic losses. Operation of these complex networks is an extremely challenging task due to their complex structures, wide geographical coverage, complex data/information technology systems, and highly dynamic and nonlinear behaviors. None of the complex network operation is fully automated; human-in-the-loop operation is critical. Given the complexity involved, there may be thousands of possible topological configurations at any given time. During an emergency, it is not uncommon for human operators to examine thousands of possible configurations in near real-time to choose the best option and operate the network effectively. In today’s practice, network operation is largely based on experience with very limited real-time decision support, resulting in inadequate management of complex predictions and inability to anticipate, recognize, and respond to situations caused by human errors, natural disasters, and cyber attacks. A systematic approach is needed to manage the complex operation paradigms and choose the best option in a near-real-time manner. This paper applies predictive analytics techniques to establish a decision support system for complex network operation management and help operators to predict potential network failures and adapt the network to adverse situations. The resultant decision support system enables continuous monitoring of network performance and turns large amounts of data into actionable information. Examples with actual power grid data are presented to demonstrate the capability of this proposed decision support system.

  16. Benchmarking Measures of Network Influence

    NASA Astrophysics Data System (ADS)

    Bramson, Aaron; Vandermarliere, Benjamin

    2016-09-01

    Identifying key agents for the transmission of diseases (ideas, technology, etc.) across social networks has predominantly relied on measures of centrality on a static base network or a temporally flattened graph of agent interactions. Various measures have been proposed as the best trackers of influence, such as degree centrality, betweenness, and k-shell, depending on the structure of the connectivity. We consider SIR and SIS propagation dynamics on a temporally-extruded network of observed interactions and measure the conditional marginal spread as the change in the magnitude of the infection given the removal of each agent at each time: its temporal knockout (TKO) score. We argue that this TKO score is an effective benchmark measure for evaluating the accuracy of other, often more practical, measures of influence. We find that none of the network measures applied to the induced flat graphs are accurate predictors of network propagation influence on the systems studied; however, temporal networks and the TKO measure provide the requisite targets for the search for effective predictive measures.

  17. Benchmarking Measures of Network Influence

    PubMed Central

    Bramson, Aaron; Vandermarliere, Benjamin

    2016-01-01

    Identifying key agents for the transmission of diseases (ideas, technology, etc.) across social networks has predominantly relied on measures of centrality on a static base network or a temporally flattened graph of agent interactions. Various measures have been proposed as the best trackers of influence, such as degree centrality, betweenness, and k-shell, depending on the structure of the connectivity. We consider SIR and SIS propagation dynamics on a temporally-extruded network of observed interactions and measure the conditional marginal spread as the change in the magnitude of the infection given the removal of each agent at each time: its temporal knockout (TKO) score. We argue that this TKO score is an effective benchmark measure for evaluating the accuracy of other, often more practical, measures of influence. We find that none of the network measures applied to the induced flat graphs are accurate predictors of network propagation influence on the systems studied; however, temporal networks and the TKO measure provide the requisite targets for the search for effective predictive measures. PMID:27670635

  18. Measurement of Online Social Networks

    ERIC Educational Resources Information Center

    Gjoka, Mina

    2010-01-01

    In recent years, the popularity of online social networks (OSN) has risen to unprecedented levels, with the most popular ones having hundreds of millions of users. This success has generated interest within the networking community and has given rise to a number of measurement and characterization studies, which provide a first step towards their…

  19. Power grid vulnerability: a complex network approach.

    PubMed

    Arianos, S; Bompard, E; Carbone, A; Xue, F

    2009-03-01

    Power grids exhibit patterns of reaction to outages similar to complex networks. Blackout sequences follow power laws, as complex systems operating near a critical point. Here, the tolerance of electric power grids to both accidental and malicious outages is analyzed in the framework of complex network theory. In particular, the quantity known as efficiency is modified by introducing a new concept of distance between nodes. As a result, a new parameter called net-ability is proposed to evaluate the performance of power grids. A comparison between efficiency and net-ability is provided by estimating the vulnerability of sample networks, in terms of both the metrics.

  20. Power grid vulnerability: A complex network approach

    NASA Astrophysics Data System (ADS)

    Arianos, S.; Bompard, E.; Carbone, A.; Xue, F.

    2009-03-01

    Power grids exhibit patterns of reaction to outages similar to complex networks. Blackout sequences follow power laws, as complex systems operating near a critical point. Here, the tolerance of electric power grids to both accidental and malicious outages is analyzed in the framework of complex network theory. In particular, the quantity known as efficiency is modified by introducing a new concept of distance between nodes. As a result, a new parameter called net-ability is proposed to evaluate the performance of power grids. A comparison between efficiency and net-ability is provided by estimating the vulnerability of sample networks, in terms of both the metrics.

  1. Optimization of spatial complex networks

    NASA Astrophysics Data System (ADS)

    Guillier, S.; Muñoz, V.; Rogan, J.; Zarama, R.; Valdivia, J. A.

    2017-02-01

    First, we estimate the connectivity properties of a predefined (fixed node locations) spatial network which optimizes a connectivity functional that balances construction and transportation costs. In this case we obtain a Gaussian distribution for the connectivity. However, when we consider these spatial networks in a growing process, we obtain a power law distribution for the connectivity. If the transportation costs in the functional involve the shortest geometrical path, we obtain a scaling exponent γ = 2.5. However, if the transportation costs in the functional involve just the shortest path, we obtain γ = 2.2. Both cases may be useful to analyze in some real networks.

  2. Statistically Validated Networks in Bipartite Complex Systems

    PubMed Central

    Tumminello, Michele; Miccichè, Salvatore; Lillo, Fabrizio; Piilo, Jyrki; Mantegna, Rosario N.

    2011-01-01

    Many complex systems present an intrinsic bipartite structure where elements of one set link to elements of the second set. In these complex systems, such as the system of actors and movies, elements of one set are qualitatively different than elements of the other set. The properties of these complex systems are typically investigated by constructing and analyzing a projected network on one of the two sets (for example the actor network or the movie network). Complex systems are often very heterogeneous in the number of relationships that the elements of one set establish with the elements of the other set, and this heterogeneity makes it very difficult to discriminate links of the projected network that are just reflecting system's heterogeneity from links relevant to unveil the properties of the system. Here we introduce an unsupervised method to statistically validate each link of a projected network against a null hypothesis that takes into account system heterogeneity. We apply the method to a biological, an economic and a social complex system. The method we propose is able to detect network structures which are very informative about the organization and specialization of the investigated systems, and identifies those relationships between elements of the projected network that cannot be explained simply by system heterogeneity. We also show that our method applies to bipartite systems in which different relationships might have different qualitative nature, generating statistically validated networks in which such difference is preserved. PMID:21483858

  3. Universality in complex networks: random matrix analysis.

    PubMed

    Bandyopadhyay, Jayendra N; Jalan, Sarika

    2007-08-01

    We apply random matrix theory to complex networks. We show that nearest neighbor spacing distribution of the eigenvalues of the adjacency matrices of various model networks, namely scale-free, small-world, and random networks follow universal Gaussian orthogonal ensemble statistics of random matrix theory. Second, we show an analogy between the onset of small-world behavior, quantified by the structural properties of networks, and the transition from Poisson to Gaussian orthogonal ensemble statistics, quantified by Brody parameter characterizing a spectral property. We also present our analysis for a protein-protein interaction network in budding yeast.

  4. Measuring and modeling correlations in multiplex networks

    NASA Astrophysics Data System (ADS)

    Nicosia, Vincenzo; Latora, Vito

    2015-09-01

    The interactions among the elementary components of many complex systems can be qualitatively different. Such systems are therefore naturally described in terms of multiplex or multilayer networks, i.e., networks where each layer stands for a different type of interaction between the same set of nodes. There is today a growing interest in understanding when and why a description in terms of a multiplex network is necessary and more informative than a single-layer projection. Here we contribute to this debate by presenting a comprehensive study of correlations in multiplex networks. Correlations in node properties, especially degree-degree correlations, have been thoroughly studied in single-layer networks. Here we extend this idea to investigate and characterize correlations between the different layers of a multiplex network. Such correlations are intrinsically multiplex, and we first study them empirically by constructing and analyzing several multiplex networks from the real world. In particular, we introduce various measures to characterize correlations in the activity of the nodes and in their degree at the different layers and between activities and degrees. We show that real-world networks exhibit indeed nontrivial multiplex correlations. For instance, we find cases where two layers of the same multiplex network are positively correlated in terms of node degrees, while other two layers are negatively correlated. We then focus on constructing synthetic multiplex networks, proposing a series of models to reproduce the correlations observed empirically and/or to assess their relevance.

  5. Jamming in complex networks with degree correlation

    NASA Astrophysics Data System (ADS)

    Pastore y Piontti, Ana L.; Braunstein, Lidia A.; Macri, Pablo A.

    2010-10-01

    We study the effects of the degree-degree correlations on the pressure congestion J when we apply a dynamical process on scale free complex networks using the gradient network approach. We find that the pressure congestion for disassortative (assortative) networks is lower (bigger) than the one for uncorrelated networks which allow us to affirm that disassortative networks enhance transport through them. This result agree with the fact that many real world transportation networks naturally evolve to this kind of correlation. We explain our results showing that for the disassortative case the clusters in the gradient network turn out to be as much elongated as possible, reducing the pressure congestion J and observing the opposite behavior for the assortative case. Finally we apply our model to real world networks, and the results agree with our theoretical model.

  6. Optimal dynamic bandwidth allocation for complex networks

    NASA Astrophysics Data System (ADS)

    Jiang, Zhong-Yuan; Liang, Man-Gui; Li, Qian; Guo, Dong-Chao

    2013-03-01

    Traffic capacity of one network strongly depends on the link’s bandwidth allocation strategy. In previous bandwidth allocation mechanisms, once one link’s bandwidth is allocated, it will be fixed throughout the overall traffic transmission process. However, the traffic load of every link changes from time to time. In this paper, with finite total bandwidth resource of the network, we propose to dynamically allocate the total bandwidth resource in which each link’s bandwidth is proportional to the queue length of the output buffer of the link per time step. With plenty of data packets in the network, the traffic handling ability of all links of the network achieves full utilization. The theoretical analysis and the extensive simulation results on complex networks are consistent. This work is valuable for network service providers to improve network performance or to do reasonable network design efficiently.

  7. Experimental flux measurements on a network scale

    SciTech Connect

    Schwender, J.

    2011-10-11

    Metabolic flux is a fundamental property of living organisms. In recent years, methods for measuring metabolic flux in plants on a network scale have evolved further. One major challenge in studying flux in plants is the complexity of the plant's metabolism. In particular, in the presence of parallel pathways in multiple cellular compartments, the core of plant central metabolism constitutes a complex network. Hence, a common problem with the reliability of the contemporary results of {sup 13}C-Metabolic Flux Analysis in plants is the substantial reduction in complexity that must be included in the simulated networks; this omission partly is due to limitations in computational simulations. Here, I discuss recent emerging strategies that will better address these shortcomings.

  8. Error and attack tolerance of complex networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka; Jeong, Hawoong; Barabási, Albert-László

    2000-07-01

    Many complex systems display a surprising degree of tolerance against errors. For example, relatively simple organisms grow, persist and reproduce despite drastic pharmaceutical or environmental interventions, an error tolerance attributed to the robustness of the underlying metabolic network. Complex communication networks display a surprising degree of robustness: although key components regularly malfunction, local failures rarely lead to the loss of the global information-carrying ability of the network. The stability of these and other complex systems is often attributed to the redundant wiring of the functional web defined by the systems' components. Here we demonstrate that error tolerance is not shared by all redundant systems: it is displayed only by a class of inhomogeneously wired networks, called scale-free networks, which include the World-Wide Web, the Internet, social networks and cells. We find that such networks display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected even by unrealistically high failure rates. However, error tolerance comes at a high price in that these networks are extremely vulnerable to attacks (that is, to the selection and removal of a few nodes that play a vital role in maintaining the network's connectivity). Such error tolerance and attack vulnerability are generic properties of communication networks.

  9. Distinguishing fiction from non-fiction with complex networks

    NASA Astrophysics Data System (ADS)

    Larue, David M.; Carr, Lincoln D.; Jones, Linnea K.; Stevanak, Joe T.

    2014-03-01

    Complex Network Measures are applied to networks constructed from texts in English to demonstrate an initial viability in textual analysis. Texts from novels and short stories obtained from Project Gutenberg and news stories obtained from NPR are selected. Unique word stems in a text are used as nodes in an associated unweighted undirected network, with edges connecting words occurring within a certain number of words somewhere in the text. Various combinations of complex network measures are computed for each text's network. Fisher's Linear Discriminant analysis is used to build a parameter optimizing the ability to separate the texts according to their genre. Success rates in the 70% range for correctly distinguishing fiction from non-fiction were obtained using edges defined as within four words, using 400 word samples from 400 texts from each of the two genres with some combinations of measures such as the power-law exponents of degree distributions and clustering coefficients.

  10. Traffic congestion in interconnected complex networks

    NASA Astrophysics Data System (ADS)

    Tan, Fei; Wu, Jiajing; Xia, Yongxiang; Tse, Chi K.

    2014-06-01

    Traffic congestion in isolated complex networks has been investigated extensively over the last decade. Coupled network models have recently been developed to facilitate further understanding of real complex systems. Analysis of traffic congestion in coupled complex networks, however, is still relatively unexplored. In this paper, we try to explore the effect of interconnections on traffic congestion in interconnected Barabási-Albert scale-free networks. We find that assortative coupling can alleviate traffic congestion more readily than disassortative and random coupling when the node processing capacity is allocated based on node usage probability. Furthermore, the optimal coupling probability can be found for assortative coupling. However, three types of coupling preferences achieve similar traffic performance if all nodes share the same processing capacity. We analyze interconnected Internet autonomous-system-level graphs of South Korea and Japan and obtain similar results. Some practical suggestions are presented to optimize such real-world interconnected networks accordingly.

  11. Traffic congestion in interconnected complex networks.

    PubMed

    Tan, Fei; Wu, Jiajing; Xia, Yongxiang; Tse, Chi K

    2014-06-01

    Traffic congestion in isolated complex networks has been investigated extensively over the last decade. Coupled network models have recently been developed to facilitate further understanding of real complex systems. Analysis of traffic congestion in coupled complex networks, however, is still relatively unexplored. In this paper, we try to explore the effect of interconnections on traffic congestion in interconnected Barabási-Albert scale-free networks. We find that assortative coupling can alleviate traffic congestion more readily than disassortative and random coupling when the node processing capacity is allocated based on node usage probability. Furthermore, the optimal coupling probability can be found for assortative coupling. However, three types of coupling preferences achieve similar traffic performance if all nodes share the same processing capacity. We analyze interconnected Internet autonomous-system-level graphs of South Korea and Japan and obtain similar results. Some practical suggestions are presented to optimize such real-world interconnected networks accordingly.

  12. Synchronization reveals topological scales in complex networks.

    PubMed

    Arenas, Alex; Díaz-Guilera, Albert; Pérez-Vicente, Conrad J

    2006-03-24

    We study the relationship between topological scales and dynamic time scales in complex networks. The analysis is based on the full dynamics towards synchronization of a system of coupled oscillators. In the synchronization process, modular structures corresponding to well-defined communities of nodes emerge in different time scales, ordered in a hierarchical way. The analysis also provides a useful connection between synchronization dynamics, complex networks topology, and spectral graph analysis.

  13. Localized recovery of complex networks against failure

    NASA Astrophysics Data System (ADS)

    Shang, Yilun

    2016-07-01

    Resilience of complex networks to failure has been an important issue in network research for decades, and recent studies have begun to focus on the inverse recovery of network functionality through strategically healing missing nodes or edges. However, the effect of network recovery is far from fully understood, and a general theory is still missing. Here we propose and study a general model of localized recovery, where a group of neighboring nodes are restored in an invasive way from a seed node. We develop a theoretical framework to compare the effect of random recovery (RR) and localized recovery (LR) in complex networks including Erdős-Rényi networks, random regular networks, and scale-free networks. We find detailed phase diagrams for the subnetwork of occupied nodes and the “complement network” of failed nodes under RR and LR. By identifying the two competitive forces behind LR, we present an analytical and numerical approach to guide us in choosing the appropriate recovery strategy and provide estimation on its effect by using the degree distribution of the original network as the only input. Our work therefore provides insight for quantitatively understanding recovery process and its implications in infrastructure protection in various complex systems.

  14. Localized recovery of complex networks against failure

    PubMed Central

    Shang, Yilun

    2016-01-01

    Resilience of complex networks to failure has been an important issue in network research for decades, and recent studies have begun to focus on the inverse recovery of network functionality through strategically healing missing nodes or edges. However, the effect of network recovery is far from fully understood, and a general theory is still missing. Here we propose and study a general model of localized recovery, where a group of neighboring nodes are restored in an invasive way from a seed node. We develop a theoretical framework to compare the effect of random recovery (RR) and localized recovery (LR) in complex networks including Erdős-Rényi networks, random regular networks, and scale-free networks. We find detailed phase diagrams for the subnetwork of occupied nodes and the “complement network” of failed nodes under RR and LR. By identifying the two competitive forces behind LR, we present an analytical and numerical approach to guide us in choosing the appropriate recovery strategy and provide estimation on its effect by using the degree distribution of the original network as the only input. Our work therefore provides insight for quantitatively understanding recovery process and its implications in infrastructure protection in various complex systems. PMID:27456202

  15. 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

  16. Maximizing information exchange between complex networks

    NASA Astrophysics Data System (ADS)

    West, Bruce J.; Geneston, Elvis L.; Grigolini, Paolo

    2008-10-01

    Science is not merely the smooth progressive interaction of hypothesis, experiment and theory, although it sometimes has that form. More realistically the scientific study of any given complex phenomenon generates a number of explanations, from a variety of perspectives, that eventually requires synthesis to achieve a deep level of insight and understanding. One such synthesis has created the field of out-of-equilibrium statistical physics as applied to the understanding of complex dynamic networks. Over the past forty years the concept of complexity has undergone a metamorphosis. Complexity was originally seen as a consequence of memory in individual particle trajectories, in full agreement with a Hamiltonian picture of microscopic dynamics and, in principle, macroscopic dynamics could be derived from the microscopic Hamiltonian picture. The main difficulty in deriving macroscopic dynamics from microscopic dynamics is the need to take into account the actions of a very large number of components. The existence of events such as abrupt jumps, considered by the conventional continuous time random walk approach to describing complexity was never perceived as conflicting with the Hamiltonian view. Herein we review many of the reasons why this traditional Hamiltonian view of complexity is unsatisfactory. We show that as a result of technological advances, which make the observation of single elementary events possible, the definition of complexity has shifted from the conventional memory concept towards the action of non-Poisson renewal events. We show that the observation of crucial processes, such as the intermittent fluorescence of blinking quantum dots as well as the brain’s response to music, as monitored by a set of electrodes attached to the scalp, has forced investigators to go beyond the traditional concept of complexity and to establish closer contact with the nascent field of complex networks. Complex networks form one of the most challenging areas of

  17. 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.

  18. Onset of traffic congestion in complex networks.

    PubMed

    Zhao, Liang; Lai, Ying-Cheng; Park, Kwangho; Ye, Nong

    2005-02-01

    Free traffic flow on a complex network is key to its normal and efficient functioning. Recent works indicate that many realistic networks possess connecting topologies with a scale-free feature: the probability distribution of the number of links at nodes, or the degree distribution, contains a power-law component. A natural question is then how the topology influences the dynamics of traffic flow on a complex network. Here we present two models to address this question, taking into account the network topology, the information-generating rate, and the information-processing capacity of individual nodes. For each model, we study four kinds of networks: scale-free, random, and regular networks and Cayley trees. In the first model, the capacity of packet delivery of each node is proportional to its number of links, while in the second model, it is proportional to the number of shortest paths passing through the node. We find, in both models, that there is a critical rate of information generation, below which the network traffic is free but above which traffic congestion occurs. Theoretical estimates are given for the critical point. For the first model, scale-free networks and random networks are found to be more tolerant to congestion. For the second model, the congestion condition is independent of network size and topology, suggesting that this model may be practically useful for designing communication protocols.

  19. Controlling extreme events on complex networks

    NASA Astrophysics Data System (ADS)

    Chen, Yu-Zhong; Huang, Zi-Gang; Lai, Ying-Cheng

    2014-08-01

    Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network ``mobile'' can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed.

  20. Analysis of complex networks using aggressive abstraction.

    SciTech Connect

    Colbaugh, Richard; Glass, Kristin.; Willard, Gerald

    2008-10-01

    This paper presents a new methodology for analyzing complex networks in which the network of interest is first abstracted to a much simpler (but equivalent) representation, the required analysis is performed using the abstraction, and analytic conclusions are then mapped back to the original network and interpreted there. We begin by identifying a broad and important class of complex networks which admit abstractions that are simultaneously dramatically simplifying and property preserving we call these aggressive abstractions -- and which can therefore be analyzed using the proposed approach. We then introduce and develop two forms of aggressive abstraction: 1.) finite state abstraction, in which dynamical networks with uncountable state spaces are modeled using finite state systems, and 2.) onedimensional abstraction, whereby high dimensional network dynamics are captured in a meaningful way using a single scalar variable. In each case, the property preserving nature of the abstraction process is rigorously established and efficient algorithms are presented for computing the abstraction. The considerable potential of the proposed approach to complex networks analysis is illustrated through case studies involving vulnerability analysis of technological networks and predictive analysis for social processes.

  1. The price of complexity in financial networks

    NASA Astrophysics Data System (ADS)

    Battiston, Stefano; Caldarelli, Guido; May, Robert M.; Roukny, Tarik; Stiglitz, Joseph E.

    2016-09-01

    Financial institutions form multilayer networks by engaging in contracts with each other and by holding exposures to common assets. As a result, the default probability of one institution depends on the default probability of all of the other institutions in the network. Here, we show how small errors on the knowledge of the network of contracts can lead to large errors in the probability of systemic defaults. From the point of view of financial regulators, our findings show that the complexity of financial networks may decrease the ability to mitigate systemic risk, and thus it may increase the social cost of financial crises.

  2. Travel and tourism: Into a complex network

    NASA Astrophysics Data System (ADS)

    Miguéns, J. I. L.; Mendes, J. F. F.

    2008-05-01

    It is discussed how the worldwide tourist arrivals, about 10% of the world’s domestic product, form a largely heterogeneous and directed complex network. Remarkably the random network of connectivity is converted into a scale-free network of intensities. The importance of weights on network connections is brought into discussion. It is also shown how strategic positioning particularly benefits from market diversity and that interactions among countries prevail on a technological and economic pattern, questioning the backbone of driving forces in traveling.

  3. The price of complexity in financial networks

    PubMed Central

    May, Robert M.; Roukny, Tarik; Stiglitz, Joseph E.

    2016-01-01

    Financial institutions form multilayer networks by engaging in contracts with each other and by holding exposures to common assets. As a result, the default probability of one institution depends on the default probability of all of the other institutions in the network. Here, we show how small errors on the knowledge of the network of contracts can lead to large errors in the probability of systemic defaults. From the point of view of financial regulators, our findings show that the complexity of financial networks may decrease the ability to mitigate systemic risk, and thus it may increase the social cost of financial crises. PMID:27555583

  4. The price of complexity in financial networks.

    PubMed

    Battiston, Stefano; Caldarelli, Guido; May, Robert M; Roukny, Tarik; Stiglitz, Joseph E

    2016-09-06

    Financial institutions form multilayer networks by engaging in contracts with each other and by holding exposures to common assets. As a result, the default probability of one institution depends on the default probability of all of the other institutions in the network. Here, we show how small errors on the knowledge of the network of contracts can lead to large errors in the probability of systemic defaults. From the point of view of financial regulators, our findings show that the complexity of financial networks may decrease the ability to mitigate systemic risk, and thus it may increase the social cost of financial crises.

  5. Quantum Navigation and Ranking in Complex Networks

    PubMed Central

    Sánchez-Burillo, Eduardo; Duch, Jordi; Gómez-Gardeñes, Jesús; Zueco, David

    2012-01-01

    Complex networks are formal frameworks capturing the interdependencies between the elements of large systems and databases. This formalism allows to use network navigation methods to rank the importance that each constituent has on the global organization of the system. A key example is Pagerank navigation which is at the core of the most used search engine of the World Wide Web. Inspired in this classical algorithm, we define a quantum navigation method providing a unique ranking of the elements of a network. We analyze the convergence of quantum navigation to the stationary rank of networks and show that quantumness decreases the number of navigation steps before convergence. In addition, we show that quantum navigation allows to solve degeneracies found in classical ranks. By implementing the quantum algorithm in real networks, we confirm these improvements and show that quantum coherence unveils new hierarchical features about the global organization of complex systems. PMID:22930671

  6. Quantum Navigation and Ranking in Complex Networks

    NASA Astrophysics Data System (ADS)

    Sánchez-Burillo, Eduardo; Duch, Jordi; Gómez-Gardeñes, Jesús; Zueco, David

    2012-08-01

    Complex networks are formal frameworks capturing the interdependencies between the elements of large systems and databases. This formalism allows to use network navigation methods to rank the importance that each constituent has on the global organization of the system. A key example is Pagerank navigation which is at the core of the most used search engine of the World Wide Web. Inspired in this classical algorithm, we define a quantum navigation method providing a unique ranking of the elements of a network. We analyze the convergence of quantum navigation to the stationary rank of networks and show that quantumness decreases the number of navigation steps before convergence. In addition, we show that quantum navigation allows to solve degeneracies found in classical ranks. By implementing the quantum algorithm in real networks, we confirm these improvements and show that quantum coherence unveils new hierarchical features about the global organization of complex systems.

  7. Predicting and Controlling Complex Networks

    DTIC Science & Technology

    2015-06-22

    networks and control . . . . . . . . . . . . . . . . . . . 7 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically...Ni, Y.-C. Lai, and C. Grebogi, “Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games,” Physical Review E 83...of Physics B 76, 179-183 (2010). 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games Biodiversity is

  8. Shock waves on complex networks

    PubMed Central

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

    2014-01-01

    Power grids, road maps, and river streams are examples of infrastructural networks which are highly vulnerable to external perturbations. An abrupt local change of load (voltage, traffic density, or water level) might propagate in a cascading way and affect a significant fraction of the network. Almost discontinuous perturbations can be modeled by shock waves which can eventually interfere constructively and endanger the normal functionality of the infrastructure. We study their dynamics by solving the Burgers equation under random perturbations on several real and artificial directed graphs. Even for graphs with a narrow distribution of node properties (e.g., degree or betweenness), a steady state is reached exhibiting a heterogeneous load distribution, having a difference of one order of magnitude between the highest and average loads. Unexpectedly we find for the European power grid and for finite Watts-Strogatz networks a broad pronounced bimodal distribution for the loads. To identify the most vulnerable nodes, we introduce the concept of node-basin size, a purely topological property which we show to be strongly correlated to the average load of a node. PMID:24821422

  9. Phase transitions in complex network dynamics

    NASA Astrophysics Data System (ADS)

    Squires, Shane

    Two phase transitions in complex networks are analyzed. The first of these is a percolation transition, in which the network develops a macroscopic connected component as edges are added to it. Recent work has shown that if edges are added "competitively" to an undirected network, the onset of percolation is abrupt or "explosive." A new variant of explosive percolation is introduced here for directed networks, whose critical behavior is explored using numerical simulations and finite-size scaling theory. This process is also characterized by a very rapid percolation transition, but it is not as sudden as in undirected networks. The second phase transition considered here is the emergence of instability in Boolean networks, a class of dynamical systems that are widely used to model gene regulation. The dynamics, which are determined by the network topology and a set of update rules, may be either stable or unstable, meaning that small perturbations to the state of the network either die out or grow to become macroscopic. Here, this transition is analytically mapped onto a well-studied percolation problem, which can be used to predict the average steady-state distance between perturbed and unperturbed trajectories. This map applies to specific Boolean networks with few restrictions on network topology, but can only be applied to two commonly used types of update rules. Finally, a method is introduced for predicting the stability of Boolean networks with a much broader range of update rules. The network is assumed to have a given complex topology, subject only to a locally tree-like condition, and the update rules may be correlated with topological features of the network. While past work has addressed the separate effects of topology and update rules on stability, the present results are the first widely applicable approach to studying how these effects interact. Numerical simulations agree with the theory and show that such correlations between topology and update

  10. Complex Cooperative Networks from Evolutionary Preferential Attachment

    PubMed Central

    Poncela, Julia; Gómez-Gardeñes, Jesús; Floría, Luis M.; Sánchez, Angel; Moreno, Yamir

    2008-01-01

    In spite of its relevance to the origin of complex networks, the interplay between form and function and its role during network formation remains largely unexplored. While recent studies introduce dynamics by considering rewiring processes of a pre-existent network, we study network growth and formation by proposing an evolutionary preferential attachment model, its main feature being that the capacity of a node to attract new links depends on a dynamical variable governed in turn by the node interactions. As a specific example, we focus on the problem of the emergence of cooperation by analyzing the formation of a social network with interactions given by the Prisoner's Dilemma. The resulting networks show many features of real systems, such as scale-free degree distributions, cooperative behavior and hierarchical clustering. Interestingly, results such as the cooperators being located mostly on nodes of intermediate degree are very different from the observations of cooperative behavior on static networks. The evolutionary preferential attachment mechanism points to an evolutionary origin of scale-free networks and may help understand similar feedback problems in the dynamics of complex networks by appropriately choosing the game describing the interaction of nodes. PMID:18560601

  11. Distributed multiple path routing in complex networks

    NASA Astrophysics Data System (ADS)

    Chen, Guang; Wang, San-Xiu; Wu, Ling-Wei; Mei, Pan; Yang, Xu-Hua; Wen, Guang-Hui

    2016-12-01

    Routing in complex transmission networks is an important problem that has garnered extensive research interest in the recent years. In this paper, we propose a novel routing strategy called the distributed multiple path (DMP) routing strategy. For each of the O-D node pairs in a given network, the DMP routing strategy computes and stores multiple short-length paths that overlap less with each other in advance. And during the transmission stage, it rapidly selects an actual routing path which provides low transmission cost from the pre-computed paths for each transmission task, according to the real-time network transmission status information. Computer simulation results obtained for the lattice, ER random, and scale-free networks indicate that the strategy can significantly improve the anti-congestion ability of transmission networks, as well as provide favorable routing robustness against partial network failures.

  12. Random matrix analysis of complex networks.

    PubMed

    Jalan, Sarika; Bandyopadhyay, Jayendra N

    2007-10-01

    We study complex networks under random matrix theory (RMT) framework. Using nearest-neighbor and next-nearest-neighbor spacing distributions we analyze the eigenvalues of the adjacency matrix of various model networks, namely, random, scale-free, and small-world networks. These distributions follow the Gaussian orthogonal ensemble statistic of RMT. To probe long-range correlations in the eigenvalues we study spectral rigidity via the Delta_{3} statistic of RMT as well. It follows RMT prediction of linear behavior in semilogarithmic scale with the slope being approximately 1pi;{2} . Random and scale-free networks follow RMT prediction for very large scale. A small-world network follows it for sufficiently large scale, but much less than the random and scale-free networks.

  13. Network model of bilateral power markets based on complex networks

    NASA Astrophysics Data System (ADS)

    Wu, Yang; Liu, Junyong; Li, Furong; Yan, Zhanxin; Zhang, Li

    2014-06-01

    The bilateral power transaction (BPT) mode becomes a typical market organization with the restructuring of electric power industry, the proper model which could capture its characteristics is in urgent need. However, the model is lacking because of this market organization's complexity. As a promising approach to modeling complex systems, complex networks could provide a sound theoretical framework for developing proper simulation model. In this paper, a complex network model of the BPT market is proposed. In this model, price advantage mechanism is a precondition. Unlike other general commodity transactions, both of the financial layer and the physical layer are considered in the model. Through simulation analysis, the feasibility and validity of the model are verified. At same time, some typical statistical features of BPT network are identified. Namely, the degree distribution follows the power law, the clustering coefficient is low and the average path length is a bit long. Moreover, the topological stability of the BPT network is tested. The results show that the network displays a topological robustness to random market member's failures while it is fragile against deliberate attacks, and the network could resist cascading failure to some extent. These features are helpful for making decisions and risk management in BPT markets.

  14. Opinion control in complex networks

    NASA Astrophysics Data System (ADS)

    Masuda, Naoki

    2015-03-01

    In many political elections, the electorate appears to be a composite of partisan and independent voters. Given that partisans are not likely to convert to a different party, an important goal for a political party could be to mobilize independent voters toward the party with the help of strong leadership, mass media, partisans, and the effects of peer-to-peer influence. Based on the exact solution of classical voter model dynamics in the presence of perfectly partisan voters (i.e., zealots), we propose a computational method that uses pinning control strategy to maximize the share of a party in a social network of independent voters. The party, corresponding to the controller or zealots, optimizes the nodes to be controlled given the information about the connectivity of independent voters and the set of nodes that the opposing party controls. We show that controlling hubs is generally a good strategy, but the optimized strategy is even better. The superiority of the optimized strategy is particularly eminent when the independent voters are connected as directed (rather than undirected) networks.

  15. Analytical and experimental study on complex compressed air pipe network

    NASA Astrophysics Data System (ADS)

    Gai, Yushou; Cai, Maolin; Shi, Yan

    2015-09-01

    To analyze the working characteristics of complex compressed air networks, numerical methods are widely used which are based on finite element technology or intelligent algorithms. However, the effectiveness of the numerical methods is limited. In this paper, to provide a new method to optimize the design and the air supply strategy of the complex compressed air pipe network, firstly, a novel method to analyze the topology structure of the compressed air flow in the pipe network is initially proposed. A matrix is used to describe the topology structure of the compressed air flow. Moreover, based on the analysis of the pressure loss of the pipe network, the relationship between the pressure and the flow of the compressed air is derived, and a prediction method of pressure fluctuation and air flow in a segment in a complex pipe network is proposed. Finally, to inspect the effectiveness of the method, an experiment with a complex network is designed. The pressure and the flow of airflow in the network are measured and studied. The results of the study show that, the predicted results with the proposed method have a good consistency with the experimental results, and that verifies the air flow prediction method of the complex pipe network. This research proposes a new method to analyze the compressed air network and a prediction method of pressure fluctuation and air flow in a segment, which can predicate the fluctuation of the pressure according to the flow of compressed air, and predicate the fluctuation of the flow according to the pressure in a segment of a complex pipe network.

  16. Self-organized topology of recurrence-based complex networks.

    PubMed

    Yang, Hui; Liu, Gang

    2013-12-01

    With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., "what is the self-organizing geometry of a recurrence network?" and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.

  17. Social networks as embedded complex adaptive systems.

    PubMed

    Benham-Hutchins, Marge; Clancy, Thomas R

    2010-09-01

    As systems evolve over time, their natural tendency is to become increasingly more complex. Studies in the field of complex systems have generated new perspectives on management in social organizations such as hospitals. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. This is the 15th in a series of articles applying complex systems science to the traditional management concepts of planning, organizing, directing, coordinating, and controlling. In this article, the authors discuss healthcare social networks as a hierarchy of embedded complex adaptive systems. The authors further examine the use of social network analysis tools as a means to understand complex communication patterns and reduce medical errors.

  18. Predictive Control of Large Complex Networks

    NASA Astrophysics Data System (ADS)

    Haber, Aleksandar; Motter, Adilson E.

    Networks of coupled dynamical subsystems are increasingly used to represent complex natural and engineered systems. While recent technological developments give us improved means to actively control the dynamics of individual subsystems in various domains, network control remains a challenging problem due to difficulties imposed by intrinsic nonlinearities, control constraints, and the large-scale nature of the systems. In this talk, we will present a model predictive control approach that is effective while accounting for these realistic properties of complex networks. Our method can systematically identify control interventions that steer the trajectory to a desired state, even in the presence of strong nonlinearities and constraints. Numerical tests show that the method is applicable to a variety of networks, ranging from power grids to chemical reaction systems.

  19. Complex Dynamics in Information Sharing Networks

    NASA Astrophysics Data System (ADS)

    Cronin, Bruce

    This study examines the roll-out of an electronic knowledge base in a medium-sized professional services firm over a six year period. The efficiency of such implementation is a key business problem in IT systems of this type. Data from usage logs provides the basis for analysis of the dynamic evolution of social networks around the depository during this time. The adoption pattern follows an "s-curve" and usage exhibits something of a power law distribution, both attributable to network effects, and network position is associated with organisational performance on a number of indicators. But periodicity in usage is evident and the usage distribution displays an exponential cut-off. Further analysis provides some evidence of mathematical complexity in the periodicity. Some implications of complex patterns in social network data for research and management are discussed. The study provides a case study demonstrating the utility of the broad methodological approach.

  20. Complex network analysis of time series

    NASA Astrophysics Data System (ADS)

    Gao, Zhong-Ke; Small, Michael; Kurths, Jürgen

    2016-12-01

    Revealing complicated behaviors from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. The past decade has witnessed a rapid development of complex network studies, which allow to characterize many types of systems in nature and technology that contain a large number of components interacting with each other in a complicated manner. Recently, the complex network theory has been incorporated into the analysis of time series and fruitful achievements have been obtained. Complex network analysis of time series opens up new venues to address interdisciplinary challenges in climate dynamics, multiphase flow, brain functions, ECG dynamics, economics and traffic systems.

  1. Micro-Macro Analysis of Complex Networks

    PubMed Central

    Marchiori, Massimo; Possamai, Lino

    2015-01-01

    Complex systems have attracted considerable interest because of their wide range of applications, and are often studied via a “classic” approach: study a specific system, find a complex network behind it, and analyze the corresponding properties. This simple methodology has produced a great deal of interesting results, but relies on an often implicit underlying assumption: the level of detail on which the system is observed. However, in many situations, physical or abstract, the level of detail can be one out of many, and might also depend on intrinsic limitations in viewing the data with a different level of abstraction or precision. So, a fundamental question arises: do properties of a network depend on its level of observability, or are they invariant? If there is a dependence, then an apparently correct network modeling could in fact just be a bad approximation of the true behavior of a complex system. In order to answer this question, we propose a novel micro-macro analysis of complex systems that quantitatively describes how the structure of complex networks varies as a function of the detail level. To this extent, we have developed a new telescopic algorithm that abstracts from the local properties of a system and reconstructs the original structure according to a fuzziness level. This way we can study what happens when passing from a fine level of detail (“micro”) to a different scale level (“macro”), and analyze the corresponding behavior in this transition, obtaining a deeper spectrum analysis. The obtained results show that many important properties are not universally invariant with respect to the level of detail, but instead strongly depend on the specific level on which a network is observed. Therefore, caution should be taken in every situation where a complex network is considered, if its context allows for different levels of observability. PMID:25635812

  2. Structurally robust control of complex networks

    NASA Astrophysics Data System (ADS)

    Nacher, Jose C.; Akutsu, Tatsuya

    2015-01-01

    Robust control theory has been successfully applied to numerous real-world problems using a small set of devices called controllers. However, the real systems represented by networks contain unreliable components and modern robust control engineering has not addressed the problem of structural changes on complex networks including scale-free topologies. Here, we introduce the concept of structurally robust control of complex networks and provide a concrete example using an algorithmic framework that is widely applied in engineering. The developed analytical tools, computer simulations, and real network analyses lead herein to the discovery that robust control can be achieved in scale-free networks with exactly the same order of controllers required in a standard nonrobust configuration by adjusting only the minimum degree. The presented methodology also addresses the probabilistic failure of links in real systems, such as neural synaptic unreliability in Caenorhabditis elegans, and suggests a new direction to pursue in studies of complex networks in which control theory has a role.

  3. Studies on a network of complex neurons

    NASA Astrophysics Data System (ADS)

    Chakravarthy, Srinivasa V.; Ghosh, Joydeep

    1993-08-01

    In the last decade, much effort has been directed towards understanding the role of chaos in the brain. Work with rabbits reveals that in the resting state the electrical activity on the surface of the olfactory bulb is chaotic. But, when the animal is involved in a recognition task, the activity shifts to a specific pattern corresponding to the odor that is being recognized. Unstable, quasiperiodic behavior can be found in a class of conservative, deterministic physical systems called the Hamiltonian systems. In this paper, we formulate a complex version of Hopfield's network os real parameters and show that a variation on this model is a conservative system. Conditions under which the complex network can be used as a Content Addressable memory are studied. We also examine the effect of singularities of the complex sigmoid function on the network dynamics. The network exhibits unpredictable behavior at the singularities due to the failure of a uniqueness condition for the solution of the dynamic equations. On incorporating a weight adaptation rule, the structure of the resulting complex network equations is shown to have an interesting similarity with Kosko's Adaptive Bidirectional Associative Memory.

  4. Studies on a network of complex neurons

    NASA Astrophysics Data System (ADS)

    Chakravarthy, Srinivasa V.; Ghosh, Joydeep

    1993-09-01

    In the last decade, much effort has been directed towards understanding the role of chaos in the brain. Work with rabbits reveals that in the resting state the electrical activity on the surface of the olfactory bulb is chaotic. But, when the animal is involved in a recognition task, the activity shifts to a specific pattern corresponding to the odor that is being recognized. Unstable, quasiperiodic behavior can be found in a class of conservative, deterministic physical systems called the Hamiltonian systems. In this paper, we formulate a complex version of Hopfield's network of real parameters and show that a variation on this model is a conservative system. Conditions under which the complex network can be used as a Content Addressable memory are studied. We also examine the effect of singularities of the complex sigmoid function on the network dynamics. The network exhibits unpredictable behavior at the singularities due to the failure of a uniqueness condition for the solution of the dynamic equations. On incorporating a weight adaptation rule, the structure of the resulting complex network equations is shown to have an interesting similarity with Kosko's Adaptive Bidirectional Associative Memory.

  5. Complexity, dynamic cellular network, and tumorigenesis.

    PubMed

    Waliszewski, P

    1997-01-01

    A holistic approach to tumorigenesis is proposed. The main element of the model is the existence of dynamic cellular network. This network comprises a molecular and an energetistic structure of a cell connected through the multidirectional flow of information. The interactions within dynamic cellular network are complex, stochastic, nonlinear, and also involve quantum effects. From this non-reductionist perspective, neither tumorigenesis can be limited to the genetic aspect, nor the initial event must be of molecular nature, nor mutations and epigenetic factors are mutually exclusive, nor a link between cause and effect can be established. Due to complexity, an unstable stationary state of dynamic cellular network rather than a group of unrelated genes determines the phenotype of normal and transformed cells. This implies relativity of tumor suppressor genes and oncogenes. A bifurcation point is defined as an unstable state of dynamic cellular network leading to the other phenotype-stationary state. In particular, the bifurcation point may be determined by a change of expression of a single gene. Then, the gene is called bifurcation point gene. The unstable stationary state facilitates the chaotic dynamics. This may result in a fractal dimension of both normal and tumor tissues. The co-existence of chaotic dynamics and complexity is the essence of cellular processes and shapes differentiation, morphogenesis, and tumorigenesis. In consequence, tumorigenesis is a complex, unpredictable process driven by the interplay between self-organisation and selection.

  6. Size reduction of complex networks preserving modularity

    SciTech Connect

    Arenas, A.; Duch, J.; Fernandez, A.; Gomez, S.

    2008-12-24

    The ubiquity of modular structure in real-world complex networks is being the focus of attention in many trials to understand the interplay between network topology and functionality. The best approaches to the identification of modular structure are based on the optimization of a quality function known as modularity. However this optimization is a hard task provided that the computational complexity of the problem is in the NP-hard class. Here we propose an exact method for reducing the size of weighted (directed and undirected) complex networks while maintaining invariant its modularity. This size reduction allows the heuristic algorithms that optimize modularity for a better exploration of the modularity landscape. We compare the modularity obtained in several real complex-networks by using the Extremal Optimization algorithm, before and after the size reduction, showing the improvement obtained. We speculate that the proposed analytical size reduction could be extended to an exact coarse graining of the network in the scope of real-space renormalization.

  7. Center of mass in complex networks

    PubMed Central

    Fu, Chuanji; Gao, Yachun; Cai, Shimin; Yang, Hongchun; Yang, Chun

    2017-01-01

    Network dynamics is always a big challenge in nonlinear dynamics. Although great advancements have been made in various types of complex systems, an universal theoretical framework is required. In this paper, we introduce the concept of center of ‘mass’ of complex networks, where ‘mass’ stands for node importance or centrality in contrast to that of particle systems, and further prove that the phase transition and evolutionary state of the system can be characterized by the activity of center of ‘mass’. The steady states of several complex networks (gene regulatory networks and epidemic spreading systems) are then studied by analytically calculating the decoupled equation of the dynamic activity of center of ‘mass’, which is derived from the dynamic equation of the complex networks. The limitations of this method are also pointed out, such as the dynamical problems that related with the relative activities among components, and those systems that consist of oscillatory or chaotic motions. PMID:28106109

  8. Identifying community structure in complex networks

    NASA Astrophysics Data System (ADS)

    Shao, Chenxi; Duan, Yubing

    2015-07-01

    A wide variety of applications could be formulated to resolve the problem of finding all communities from a given network, ranging from social and biological network analysis to web mining and searching. In this study, we propose the concept of virtual attractive strength between each pair of node in networks, and then give the definition of community structure based on the proposed attractive strength. Furthermore, we present a community detection method by moving vertices to the clusters that produce the largest attractive strengths to them until the division of network reaches unchanged. Experimental results on synthetic and real networks indicate that the proposed approach has favorite effectiveness and fast convergence speed, which provides an efficient method for exploring and analyzing complex systems.

  9. Characterizing English Poetic Style Using Complex Networks

    NASA Astrophysics Data System (ADS)

    Roxas-Villanueva, Ranzivelle Marianne; Nambatac, Maelori Krista; Tapang, Giovanni

    Complex networks have been proven useful in characterizing written texts. Here, we use networks to probe if there exist a similarity within, and difference across, era as reflected within the poem's structure. In literary history, boundary lines are set to distinguish the change in writing styles through time. We obtain the network parameters and motif frequencies of 845 poems published from 1522 to 1931 and relate this to the writing of the Elizabethan, 17th Century, Augustan, Romantic and Victorian eras. Analysis of the different network parameters shows a significant difference of the Augustan era (1667-1780) with the rest. The network parameters and the convex hull and centroids of the motif frequencies reflect the adjectival sequence pattern of the poems of the Augustan era.

  10. The complexity of Chinese syntactic dependency networks

    NASA Astrophysics Data System (ADS)

    Liu, Haitao

    2008-05-01

    This paper proposes how to build a syntactic network based on syntactic theory and presents some statistical properties of Chinese syntactic dependency networks based on two Chinese treebanks with different genres. The results show that the two syntactic networks are small-world networks, and their degree distributions obey a power law. The finding, that the two syntactic networks have the same diameter and different average degrees, path lengths, clustering coefficients and power exponents, can be seen as an indicator that complexity theory can work as a means of stylistic study. The paper links the degree of a vertex with a valency of a word, the small world with the minimized average distance of a language, that reinforces the explanations of the findings from linguistics.

  11. Competitive cluster growth in complex networks

    NASA Astrophysics Data System (ADS)

    Moreira, André A.; Paula, Demétrius R.; Costa Filho, Raimundo N.; Andrade, José S., Jr.

    2006-06-01

    In this work we propose an idealized model for competitive cluster growth in complex networks. Each cluster can be thought of as a fraction of a community that shares some common opinion. Our results show that the cluster size distribution depends on the particular choice for the topology of the network of contacts among the agents. As an application, we show that the cluster size distributions obtained when the growth process is performed on hierarchical networks, e.g., the Apollonian network, have a scaling form similar to what has been observed for the distribution of a number of votes in an electoral process. We suggest that this similarity may be due to the fact that social networks involved in the electoral process may also possess an underlining hierarchical structure.

  12. Complex root networks of Chinese characters

    NASA Astrophysics Data System (ADS)

    Lee, Po-Han; Chen, Jia-Ling; Wang, Po-Cheng; Chi, Ting-Ting; Xiao, Zhi-Ren; Jhang, Zih-Jian; Yeh, Yeong-Nan; Chen, Yih-Yuh; Hu, Chin-Kun

    There are several sets of Chinese characters still available today, including Oracle Bone Inscriptions (OBI) in Shang Dynasty, Chu characters (CC) used in Chu of Warring State Period, Small Seal Script in dictionary Shuowen Jiezi (SJ) in Eastern Han Dynasty, and Kangxi Dictionary (KD) in Qing Dynasty. Such as Chinese characters were all constructed via combinations of meaningful patterns, called roots. Our studies for the complex networks of all roots indicate that the roots of the characters in OBI, CC, SJ and KD have characteristics of small world networks and scale-free networks.

  13. Extractive summarization using complex networks and syntactic dependency

    NASA Astrophysics Data System (ADS)

    Amancio, Diego R.; Nunes, Maria G. V.; Oliveira, Osvaldo N.; Costa, Luciano da F.

    2012-02-01

    The realization that statistical physics methods can be applied to analyze written texts represented as complex networks has led to several developments in natural language processing, including automatic summarization and evaluation of machine translation. Most importantly, so far only a few metrics of complex networks have been used and therefore there is ample opportunity to enhance the statistics-based methods as new measures of network topology and dynamics are created. In this paper, we employ for the first time the metrics betweenness, vulnerability and diversity to analyze written texts in Brazilian Portuguese. Using strategies based on diversity metrics, a better performance in automatic summarization is achieved in comparison to previous work employing complex networks. With an optimized method the Rouge score (an automatic evaluation method used in summarization) was 0.5089, which is the best value ever achieved for an extractive summarizer with statistical methods based on complex networks for Brazilian Portuguese. Furthermore, the diversity metric can detect keywords with high precision, which is why we believe it is suitable to produce good summaries. It is also shown that incorporating linguistic knowledge through a syntactic parser does enhance the performance of the automatic summarizers, as expected, but the increase in the Rouge score is only minor. These results reinforce the suitability of complex network methods for improving automatic summarizers in particular, and treating text in general.

  14. Dynamic information routing in complex networks

    NASA Astrophysics Data System (ADS)

    Kirst, Christoph; Timme, Marc; Battaglia, Demian

    2015-03-01

    Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how information may be specifically communicated and dynamically routed in these systems is not well understood. Here we demonstrate that collective dynamical states systematically control patterns of information sharing and transfer in networks, as measured by delayed mutual information and transfer entropies between activities of a network's units. For oscillatory networks we analyze how individual unit properties, the connectivity structure and external inputs all provide means to flexibly control information routing. For multi-scale, modular architectures, we resolve communication patterns at all levels and show how local interventions within one sub-network may remotely control the non-local network-wide routing of information. This theory helps understanding information routing patterns across systems where collective dynamics co-occurs with a communication function.

  15. An exploration of graph metric reproducibility in complex brain networks

    PubMed Central

    Telesford, Qawi K.; Burdette, Jonathan H.; Laurienti, Paul J.

    2013-01-01

    The application of graph theory to brain networks has become increasingly popular in the neuroimaging community. These investigations and analyses have led to a greater understanding of the brain's complex organization. More importantly, it has become a useful tool for studying the brain under various states and conditions. With the ever expanding popularity of network science in the neuroimaging community, there is increasing interest to validate the measurements and calculations derived from brain networks. Underpinning these studies is the desire to use brain networks in longitudinal studies or as clinical biomarkers to understand changes in the brain. A highly reproducible tool for brain imaging could potentially prove useful as a clinical tool. In this review, we examine recent studies in network reproducibility and their implications for analysis of brain networks. PMID:23717257

  16. Discriminating complex networks through supervised NDR and Bayesian classifier

    NASA Astrophysics Data System (ADS)

    Yan, Ke-Sheng; Rong, Li-Li; Yu, Kai

    2016-12-01

    Discriminating complex networks is a particularly important task for the purpose of the systematic study of networks. In order to discriminate unknown networks exactly, a large set of network measurements are needed to be taken into account for comprehensively considering network properties. However, as we demonstrate in this paper, these measurements are nonlinear correlated with each other in general, resulting in a wide variety of redundant measurements which unintentionally explain the same aspects of network properties. To solve this problem, we adopt supervised nonlinear dimensionality reduction (NDR) to eliminate the nonlinear redundancy and visualize networks in a low-dimensional projection space. Though unsupervised NDR can achieve the same aim, we illustrate that supervised NDR is more appropriate than unsupervised NDR for discrimination task. After that, we perform Bayesian classifier (BC) in the projection space to discriminate the unknown network by considering the projection score vectors as the input of the classifier. We also demonstrate the feasibility and effectivity of this proposed method in six extensive research real networks, ranging from technological to social or biological. Moreover, the effectiveness and advantage of the proposed method is proved by the contrast experiments with the existing method.

  17. Complexity Measures in Magnetoencephalography: Measuring "Disorder" in Schizophrenia

    PubMed Central

    Brookes, Matthew J.; Hall, Emma L.; Robson, Siân E.; Price, Darren; Palaniyappan, Lena; Liddle, Elizabeth B.; Liddle, Peter F.; Robinson, Stephen E.; Morris, Peter G.

    2015-01-01

    This paper details a methodology which, when applied to magnetoencephalography (MEG) data, is capable of measuring the spatio-temporal dynamics of ‘disorder’ in the human brain. Our method, which is based upon signal entropy, shows that spatially separate brain regions (or networks) generate temporally independent entropy time-courses. These time-courses are modulated by cognitive tasks, with an increase in local neural processing characterised by localised and transient increases in entropy in the neural signal. We explore the relationship between entropy and the more established time-frequency decomposition methods, which elucidate the temporal evolution of neural oscillations. We observe a direct but complex relationship between entropy and oscillatory amplitude, which suggests that these metrics are complementary. Finally, we provide a demonstration of the clinical utility of our method, using it to shed light on aberrant neurophysiological processing in schizophrenia. We demonstrate significantly increased task induced entropy change in patients (compared to controls) in multiple brain regions, including a cingulo-insula network, bilateral insula cortices and a right fronto-parietal network. These findings demonstrate potential clinical utility for our method and support a recent hypothesis that schizophrenia can be characterised by abnormalities in the salience network (a well characterised distributed network comprising bilateral insula and cingulate cortices). PMID:25886553

  18. The complex network of musical tastes

    NASA Astrophysics Data System (ADS)

    Buldú, Javier M.; Cano, P.; Koppenberger, M.; Almendral, Juan A.; Boccaletti, S.

    2007-06-01

    We present an empirical study of the evolution of a social network constructed under the influence of musical tastes. The network is obtained thanks to the selfless effort of a broad community of users who share playlists of their favourite songs with other users. When two songs co-occur in a playlist a link is created between them, leading to a complex network where songs are the fundamental nodes. In this representation, songs in the same playlist could belong to different musical genres, but they are prone to be linked by a certain musical taste (e.g. if songs A and B co-occur in several playlists, an user who likes A will probably like also B). Indeed, playlist collections such as the one under study are the basic material that feeds some commercial music recommendation engines. Since playlists have an input date, we are able to evaluate the topology of this particular complex network from scratch, observing how its characteristic parameters evolve in time. We compare our results with those obtained from an artificial network defined by means of a null model. This comparison yields some insight on the evolution and structure of such a network, which could be used as ground data for the development of proper models. Finally, we gather information that can be useful for the development of music recommendation engines and give some hints about how top-hits appear.

  19. The Kuramoto model in complex networks

    NASA Astrophysics Data System (ADS)

    Rodrigues, Francisco A.; Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen

    2016-01-01

    Synchronization of an ensemble of oscillators is an emergent phenomenon present in several complex systems, ranging from social and physical to biological and technological systems. The most successful approach to describe how coherent behavior emerges in these complex systems is given by the paradigmatic Kuramoto model. This model has been traditionally studied in complete graphs. However, besides being intrinsically dynamical, complex systems present very heterogeneous structure, which can be represented as complex networks. This report is dedicated to review main contributions in the field of synchronization in networks of Kuramoto oscillators. In particular, we provide an overview of the impact of network patterns on the local and global dynamics of coupled phase oscillators. We cover many relevant topics, which encompass a description of the most used analytical approaches and the analysis of several numerical results. Furthermore, we discuss recent developments on variations of the Kuramoto model in networks, including the presence of noise and inertia. The rich potential for applications is discussed for special fields in engineering, neuroscience, physics and Earth science. Finally, we conclude by discussing problems that remain open after the last decade of intensive research on the Kuramoto model and point out some promising directions for future research.

  20. Realizing Wisdom Theory in Complex Learning Networks

    ERIC Educational Resources Information Center

    Kok, Ayse

    2009-01-01

    The word "wisdom" is rarely seen in contemporary technology and learning discourse. This conceptual paper aims to provide some clear principles that answer the question: How can we establish wisdom in complex learning networks? By considering the nature of contemporary calls for wisdom the paper provides a metatheoretial framework to evaluate the…

  1. Controlling extreme events on complex networks

    PubMed Central

    Chen, Yu-Zhong; Huang, Zi-Gang; Lai, Ying-Cheng

    2014-01-01

    Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network “mobile” can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed. PMID:25131344

  2. Amplitude dynamics favors synchronization in complex networks

    PubMed Central

    Gambuzza, Lucia Valentina; Gómez-Gardeñes, Jesus; Frasca, Mattia

    2016-01-01

    In this paper we study phase synchronization in random complex networks of coupled periodic oscillators. In particular, we show that, when amplitude dynamics is not negligible, phase synchronization may be enhanced. To illustrate this, we compare the behavior of heterogeneous units with both amplitude and phase dynamics and pure (Kuramoto) phase oscillators. We find that in small network motifs the behavior crucially depends on the topology and on the node frequency distribution. Surprisingly, the microscopic structures for which the amplitude dynamics improves synchronization are those that are statistically more abundant in random complex networks. Thus, amplitude dynamics leads to a general lowering of the synchronization threshold in arbitrary random topologies. Finally, we show that this synchronization enhancement is generic of oscillators close to Hopf bifurcations. To this aim we consider coupled FitzHugh-Nagumo units modeling neuron dynamics. PMID:27108847

  3. Amplitude dynamics favors synchronization in complex networks

    NASA Astrophysics Data System (ADS)

    Gambuzza, Lucia Valentina; Gómez-Gardeñes, Jesus; Frasca, Mattia

    2016-04-01

    In this paper we study phase synchronization in random complex networks of coupled periodic oscillators. In particular, we show that, when amplitude dynamics is not negligible, phase synchronization may be enhanced. To illustrate this, we compare the behavior of heterogeneous units with both amplitude and phase dynamics and pure (Kuramoto) phase oscillators. We find that in small network motifs the behavior crucially depends on the topology and on the node frequency distribution. Surprisingly, the microscopic structures for which the amplitude dynamics improves synchronization are those that are statistically more abundant in random complex networks. Thus, amplitude dynamics leads to a general lowering of the synchronization threshold in arbitrary random topologies. Finally, we show that this synchronization enhancement is generic of oscillators close to Hopf bifurcations. To this aim we consider coupled FitzHugh-Nagumo units modeling neuron dynamics.

  4. Emergence of fractal scaling in complex networks.

    PubMed

    Wei, Zong-Wen; Wang, Bing-Hong

    2016-09-01

    Some real-world networks are shown to be fractal or self-similar. It is widespread that such a phenomenon originates from the repulsion between hubs or disassortativity. Here we show that this common belief fails to capture the causality. Our key insight to address it is to pinpoint links critical to fractality. Those links with small edge betweenness centrality (BC) constitute a special architecture called fractal reference system, which gives birth to the fractal structure of those reported networks. In contrast, a small amount of links with high BC enable small-world effects, hiding the intrinsic fractality. With enough of such links removed, fractal scaling spontaneously arises from nonfractal networks. Our results provide a multiple-scale view on the structure and dynamics and place fractality as a generic organizing principle of complex networks on a firmer ground.

  5. Emergence of fractal scaling in complex networks

    NASA Astrophysics Data System (ADS)

    Wei, Zong-Wen; Wang, Bing-Hong

    2016-09-01

    Some real-world networks are shown to be fractal or self-similar. It is widespread that such a phenomenon originates from the repulsion between hubs or disassortativity. Here we show that this common belief fails to capture the causality. Our key insight to address it is to pinpoint links critical to fractality. Those links with small edge betweenness centrality (BC) constitute a special architecture called fractal reference system, which gives birth to the fractal structure of those reported networks. In contrast, a small amount of links with high BC enable small-world effects, hiding the intrinsic fractality. With enough of such links removed, fractal scaling spontaneously arises from nonfractal networks. Our results provide a multiple-scale view on the structure and dynamics and place fractality as a generic organizing principle of complex networks on a firmer ground.

  6. The generalization complexity measure for continuous input data.

    PubMed

    Gómez, Iván; Cannas, Sergio A; Osenda, Omar; Jerez, José M; Franco, Leonardo

    2014-01-01

    We introduce in this work an extension for the generalization complexity measure to continuous input data. The measure, originally defined in Boolean space, quantifies the complexity of data in relationship to the prediction accuracy that can be expected when using a supervised classifier like a neural network, SVM, and so forth. We first extend the original measure for its use with continuous functions to later on, using an approach based on the use of the set of Walsh functions, consider the case of having a finite number of data points (inputs/outputs pairs), that is, usually the practical case. Using a set of trigonometric functions a model that gives a relationship between the size of the hidden layer of a neural network and the complexity is constructed. Finally, we demonstrate the application of the introduced complexity measure, by using the generated model, to the problem of estimating an adequate neural network architecture for real-world data sets.

  7. The Generalization Complexity Measure for Continuous Input Data

    PubMed Central

    Cannas, Sergio A.; Osenda, Omar; Jerez, José M.

    2014-01-01

    We introduce in this work an extension for the generalization complexity measure to continuous input data. The measure, originally defined in Boolean space, quantifies the complexity of data in relationship to the prediction accuracy that can be expected when using a supervised classifier like a neural network, SVM, and so forth. We first extend the original measure for its use with continuous functions to later on, using an approach based on the use of the set of Walsh functions, consider the case of having a finite number of data points (inputs/outputs pairs), that is, usually the practical case. Using a set of trigonometric functions a model that gives a relationship between the size of the hidden layer of a neural network and the complexity is constructed. Finally, we demonstrate the application of the introduced complexity measure, by using the generated model, to the problem of estimating an adequate neural network architecture for real-world data sets. PMID:24983000

  8. Markovian dynamics on complex reaction networks

    NASA Astrophysics Data System (ADS)

    Goutsias, J.; Jenkinson, G.

    2013-08-01

    Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underlying population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions and the large size of the underlying state-spaces, computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.

  9. A graph clustering method for community detection in complex networks

    NASA Astrophysics Data System (ADS)

    Zhou, HongFang; Li, Jin; Li, JunHuai; Zhang, FaCun; Cui, YingAn

    2017-03-01

    Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.

  10. Surprise maximization reveals the community structure of complex networks

    PubMed Central

    Aldecoa, Rodrigo; Marín, Ignacio

    2013-01-01

    How to determine the community structure of complex networks is an open question. It is critical to establish the best strategies for community detection in networks of unknown structure. Here, using standard synthetic benchmarks, we show that none of the algorithms hitherto developed for community structure characterization perform optimally. Significantly, evaluating the results according to their modularity, the most popular measure of the quality of a partition, systematically provides mistaken solutions. However, a novel quality function, called Surprise, can be used to elucidate which is the optimal division into communities. Consequently, we show that the best strategy to find the community structure of all the networks examined involves choosing among the solutions provided by multiple algorithms the one with the highest Surprise value. We conclude that Surprise maximization precisely reveals the community structure of complex networks. PMID:23320141

  11. Predicting links based on knowledge dissemination in complex network

    NASA Astrophysics Data System (ADS)

    Zhou, Wen; Jia, Yifan

    2017-04-01

    Link prediction is the task of mining the missing links in networks or predicting the next vertex pair to be connected by a link. A lot of link prediction methods were inspired by evolutionary processes of networks. In this paper, a new mechanism for the formation of complex networks called knowledge dissemination (KD) is proposed with the assumption of knowledge disseminating through the paths of a network. Accordingly, a new link prediction method-knowledge dissemination based link prediction (KDLP)-is proposed to test KD. KDLP characterizes vertex similarity based on knowledge quantity (KQ) which measures the importance of a vertex through H-index. Extensive numerical simulations on six real-world networks demonstrate that KDLP is a strong link prediction method which performs at a higher prediction accuracy than four well-known similarity measures including common neighbors, local path index, average commute time and matrix forest index. Furthermore, based on the common conclusion that an excellent link prediction method reveals a good evolving mechanism, the experiment results suggest that KD is a considerable network evolving mechanism for the formation of complex networks.

  12. Disease Surveillance on Complex Social Networks

    PubMed Central

    Herrera, Jose L.; Srinivasan, Ravi; Brownstein, John S.; Galvani, Alison P.; Meyers, Lauren Ancel

    2016-01-01

    As infectious disease surveillance systems expand to include digital, crowd-sourced, and social network data, public health agencies are gaining unprecedented access to high-resolution data and have an opportunity to selectively monitor informative individuals. Contact networks, which are the webs of interaction through which diseases spread, determine whether and when individuals become infected, and thus who might serve as early and accurate surveillance sensors. Here, we evaluate three strategies for selecting sensors—sampling the most connected, random, and friends of random individuals—in three complex social networks—a simple scale-free network, an empirical Venezuelan college student network, and an empirical Montreal wireless hotspot usage network. Across five different surveillance goals—early and accurate detection of epidemic emergence and peak, and general situational awareness—we find that the optimal choice of sensors depends on the public health goal, the underlying network and the reproduction number of the disease (R0). For diseases with a low R0, the most connected individuals provide the earliest and most accurate information about both the onset and peak of an outbreak. However, identifying network hubs is often impractical, and they can be misleading if monitored for general situational awareness, if the underlying network has significant community structure, or if R0 is high or unknown. Taking a theoretical approach, we also derive the optimal surveillance system for early outbreak detection but find that real-world identification of such sensors would be nearly impossible. By contrast, the friends-of-random strategy offers a more practical and robust alternative. It can be readily implemented without prior knowledge of the network, and by identifying sensors with higher than average, but not the highest, epidemiological risk, it provides reasonably early and accurate information. PMID:27415615

  13. Universality at Breakdown of Quantum Transport on Complex Networks.

    PubMed

    Kulvelis, Nikolaj; Dolgushev, Maxim; Mülken, Oliver

    2015-09-18

    We consider single-particle quantum transport on parametrized complex networks. Based on general arguments regarding the spectrum of the corresponding Hamiltonian, we derive bounds for a measure of the global transport efficiency defined by the time-averaged return probability. For treelike networks, we show analytically that a transition from efficient to inefficient transport occurs depending on the (average) functionality of the nodes of the network. In the infinite system size limit, this transition can be characterized by an exponent which is universal for all treelike networks. Our findings are corroborated by analytic results for specific deterministic networks, dendrimers and Vicsek fractals, and by Monte Carlo simulations of iteratively built scale-free trees.

  14. Post Disaster Governance, Complexity and Network Theory

    PubMed Central

    Lassa, Jonatan A.

    2015-01-01

    This research aims to understand the organizational network typology of large­-scale disaster intervention in developing countries and to understand the complexity of post-­disaster intervention, through the use of network theory based on empirical data from post-­tsunami reconstruction in Aceh, Indonesia, during 2005/­2007. The findings suggest that the ‘ degrees of separation’ (or network diameter) between any two organizations in the field is 5, thus reflecting ‘small­ world’ realities and therefore making no significant difference with the real human networks, as found in previous experiments. There are also significant loops in the network reflecting the fact that some actors tend to not cooperate, which challenges post­ disaster coordination. The findings show the landscape of humanitarian actors is not randomly distributed. Many actors were connected to each other through certain hubs, while hundreds of actors make ‘scattered’ single ‘principal-­client’ links. The paper concludes that by understanding the distribution of degree, centrality, ‘degrees of separation’ and visualization of the network, authorities can improve their understanding of the realities of coordination, from macro to micro scales. PMID:26236562

  15. Robust Multiobjective Controllability of Complex Neuronal Networks.

    PubMed

    Tang, Yang; Gao, Huijun; Du, Wei; Lu, Jianquan; Vasilakos, Athanasios V; Kurths, Jurgen

    2016-01-01

    This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc.

  16. Study of complex networks using statistical physics methods

    NASA Astrophysics Data System (ADS)

    Chen, Yiping

    The goal of this thesis is to study the behaviors of complex networks in several aspects using methods from statistical physics. Networks are structures that consist of nodes and links. By changing the way links connect to nodes, different complex network structures can be constructed such as Erdḧs-Renyi (ER) networks and scale-free (SF) networks. Complex networks have wide relevance to many real world problems, including the spread of disease in human society, message routing in the Internet, etc. In this thesis analytical and simulation results are obtained regarding optimal paths in disordered networks, fragmentation of social networks, and improved strategies for immunization against diseases. In the study of disordered systems, of particular current interest is the scaling behavior of the optimal path length ℓopt from strong disorder to weak disorder state for different weight distributions P(w). Here we derive analytically a new criterion. Using this criterion we find that for all P(w) that possess a strong-weak disorder crossover, the distributions p(ℓ) of the optimal path lengths display the same universal behavior. Fragmentation in social networks is also studied using methods from percolation theory. Recently, a new measure of fragmentation F has been developed in social network studies. For each removal of a subset of links or nodes, F is defined as the ratio between the number of pairs of nodes that are not connected in the fragmented network after removal, and the total number of pairs in the original fully connected network. We study the statistical behavior of F using both analytical and numerical methods and relate it to the traditional measure of fragmentation, the relative size of the largest cluster, Pinfinity, used in percolation theory. Finally, we tried to find a better immunization strategy. It is widely accepted that the most efficient immunization strategies are based on "targeted" strategies. Here we propose a novel "equal graph

  17. The complex networks approach for authorship attribution of books

    NASA Astrophysics Data System (ADS)

    Mehri, Ali; Darooneh, Amir H.; Shariati, Ashrafalsadat

    2012-04-01

    Authorship analysis by means of textual features is an important task in linguistic studies. We employ complex networks theory to tackle this disputed problem. In this work, we focus on some measurable quantities of word co-occurrence network of each book for authorship characterization. Based on the network features, attribution probability is defined for authorship identification. Furthermore, two scaling exponents, q-parameter and α-exponent, are combined to classify personal writing style with acceptable high resolution power. The q-parameter, generally known as the nonextensivity measure, is calculated for degree distribution and the α-exponent comes from a power law relationship between number of links and number of nodes in the co-occurrence network constructed for different books written by each author. The applicability of the presented method is evaluated in an experiment with thirty six books of five Persian litterateurs. Our results show high accuracy rate in authorship attribution.

  18. Strong correlations between text quality and complex networks features

    NASA Astrophysics Data System (ADS)

    Antiqueira, L.; Nunes, M. G. V.; Oliveira, O. N., Jr.; F. Costa, L. da

    2007-01-01

    Concepts of complex networks have been used to obtain metrics that were correlated to text quality established by scores assigned by human judges. Texts produced by high-school students in Portuguese were represented as scale-free networks (word adjacency model), from which typical network features such as the in/outdegree, clustering coefficient and shortest path were obtained. Another metric was derived from the dynamics of the network growth, based on the variation of the number of connected components. The scores assigned by the human judges according to three text quality criteria (coherence and cohesion, adherence to standard writing conventions and theme adequacy/development) were correlated with the network measurements. Text quality for all three criteria was found to decrease with increasing average values of outdegrees, clustering coefficient and deviation from the dynamics of network growth. Among the criteria employed, cohesion and coherence showed the strongest correlation, which probably indicates that the network measurements are able to capture how the text is developed in terms of the concepts represented by the nodes in the networks. Though based on a particular set of texts and specific language, the results presented here point to potential applications in other instances of text analysis.

  19. Stochastic competitive learning in complex networks.

    PubMed

    Silva, Thiago Christiano; Zhao, Liang

    2012-03-01

    Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning..

  20. Different Epidemic Models on Complex Networks

    NASA Astrophysics Data System (ADS)

    Zhang, Hai-Feng; Small, Michael; Fu, Xin-Chu

    2009-07-01

    Models for diseases spreading are not just limited to SIS or SIR. For instance, for the spreading of AIDS/HIV, the susceptible individuals can be classified into different cases according to their immunity, and similarly, the infected individuals can be sorted into different classes according to their infectivity. Moreover, some diseases may develop through several stages. Many authors have shown that the individuals' relation can be viewed as a complex network. So in this paper, in order to better explain the dynamical behavior of epidemics, we consider different epidemic models on complex networks, and obtain the epidemic threshold for each case. Finally, we present numerical simulations for each case to verify our results.

  1. Complex network approach to fractional time series

    SciTech Connect

    Manshour, Pouya

    2015-10-15

    In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.

  2. Complex networks analysis of obstructive nephropathy data

    NASA Astrophysics Data System (ADS)

    Zanin, M.; Boccaletti, S.

    2011-09-01

    Congenital obstructive nephropathy (ON) is one of the most frequent and complex diseases affecting children, characterized by an abnormal flux of the urine, due to a partial or complete obstruction of the urinary tract; as a consequence, urine may accumulate in the kidney and disturb the normal operation of the organ. Despite important advances, pathological mechanisms are not yet fully understood. In this contribution, the topology of complex networks, based on vectors of features of control and ON subjects, is related with the severity of the pathology. Nodes in these networks represent genetic and metabolic profiles, while connections between them indicate an abnormal relation between their expressions. Resulting topologies allow discriminating ON subjects and detecting which genetic or metabolic elements are responsible for the malfunction.

  3. Preferential urn model and nongrowing complex networks.

    PubMed

    Ohkubo, Jun; Yasuda, Muneki; Tanaka, Kazuyuki

    2005-12-01

    A preferential urn model, which is based on the concept "the rich get richer," is proposed. From a relationship between a nongrowing model for complex networks and the preferential urn model in regard to degree distributions, it is revealed that a fitness parameter in the nongrowing model is interpreted as an inverse local temperature in the preferential urn model. Furthermore, it is clarified that the preferential urn model with randomness generates a fat-tailed occupation distribution; the concept of the local temperature enables us to understand the fat-tailed occupation distribution intuitively. Since the preferential urn model is a simple stochastic model, it can be applied to research on not only the nongrowing complex networks, but also many other fields such as econophysics and social sciences.

  4. Complex growing networks with intrinsic vertex fitness

    SciTech Connect

    Bedogne, C.; Rodgers, G. J.

    2006-10-15

    One of the major questions in complex network research is to identify the range of mechanisms by which a complex network can self organize into a scale-free state. In this paper we investigate the interplay between a fitness linking mechanism and both random and preferential attachment. In our models, each vertex is assigned a fitness x, drawn from a probability distribution {rho}(x). In Model A, at each time step a vertex is added and joined to an existing vertex, selected at random, with probability p and an edge is introduced between vertices with fitnesses x and y, with a rate f(x,y), with probability 1-p. Model B differs from Model A in that, with probability p, edges are added with preferential attachment rather than randomly. The analysis of Model A shows that, for every fixed fitness x, the network's degree distribution decays exponentially. In Model B we recover instead a power-law degree distribution whose exponent depends only on p, and we show how this result can be generalized. The properties of a number of particular networks are examined.

  5. Structural and dynamical properties of complex networks

    NASA Astrophysics Data System (ADS)

    Ghoshal, Gourab

    Recent years have witnessed a substantial amount of interest within the physics community in the properties of networks. Techniques from statistical physics coupled with the widespread availability of computing resources have facilitated studies ranging from large scale empirical analysis of the worldwide web, social networks, biological systems, to the development of theoretical models and tools to explore the various properties of these systems. Following these developments, in this dissertation, we present and solve for a diverse set of new problems, investigating the structural and dynamical properties of both model and real world networks. We start by defining a new metric to measure the stability of network structure to disruptions, and then using a combination of theory and simulation study its properties in detail on artificially generated networks; we then compare our results to a selection of networks from the real world and find good agreement in most cases. In the following chapter, we propose a mathematical model that mimics the structure of popular file-sharing websites such as Flickr and CiteULike and demonstrate that many of its properties can solved exactly in the limit of large network size. The remaining part of the dissertation primarily focuses on the dynamical properties of networks. We first formulate a model of a network that evolves under the addition and deletion of vertices and edges, and solve for the equilibrium degree distribution for a variety of cases of interest. We then consider networks whose structure can be manipulated by adjusting the rules by which vertices enter and leave the network. We focus in particular on degree distributions and show that, with some mild constraints, it is possible by a suitable choice of rules to arrange for the network to have any degree distribution we desire. In addition we define a simple local algorithm by which appropriate rules can be implemented in practice. Finally, we conclude our

  6. Complex Networks Unveiling Spatial Patterns in Turbulence

    NASA Astrophysics Data System (ADS)

    Scarsoglio, Stefania; Iacobello, Giovanni; Ridolfi, Luca

    2016-12-01

    Numerical and experimental turbulence simulations are nowadays reaching the size of the so-called big data, thus requiring refined investigative tools for appropriate statistical analyses and data mining. We present a new approach based on the complex network theory, offering a powerful framework to explore complex systems with a huge number of interacting elements. Although interest in complex networks has been increasing in the past years, few recent studies have been applied to turbulence. We propose an investigation starting from a two-point correlation for the kinetic energy of a forced isotropic field numerically solved. Among all the metrics analyzed, the degree centrality is the most significant, suggesting the formation of spatial patterns which coherently move with similar vorticity over the large eddy turnover time scale. Pattern size can be quantified through a newly-introduced parameter (i.e. average physical distance) and varies from small to intermediate scales. The network analysis allows a systematic identification of different spatial regions, providing new insights into the spatial characterization of turbulent flows. Based on present findings, the application to highly inhomogeneous flows seems promising and deserves additional future investigation.

  7. Vulnerability of complex networks under path-based attacks

    NASA Astrophysics Data System (ADS)

    Pu, Cun-Lai; Cui, Wei

    2015-02-01

    We investigate vulnerability of complex networks including model networks and real-world networks subject to path-based attacks. Specifically, we remove approximately the longest simple path from a network iteratively until there are no paths left in the network. We propose two algorithms, the random augmenting approach (RPA) and the Hamilton-path based approach (HPA), for finding the approximately longest simple path in a network. Results demonstrate that steps of longest-path attacks increase with network density linearly for random networks, while exponentially increasing for scale-free networks. The more homogeneous the degree distribution is, the more fragile the network, which is different from the previous results of node or edge attacks. HPA is generally more efficient than RPA in the longest-path attacks of complex networks. These findings further help us understand the vulnerability of complex systems, better protect complex systems, and design more tolerant complex systems.

  8. Sampling of Complex Networks: A Datamining Approach

    NASA Astrophysics Data System (ADS)

    Loecher, Markus; Dohrmann, Jakob; Bauer, Gernot

    2007-03-01

    Efficient and accurate sampling of big complex networks is still an unsolved problem. As the degree distribution is one of the most commonly used attributes to characterize a network, there have been many attempts in recent papers to derive the original degree distribution from the data obtained during a traceroute- like sampling process. This talk describes a strategy for predicting the original degree of a node using the data obtained from a network by traceroute-like sampling making use of datamining techniques. Only local quantities (the sampled degree k, the redundancy of node detection r, the time of the first discovery of a node t and the distance to the sampling source d) are used as input for the datamining models. Global properties like the betweenness centrality are ignored. These local quantities are examined theoretically and in simulations to increase their value for the predictions. The accuracy of the models is discussed as a function of the number of sources used in the sampling process and the underlying topology of the network. The purpose of this work is to introduce the techniques of the relatively young field of datamining to the discussion on network sampling.

  9. Diffusive capture process on complex networks

    NASA Astrophysics Data System (ADS)

    Lee, Sungmin; Yook, Soon-Hyung; Kim, Yup

    2006-10-01

    We study the dynamical properties of a diffusing lamb captured by a diffusing lion on the complex networks with various sizes of N . We find that the lifetime ⟨T⟩ of a lamb scales as ⟨T⟩˜N and the survival probability S(N→∞,t) becomes finite on scale-free networks with degree exponent γ>3 . However, S(N,t) for γ<3 has a long-living tail on tree-structured scale-free networks and decays exponentially on looped scale-free networks. This suggests that the second moment of degree distribution ⟨k2⟩ is the relevant factor for the dynamical properties in the diffusive capture process. We numerically find that the normalized number of capture events at a node with degree k , n(k) , decreases as n(k)˜k-σ . When γ<3 , n(k) still increases anomalously for k≈kmax , where kmax is the maximum value of k of given networks with size N . We analytically show that n(k) satisfies the relation n(k)˜k2P(k) for any degree distribution P(k) and the total number of capture events Ntot is proportional to ⟨k2⟩ , which causes the γ -dependent behavior of S(N,t) and ⟨T⟩ .

  10. Simulating Operation of a Complex Sensor Network

    NASA Technical Reports Server (NTRS)

    Jennings, Esther; Clare, Loren; Woo, Simon

    2008-01-01

    Simulation Tool for ASCTA Microsensor Network Architecture (STAMiNA) ["ASCTA" denotes the Advanced Sensors Collaborative Technology Alliance.] is a computer program for evaluating conceptual sensor networks deployed over terrain to provide military situational awareness. This or a similar program is needed because of the complexity of interactions among such diverse phenomena as sensing and communication portions of a network, deployment of sensor nodes, effects of terrain, data-fusion algorithms, and threat characteristics. STAMiNA is built upon a commercial network-simulator engine, with extensions to include both sensing and communication models in a discrete-event simulation environment. Users can define (1) a mission environment, including terrain features; (2) objects to be sensed; (3) placements and modalities of sensors, abilities of sensors to sense objects of various types, and sensor false alarm rates; (4) trajectories of threatening objects; (5) means of dissemination and fusion of data; and (6) various network configurations. By use of STAMiNA, one can simulate detection of targets through sensing, dissemination of information by various wireless communication subsystems under various scenarios, and fusion of information, incorporating such metrics as target-detection probabilities, false-alarm rates, and communication loads, and capturing effects of terrain and threat.

  11. Complex Network Structure Influences Processing in Long-Term and Short-Term Memory

    ERIC Educational Resources Information Center

    Vitevitch, Michael S.; Chan, Kit Ying; Roodenrys, Steven

    2012-01-01

    Complex networks describe how entities in systems interact; the structure of such networks is argued to influence processing. One measure of network structure, clustering coefficient, C, measures the extent to which neighbors of a node are also neighbors of each other. Previous psycholinguistic experiments found that the C of phonological…

  12. Competitive seeds-selection in complex networks

    NASA Astrophysics Data System (ADS)

    Zhao, Jiuhua; Liu, Qipeng; Wang, Lin; Wang, Xiaofan

    2017-02-01

    This paper investigates a competitive diffusion model where two competitors simultaneously select a set of nodes (seeds) in the network to influence. We focus on the problem of how to select these seeds such that, when the diffusion process terminates, a competitor can obtain more supports than its opponent. Instead of studying this problem in the game-theoretic framework as in the existing work, in this paper we design several heuristic seed-selection strategies inspired by commonly used centrality measures-Betweenness Centrality (BC), Closeness Centrality (CC), Degree Centrality (DC), Eigenvector Centrality (EC), and K-shell Centrality (KS). We mainly compare three centrality-based strategies, which have better performances in competing with the random selection strategy, through simulations on both real and artificial networks. Even though network structure varies across different networks, we find certain common trend appearing in all of these networks. Roughly speaking, BC-based strategy and DC-based strategy are better than CC-based strategy. Moreover, if a competitor adopts CC-based strategy, then BC-based strategy is a better strategy than DC-based strategy for his opponent, and the superiority of BC-based strategy decreases as the heterogeneity of the network decreases.

  13. Boolean modeling of collective effects in complex networks

    PubMed Central

    Norrell, Johannes; Socolar, Joshua E. S.

    2009-01-01

    Complex systems are often modeled as Boolean networks in attempts to capture their logical structure and reveal its dynamical consequences. Approximating the dynamics of continuous variables by discrete values and Boolean logic gates may, however, introduce dynamical possibilities that are not accessible to the original system. We show that large random networks of variables coupled through continuous transfer functions often fail to exhibit the complex dynamics of corresponding Boolean models in the disordered (chaotic) regime, even when each individual function appears to be a good candidate for Boolean idealization. A suitably modified Boolean theory explains the behavior of systems in which information does not propagate faithfully down certain chains of nodes. Model networks incorporating calculated or directly measured transfer functions reported in the literature on transcriptional regulation of genes are described by the modified theory. PMID:19658525

  14. Activity-Dependent Neuronal Model on Complex Networks

    PubMed Central

    de Arcangelis, Lucilla; Herrmann, Hans J.

    2012-01-01

    Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behavior: these avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems. We present a recent model inspired in self-organized criticality, which consists of an electrical network with threshold firing, refractory period, and activity-dependent synaptic plasticity. The model reproduces the critical behavior of the distribution of avalanche sizes and durations measured experimentally. Moreover, the power spectra of the electrical signal reproduce very robustly the power law behavior found in human electroencephalogram (EEG) spectra. We implement this model on a variety of complex networks, i.e., regular, small-world, and scale-free and verify the robustness of the critical behavior. PMID:22470347

  15. Synchronization in node of complex networks consist of complex chaotic system

    SciTech Connect

    Wei, Qiang; Xie, Cheng-jun; Liu, Hong-jun; Li, Yan-hui

    2014-07-15

    A new synchronization method is investigated for node of complex networks consists of complex chaotic system. When complex networks realize synchronization, different component of complex state variable synchronize up to different scaling complex function by a designed complex feedback controller. This paper change synchronization scaling function from real field to complex field for synchronization in node of complex networks with complex chaotic system. Synchronization in constant delay and time-varying coupling delay complex networks are investigated, respectively. Numerical simulations are provided to show the effectiveness of the proposed method.

  16. Building and measuring a high performance network architecture

    SciTech Connect

    Kramer, William T.C.; Toole, Timothy; Fisher, Chuck; Dugan, Jon; Wheeler, David; Wing, William R; Nickless, William; Goddard, Gregory; Corbato, Steven; Love, E. Paul; Daspit, Paul; Edwards, Hal; Mercer, Linden; Koester, David; Decina, Basil; Dart, Eli; Paul Reisinger, Paul; Kurihara, Riki; Zekauskas, Matthew J; Plesset, Eric; Wulf, Julie; Luce, Douglas; Rogers, James; Duncan, Rex; Mauth, Jeffery

    2001-04-20

    Once a year, the SC conferences present a unique opportunity to create and build one of the most complex and highest performance networks in the world. At SC2000, large-scale and complex local and wide area networking connections were demonstrated, including large-scale distributed applications running on different architectures. This project was designed to use the unique opportunity presented at SC2000 to create a testbed network environment and then use that network to demonstrate and evaluate high performance computational and communication applications. This testbed was designed to incorporate many interoperable systems and services and was designed for measurement from the very beginning. The end results were key insights into how to use novel, high performance networking technologies and to accumulate measurements that will give insights into the networks of the future.

  17. Complex quantum network geometries: Evolution and phase transitions

    NASA Astrophysics Data System (ADS)

    Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

    2015-08-01

    Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.

  18. Phase transitions in Pareto optimal complex networks

    NASA Astrophysics Data System (ADS)

    Seoane, Luís F.; Solé, Ricard

    2015-09-01

    The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem, finding phase transitions of different kinds. Distinct phases are associated with different arrangements of the connections, but the need of drastic topological changes does not determine the presence or the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.

  19. Spreading to localized targets in complex networks

    NASA Astrophysics Data System (ADS)

    Sun, Ye; Ma, Long; Zeng, An; Wang, Wen-Xu

    2016-12-01

    As an important type of dynamics on complex networks, spreading is widely used to model many real processes such as the epidemic contagion and information propagation. One of the most significant research questions in spreading is to rank the spreading ability of nodes in the network. To this end, substantial effort has been made and a variety of effective methods have been proposed. These methods usually define the spreading ability of a node as the number of finally infected nodes given that the spreading is initialized from the node. However, in many real cases such as advertising and news propagation, the spreading only aims to cover a specific group of nodes. Therefore, it is necessary to study the spreading ability of nodes towards localized targets in complex networks. In this paper, we propose a reversed local path algorithm for this problem. Simulation results show that our method outperforms the existing methods in identifying the influential nodes with respect to these localized targets. Moreover, the influential spreaders identified by our method can effectively avoid infecting the non-target nodes in the spreading process.

  20. Spreading to localized targets in complex networks

    PubMed Central

    Sun, Ye; Ma, Long; Zeng, An; Wang, Wen-Xu

    2016-01-01

    As an important type of dynamics on complex networks, spreading is widely used to model many real processes such as the epidemic contagion and information propagation. One of the most significant research questions in spreading is to rank the spreading ability of nodes in the network. To this end, substantial effort has been made and a variety of effective methods have been proposed. These methods usually define the spreading ability of a node as the number of finally infected nodes given that the spreading is initialized from the node. However, in many real cases such as advertising and news propagation, the spreading only aims to cover a specific group of nodes. Therefore, it is necessary to study the spreading ability of nodes towards localized targets in complex networks. In this paper, we propose a reversed local path algorithm for this problem. Simulation results show that our method outperforms the existing methods in identifying the influential nodes with respect to these localized targets. Moreover, the influential spreaders identified by our method can effectively avoid infecting the non-target nodes in the spreading process. PMID:27966613

  1. Hybrid recommendation methods in complex networks.

    PubMed

    Fiasconaro, A; Tumminello, M; Nicosia, V; Latora, V; Mantegna, R N

    2015-07-01

    We propose two recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three data sets, and we compare the performance of our methods to other recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow us to attain an improvement of performances of up to 20% with respect to existing nonparametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a careful choice of the most suitable method is highly relevant for an effective recommendation on a given system. Finally, we study how an increasing presence of random links in the network affects the recommendation scores, finding that one of the two recommendation algorithms introduced here can systematically outperform the others in noisy data sets.

  2. Evaluation method of indoor GPS measurement network

    NASA Astrophysics Data System (ADS)

    Li, Yang; Zhou, Zili; Ma, Liqun; Li, Qian

    2013-01-01

    Indoor GPS measurement network is a space coordinate measuring system, which is composed of more than one transmitter. The number and location of the transmitter determine the measurement range and accuracy of the measurement network. Therefore, how to correctly evaluate the measurement network is a key issue. By analyzing the error model of a measuring system, which is composed of two transmitters, we acquired the main cause of the measurement uncertainty. Through MATLAB simulation, we are able to get the effective measurement conditions, in order to meet specific requirement of measurement uncertainty. Meanwhile, total uncertainty of the measurement network, which is composed of measurement uncertainty, location uncertainty, receiver uncertainty and other uncertainties, is analyzed. We proposed the evaluation method based on the reference length, and at the same time, optimized the position of the reference position, posture and length, in order to meet the evaluation requirements of the entire measurement space. Finally, we simulated the measurement network for aircraft assembly in measurement space of 20m×20m×5m, and the measurement network for car assembly in measurement space of 5m×5m×2m. We evaluated the measurement network according to the above principles and estimated the uncertainty of the measurement network trough measurement bias of reference length at different locations.

  3. Complex network study of Brazilian soccer players

    NASA Astrophysics Data System (ADS)

    Onody, Roberto N.; de Castro, Paulo A.

    2004-09-01

    Although being a very popular sport in many countries, soccer has not received much attention from the scientific community. In this paper, we study soccer from a complex network point of view. First, we consider a bipartite network with two kinds of vertices or nodes: the soccer players and the clubs. Real data were gathered from the 32 editions of the Brazilian soccer championship, in a total of 13411 soccer players and 127 clubs. We find a lot of interesting and perhaps unsuspected results. The probability that a Brazilian soccer player has worked at N clubs or played M games shows an exponential decay while the probability that he has scored G goals is power law. Now, if two soccer players who have worked at the same club at the same time are connected by an edge, then a new type of network arises (composed exclusively by soccer player nodes). Our analysis shows that for this network the degree distribution decays exponentially. We determine the exact values of the clustering coefficient, the assortativity coefficient and the average shortest path length and compare them with those of the Erdös-Rényi and configuration model. The time evolution of these quantities are calculated and the corresponding results discussed.

  4. Cultural dissemination in a complex network

    NASA Astrophysics Data System (ADS)

    Xiao, X. H.; Ye, G. W.; Wang, B.; He, M. F.

    2009-03-01

    In this paper, a complex contact network is constructed as substrate for cultural diffusion. Agents are partitioned into “clusters”, interpreted as sub-cultural groups. Cultural dissemination works both within the cluster and across clusters. To reflect connection intensity, an extra-cluster interaction damping coefficient is defined. The probability of interaction between agents through the intra-cluster contact is higher than through extra-cluster. Dynamical behavior of the cultural diffusion model is studied in this contact network. It is found that there is a critical value of extra-interaction intensity that separates the system from a multicultural to mono-cultural state for low values of the number of vectors (options) per cultural feature (attribute).

  5. Complex networks analysis of obstructive nephropathy data.

    PubMed

    Zanin, M; Boccaletti, S

    2011-09-01

    Congenital obstructive nephropathy (ON) is one of the most frequent nephropathy observed among newborns and children, and the first cause of end-stage renal diseases treated by dialysis or transplantation. This pathology is characterized by the presence of an obstacle in the urinary tract, e.g., stenosis or abnormal implantation of the urethra in the kidney. In spite of important advances, pathological mechanisms are not yet fully understood. In this contribution, the topology of complex networks created upon vectors of features for control and ON subjects is related with the severity of the pathology. Nodes in these networks represent genetic and metabolic profiles, while connections between them indicate an abnormal relation between their expressions. Resulting topologies allow discriminating ON subjects and detecting which genetic or metabolic elements are responsible for the malfunction.

  6. Detection of hierarchies and complex networks in cerebral synchronization patterns

    NASA Astrophysics Data System (ADS)

    Ivanov, Plamen Ch.; Hu, Kun; Dammers, Juergen; Fieseler, Thomas; Tass, Peter A.

    2003-03-01

    We study the synchronization patterns between local current signals obtained from magnetic field tomography at different source points in the human brain during externally and intrinsically driven finger tapping. Based on the estimate of cross-correlation measures we define a ``distance'' between each pair of source points to determine the degree of synchronization in their functional response, and we develop an optimization algorithm to investigate the relation between the functional and physiological structure of the observed complex network of interactions. We identify hierarchies of functional patterns in the network through clusters of source points with different levels of synchronization, and we map these patterns to the anatomical structure of the brain.

  7. From time series to complex networks: the visibility graph.

    PubMed

    Lacasa, Lucas; Luque, Bartolo; Ballesteros, Fernando; Luque, Jordi; Nuño, Juan Carlos

    2008-04-01

    In this work we present a simple and fast computational method, the visibility algorithm, that converts a time series into a graph. The constructed graph inherits several properties of the series in its structure. Thereby, periodic series convert into regular graphs, and random series do so into random graphs. Moreover, fractal series convert into scale-free networks, enhancing the fact that power law degree distributions are related to fractality, something highly discussed recently. Some remarkable examples and analytical tools are outlined to test the method's reliability. Many different measures, recently developed in the complex network theory, could by means of this new approach characterize time series from a new point of view.

  8. Review of Public Safety in Viewpoint of Complex Networks

    NASA Astrophysics Data System (ADS)

    Chengcheng, Gai; Wenguo, Weng; Hongyong, Yuan

    2010-05-01

    In this paper, a brief review of public safety in viewpoint of complex networks is presented. Public safety incidents are divided into four categories: natural disasters, industry accidents, public health and social security, in which the complex network approaches and theories are need. We review how the complex network methods was developed and used in the studies of the three kinds of public safety incidents. The typical public safety incidents studied by the complex network methods in this paper are introduced, including the natural disaster chains, blackouts on electric power grids and epidemic spreading. Finally, we look ahead to the application prospects of the complex network theory on public safety.

  9. Review of Public Safety in Viewpoint of Complex Networks

    SciTech Connect

    Gai Chengcheng; Weng Wenguo; Yuan Hongyong

    2010-05-21

    In this paper, a brief review of public safety in viewpoint of complex networks is presented. Public safety incidents are divided into four categories: natural disasters, industry accidents, public health and social security, in which the complex network approaches and theories are need. We review how the complex network methods was developed and used in the studies of the three kinds of public safety incidents. The typical public safety incidents studied by the complex network methods in this paper are introduced, including the natural disaster chains, blackouts on electric power grids and epidemic spreading. Finally, we look ahead to the application prospects of the complex network theory on public safety.

  10. Intervality and coherence in complex networks.

    PubMed

    Domínguez-García, Virginia; Johnson, Samuel; Muñoz, Miguel A

    2016-06-01

    Food webs-networks of predators and prey-have long been known to exhibit "intervality": species can generally be ordered along a single axis in such a way that the prey of any given predator tend to lie on unbroken compact intervals. Although the meaning of this axis-usually identified with a "niche" dimension-has remained a mystery, it is assumed to lie at the basis of the highly non-trivial structure of food webs. With this in mind, most trophic network modelling has for decades been based on assigning species a niche value by hand. However, we argue here that intervality should not be considered the cause but rather a consequence of food-web structure. First, analysing a set of 46 empirical food webs, we find that they also exhibit predator intervality: the predators of any given species are as likely to be contiguous as the prey are, but in a different ordering. Furthermore, this property is not exclusive of trophic networks: several networks of genes, neurons, metabolites, cellular machines, airports, and words are found to be approximately as interval as food webs. We go on to show that a simple model of food-web assembly which does not make use of a niche axis can nevertheless generate significant intervality. Therefore, the niche dimension (in the sense used for food-web modelling) could in fact be the consequence of other, more fundamental structural traits. We conclude that a new approach to food-web modelling is required for a deeper understanding of ecosystem assembly, structure, and function, and propose that certain topological features thought to be specific of food webs are in fact common to many complex networks.

  11. Intervality and coherence in complex networks

    NASA Astrophysics Data System (ADS)

    Domínguez-García, Virginia; Johnson, Samuel; Muñoz, Miguel A.

    2016-06-01

    Food webs—networks of predators and prey—have long been known to exhibit "intervality": species can generally be ordered along a single axis in such a way that the prey of any given predator tend to lie on unbroken compact intervals. Although the meaning of this axis—usually identified with a "niche" dimension—has remained a mystery, it is assumed to lie at the basis of the highly non-trivial structure of food webs. With this in mind, most trophic network modelling has for decades been based on assigning species a niche value by hand. However, we argue here that intervality should not be considered the cause but rather a consequence of food-web structure. First, analysing a set of 46 empirical food webs, we find that they also exhibit predator intervality: the predators of any given species are as likely to be contiguous as the prey are, but in a different ordering. Furthermore, this property is not exclusive of trophic networks: several networks of genes, neurons, metabolites, cellular machines, airports, and words are found to be approximately as interval as food webs. We go on to show that a simple model of food-web assembly which does not make use of a niche axis can nevertheless generate significant intervality. Therefore, the niche dimension (in the sense used for food-web modelling) could in fact be the consequence of other, more fundamental structural traits. We conclude that a new approach to food-web modelling is required for a deeper understanding of ecosystem assembly, structure, and function, and propose that certain topological features thought to be specific of food webs are in fact common to many complex networks.

  12. Combinatorial Laplacian and entropy of simplicial complexes associated with complex networks

    NASA Astrophysics Data System (ADS)

    Maletić, S.; Rajković, M.

    2012-09-01

    Simplicial complexes represent useful and accurate models of complex networks and complex systems in general. We explore the properties of spectra of combinatorial Laplacian operator of simplicial complexes and show its relationship with connectivity properties of the Q-vector and with connectivities of cliques in the simplicial clique complex. We demonstrate the need for higher order analysis in complex networks and compare the results with ordinary graph spectra. Methods and results are obtained using social network of the Zachary karate club.

  13. Imaging complex nutrient dynamics in mycelial networks.

    PubMed

    Fricker, M D; Lee, J A; Bebber, D P; Tlalka, M; Hynes, J; Darrah, P R; Watkinson, S C; Boddy, L

    2008-08-01

    Transport networks are vital components of multi-cellular organisms, distributing nutrients and removing waste products. Animal cardiovascular and respiratory systems, and plant vasculature, are branching trees whose architecture is thought to determine universal scaling laws in these organisms. In contrast, the transport systems of many multi-cellular fungi do not fit into this conceptual framework, as they have evolved to explore a patchy environment in search of new resources, rather than ramify through a three-dimensional organism. These fungi grow as a foraging mycelium, formed by the branching and fusion of threadlike hyphae, that gives rise to a complex network. To function efficiently, the mycelial network must both transport nutrients between spatially separated source and sink regions and also maintain its integrity in the face of continuous attack by mycophagous insects or random damage. Here we review the development of novel imaging approaches and software tools that we have used to characterise nutrient transport and network formation in foraging mycelia over a range of spatial scales. On a millimetre scale, we have used a combination of time-lapse confocal imaging and fluorescence recovery after photobleaching to quantify the rate of diffusive transport through the unique vacuole system in individual hyphae. These data then form the basis of a simulation model to predict the impact of such diffusion-based movement on a scale of several millimetres. On a centimetre scale, we have used novel photon-counting scintillation imaging techniques to visualize radiolabel movement in small microcosms. This approach has revealed novel N-transport phenomena, including rapid, preferential N-resource allocation to C-rich sinks, induction of simultaneous bi-directional transport, abrupt switching between different pre-existing transport routes, and a strong pulsatile component to transport in some species. Analysis of the pulsatile transport component using Fourier

  14. Complex quantum network model of energy transfer in photosynthetic complexes.

    PubMed

    Ai, Bao-Quan; Zhu, Shi-Liang

    2012-12-01

    The quantum network model with real variables is usually used to describe the excitation energy transfer (EET) in the Fenna-Matthews-Olson (FMO) complexes. In this paper we add the quantum phase factors to the hopping terms and find that the quantum phase factors play an important role in the EET. The quantum phase factors allow us to consider the space structure of the pigments. It is found that phase coherence within the complexes would allow quantum interference to affect the dynamics of the EET. There exist some optimal phase regions where the transfer efficiency takes its maxima, which indicates that when the pigments are optimally spaced, the exciton can pass through the FMO with perfect efficiency. Moreover, the optimal phase regions almost do not change with the environments. In addition, we find that the phase factors are useful in the EET just in the case of multiple pathways. Therefore, we demonstrate that the quantum phases may bring the other two factors, the optimal space of the pigments and multiple pathways, together to contribute the EET in photosynthetic complexes with perfect efficiency.

  15. A system for measuring complex dielectric properties of thin films at submillimeter wavelengths using an open hemispherical cavity and a vector network analyzer

    NASA Astrophysics Data System (ADS)

    Rahman, Rezwanur; Taylor, P. C.; Scales, John A.

    2013-08-01

    Quasi-optical (QO) methods of dielectric spectroscopy are well established in the millimeter and submillimeter frequency bands. These methods exploit standing wave structure in the sample produced by a transmitted Gaussian beam to achieve accurate, low-noise measurement of the complex permittivity of the sample [e.g., J. A. Scales and M. Batzle, Appl. Phys. Lett. 88, 062906 (2006);, 10.1063/1.2172403 R. N. Clarke and C. B. Rosenberg, J. Phys. E 15, 9 (1982);, 10.1088/0022-3735/15/1/002 T. M. Hirovnen, P. Vainikainen, A. Lozowski, and A. V. Raisanen, IEEE Trans. Instrum. Meas. 45, 780 (1996)], 10.1109/19.516996. In effect the sample itself becomes a low-Q cavity. On the other hand, for optically thin samples (films of thickness much less than a wavelength) or extremely low loss samples (loss tangents below 10-5) the QO approach tends to break down due to loss of signal. In such a case it is useful to put the sample in a high-Q cavity and measure the perturbation of the cavity modes. Provided that the average mode frequency divided by the shift in mode frequency is less than the Q (quality factor) of the mode, then the perturbation should be resolvable. Cavity perturbation techniques are not new, but there are technological difficulties in working in the millimeter/submillimeter wave region. In this paper we will show applications of cavity perturbation to the dielectric characterization of semi-conductor thin films of the type used in the manufacture of photovoltaics in the 100 and 350 GHz range. We measured the complex optical constants of hot-wire chemical deposition grown 1-μm thick amorphous silicon (a-Si:H) film on borosilicate glass substrate. The real part of the refractive index and dielectric constant of the glass-substrate varies from frequency-independent to linearly frequency-dependent. We also see power-law behavior of the frequency-dependent optical conductivity from 316 GHz (9.48 cm-1) down to 104 GHz (3.12 cm-1).

  16. A system for measuring complex dielectric properties of thin films at submillimeter wavelengths using an open hemispherical cavity and a vector network analyzer.

    PubMed

    Rahman, Rezwanur; Taylor, P C; Scales, John A

    2013-08-01

    Quasi-optical (QO) methods of dielectric spectroscopy are well established in the millimeter and submillimeter frequency bands. These methods exploit standing wave structure in the sample produced by a transmitted Gaussian beam to achieve accurate, low-noise measurement of the complex permittivity of the sample [e.g., J. A. Scales and M. Batzle, Appl. Phys. Lett. 88, 062906 (2006); R. N. Clarke and C. B. Rosenberg, J. Phys. E 15, 9 (1982); T. M. Hirovnen, P. Vainikainen, A. Lozowski, and A. V. Raisanen, IEEE Trans. Instrum. Meas. 45, 780 (1996)]. In effect the sample itself becomes a low-Q cavity. On the other hand, for optically thin samples (films of thickness much less than a wavelength) or extremely low loss samples (loss tangents below 10(-5)) the QO approach tends to break down due to loss of signal. In such a case it is useful to put the sample in a high-Q cavity and measure the perturbation of the cavity modes. Provided that the average mode frequency divided by the shift in mode frequency is less than the Q (quality factor) of the mode, then the perturbation should be resolvable. Cavity perturbation techniques are not new, but there are technological difficulties in working in the millimeter/submillimeter wave region. In this paper we will show applications of cavity perturbation to the dielectric characterization of semi-conductor thin films of the type used in the manufacture of photovoltaics in the 100 and 350 GHz range. We measured the complex optical constants of hot-wire chemical deposition grown 1-μm thick amorphous silicon (a-Si:H) film on borosilicate glass substrate. The real part of the refractive index and dielectric constant of the glass-substrate varies from frequency-independent to linearly frequency-dependent. We also see power-law behavior of the frequency-dependent optical conductivity from 316 GHz (9.48 cm(-1)) down to 104 GHz (3.12 cm(-1)).

  17. Topological Phenotypes in Complex Leaf Venation Networks

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

    The leaves of vascular plants contain highly complex venation networks consisting of recursively nested, hierarchically organized loops. We analyze the topology of the venation of leaves from ca. 200 species belonging to ca. 10 families, defining topological metrics that quantify the hierarchical nestedness of the network cycles. We find that most of the venation variability can be described by a two dimensional phenotypic space, where one dimension consists of a linear combination of geometrical metrics and the other dimension of topological, previously uncharacterized metrics. We show how this new topological dimension in the phenotypic space significantly improves identification of leaves from fragments, by calculating a ``leaf fingerprint'' from the topology and geometry of the higher order veins. Further, we present a simple model suggesting that the topological phenotypic traits can be explained by noise effects and variations in the timing of higher order vein developmental events. This work opens the path to (a) new quantitative identification techniques for leaves which go beyond simple geometric traits such as vein density and (b) topological quantification of other planar or almost planar networks such as arterial vaculature in the neocortex and lung tissue.

  18. Noncommutative Biology: Sequential Regulation of Complex Networks

    PubMed Central

    Letsou, William; Cai, Long

    2016-01-01

    Single-cell variability in gene expression is important for generating distinct cell types, but it is unclear how cells use the same set of regulatory molecules to specifically control similarly regulated genes. While combinatorial binding of transcription factors at promoters has been proposed as a solution for cell-type specific gene expression, we found that such models resulted in substantial information bottlenecks. We sought to understand the consequences of adopting sequential logic wherein the time-ordering of factors informs the final outcome. We showed that with noncommutative control, it is possible to independently control targets that would otherwise be activated simultaneously using combinatorial logic. Consequently, sequential logic overcomes the information bottleneck inherent in complex networks. We derived scaling laws for two noncommutative models of regulation, motivated by phosphorylation/neural networks and chromosome folding, respectively, and showed that they scale super-exponentially in the number of regulators. We also showed that specificity in control is robust to the loss of a regulator. Lastly, we connected these theoretical results to real biological networks that demonstrate specificity in the context of promiscuity. These results show that achieving a desired outcome often necessitates roundabout steps. PMID:27560383

  19. Wealth distribution on complex networks with losses

    NASA Astrophysics Data System (ADS)

    Wang, Fei; Fang, Weiguo

    2015-07-01

    Wealth distribution plays an important role in the field of econophysics. In this paper, this issue is investigated based on the Bouchaud and Mézard (BM) wealth distribution model by the method of complex networks in this paper. Previous reports usually assumed that wealth increased steadily or was transferred from one agent to another. However, wealth may be lost due to some natural disasters or incidents. Therefore, we introduce terms representing losses in the BM model to describe the economic behavior more precisely. We find that an anti-degree preference helps to create an equitable economic environment. The Gini coefficient of the wealth distribution is calculated, and an optimized preferential parameter is obtained for both the case with losses and the case without losses. For variable values of the parameter, the corresponding results are obtained and discussed. If network topologies are considered, we find that a homogeneous network prompts an equitable economic environment. Simulations prove that losses enlarge the rich-poor gap.

  20. A Complex Network Approach to Stylometry.

    PubMed

    Amancio, Diego Raphael

    2015-01-01

    Statistical methods have been widely employed to study the fundamental properties of language. In recent years, methods from complex and dynamical systems proved useful to create several language models. Despite the large amount of studies devoted to represent texts with physical models, only a limited number of studies have shown how the properties of the underlying physical systems can be employed to improve the performance of natural language processing tasks. In this paper, I address this problem by devising complex networks methods that are able to improve the performance of current statistical methods. Using a fuzzy classification strategy, I show that the topological properties extracted from texts complement the traditional textual description. In several cases, the performance obtained with hybrid approaches outperformed the results obtained when only traditional or networked methods were used. Because the proposed model is generic, the framework devised here could be straightforwardly used to study similar textual applications where the topology plays a pivotal role in the description of the interacting agents.

  1. A Complex Network Approach to Stylometry

    PubMed Central

    Amancio, Diego Raphael

    2015-01-01

    Statistical methods have been widely employed to study the fundamental properties of language. In recent years, methods from complex and dynamical systems proved useful to create several language models. Despite the large amount of studies devoted to represent texts with physical models, only a limited number of studies have shown how the properties of the underlying physical systems can be employed to improve the performance of natural language processing tasks. In this paper, I address this problem by devising complex networks methods that are able to improve the performance of current statistical methods. Using a fuzzy classification strategy, I show that the topological properties extracted from texts complement the traditional textual description. In several cases, the performance obtained with hybrid approaches outperformed the results obtained when only traditional or networked methods were used. Because the proposed model is generic, the framework devised here could be straightforwardly used to study similar textual applications where the topology plays a pivotal role in the description of the interacting agents. PMID:26313921

  2. The new challenges of multiplex networks: Measures and models

    NASA Astrophysics Data System (ADS)

    Battiston, Federico; Nicosia, Vincenzo; Latora, Vito

    2017-02-01

    What do societies, the Internet, and the human brain have in common? They are all examples of complex relational systems, whose emerging behaviours are largely determined by the non-trivial networks of interactions among their constituents, namely individuals, computers, or neurons, rather than only by the properties of the units themselves. In the last two decades, network scientists have proposed models of increasing complexity to better understand real-world systems. Only recently we have realised that multiplexity, i.e. the coexistence of several types of interactions among the constituents of a complex system, is responsible for substantial qualitative and quantitative differences in the type and variety of behaviours that a complex system can exhibit. As a consequence, multilayer and multiplex networks have become a hot topic in complexity science. Here we provide an overview of some of the measures proposed so far to characterise the structure of multiplex networks, and a selection of models aiming at reproducing those structural properties and quantifying their statistical significance. Focusing on a subset of relevant topics, this brief review is a quite comprehensive introduction to the most basic tools for the analysis of multiplex networks observed in the real-world. The wide applicability of multiplex networks as a framework to model complex systems in different fields, from biology to social sciences, and the colloquial tone of the paper will make it an interesting read for researchers working on both theoretical and experimental analysis of networked systems.

  3. A Complex-Valued Projection Neural Network for Constrained Optimization of Real Functions in Complex Variables.

    PubMed

    Zhang, Songchuan; Xia, Youshen; Wang, Jun

    2015-12-01

    In this paper, we present a complex-valued projection neural network for solving constrained convex optimization problems of real functions with complex variables, as an extension of real-valued projection neural networks. Theoretically, by developing results on complex-valued optimization techniques, we prove that the complex-valued projection neural network is globally stable and convergent to the optimal solution. Obtained results are completely established in the complex domain and thus significantly generalize existing results of the real-valued projection neural networks. Numerical simulations are presented to confirm the obtained results and effectiveness of the proposed complex-valued projection neural network.

  4. Complex networks approach to geophysical time series analysis: Detecting paleoclimate transitions via recurrence networks

    NASA Astrophysics Data System (ADS)

    Donner, R. V.; Zou, Y.; Donges, J. F.; Marwan, N.; Kurths, J.

    2009-12-01

    We present a new approach for analysing structural properties of time series from complex systems. Starting from the concept of recurrences in phase space, the recurrence matrix of a time series is interpreted as the adjacency matrix of an associated complex network which links different points in time if the evolution of the considered states is very similar. A critical comparison of these recurrence networks with similar existing techniques is presented, revealing strong conceptual benefits of the new approach which can be considered as a unifying framework for transforming time series into complex networks that also includes other methods as special cases. Based on different model systems, we demonstrate that there are fundamental interrelationships between the topological properties of recurrence networks and the statistical properties of the phase space density of the underlying dynamical system. Hence, the network description yields new quantitative characteristics of the dynamical complexity of a time series, which substantially complement existing measures of recurrence quantification analysis. Finally, we illustrate the potential of our approach for detecting hidden dynamical transitions from geoscientific time series by applying it to different paleoclimate records. In particular, we are able to resolve previously unknown climatic regime shifts in East Africa during the last about 4 million years, which might have had a considerable influence on the evolution of hominids in the area.

  5. Module detection in complex networks using integer optimisation

    PubMed Central

    2010-01-01

    Background The detection of modules or community structure is widely used to reveal the underlying properties of complex networks in biology, as well as physical and social sciences. Since the adoption of modularity as a measure of network topological properties, several methodologies for the discovery of community structure based on modularity maximisation have been developed. However, satisfactory partitions of large graphs with modest computational resources are particularly challenging due to the NP-hard nature of the related optimisation problem. Furthermore, it has been suggested that optimising the modularity metric can reach a resolution limit whereby the algorithm fails to detect smaller communities than a specific size in large networks. Results We present a novel solution approach to identify community structure in large complex networks and address resolution limitations in module detection. The proposed algorithm employs modularity to express network community structure and it is based on mixed integer optimisation models. The solution procedure is extended through an iterative procedure to diminish effects that tend to agglomerate smaller modules (resolution limitations). Conclusions A comprehensive comparative analysis of methodologies for module detection based on modularity maximisation shows that our approach outperforms previously reported methods. Furthermore, in contrast to previous reports, we propose a strategy to handle resolution limitations in modularity maximisation. Overall, we illustrate ways to improve existing methodologies for community structure identification so as to increase its efficiency and applicability. PMID:21073720

  6. Complex Chebyshev-polynomial-based unified model (CCPBUM) neural networks

    NASA Astrophysics Data System (ADS)

    Jeng, Jin-Tsong; Lee, Tsu-Tian

    1998-03-01

    In this paper, we propose complex Chebyshev Polynomial Based unified model neural network for the approximation of complex- valued function. Based on this approximate transformable technique, we have derived the relationship between the single-layered neural network and multi-layered perceptron neural network. It is shown that the complex Chebyshev Polynomial Based unified model neural network can be represented as a functional link network that are based on Chebyshev polynomial. We also derived a new learning algorithm for the proposed network. It turns out that the complex Chebyshev Polynomial Based unified model neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional complex feedforward/recurrent neural network.

  7. Complex Gleason measures and the Nemytsky operator

    NASA Astrophysics Data System (ADS)

    Valles, Miguel A.

    This thesis is devoted to generalize previous results on Gleason measures to complex Gleason measures, and to develop a functional calculus for complex measures in relation to the Nemytsky operator. Furthermore we present the interpretation of our results in the field of quantum mechanics, some concrete examples and further extensions of several theorems.

  8. Dynamical complexity in the C.elegans neural network

    NASA Astrophysics Data System (ADS)

    Antonopoulos, C. G.; Fokas, A. S.; Bountis, T. C.

    2016-09-01

    We model the neuronal circuit of the C.elegans soil worm in terms of a Hindmarsh-Rose system of ordinary differential equations, dividing its circuit into six communities which are determined via the Walktrap and Louvain methods. Using the numerical solution of these equations, we analyze important measures of dynamical complexity, namely synchronicity, the largest Lyapunov exponent, and the ΦAR auto-regressive integrated information theory measure. We show that ΦAR provides a useful measure of the information contained in the C.elegans brain dynamic network. Our analysis reveals that the C.elegans brain dynamic network generates more information than the sum of its constituent parts, and that attains higher levels of integrated information for couplings for which either all its communities are highly synchronized, or there is a mixed state of highly synchronized and desynchronized communities.

  9. Properties of volume-capacity ratio in congested complex networks

    NASA Astrophysics Data System (ADS)

    Zhu, Zhi-Hong; Zheng, Jian-Feng; Gao, Zi-You; Du, Hao-Ming

    2014-04-01

    The volume-capacity ratio (v/c) is one of the most important indexes to measure the congestion of a traffic network. If v/c is very small, the traffic demand is deficient and/or the transportation supply or capacity is surplus, leading to a waste of capacity; on the contrary, a large value of v/c means that the traffic network is seriously congested. This paper investigates the properties of v/c in complex small-world and scale-free networks by introducing the congestion effects, described by link cost functions. The relationship between v/c and the degree of the node is mainly discussed. Finally, a simple strategy is presented to balance the tradeoff between traffic congestion and a waste of capacity.

  10. Characterizing global evolutions of complex systems via intermediate network representations

    NASA Astrophysics Data System (ADS)

    Iwayama, Koji; Hirata, Yoshito; Takahashi, Kohske; Watanabe, Katsumi; Aihara, Kazuyuki; Suzuki, Hideyuki

    2012-05-01

    Recent developments in measurement techniques have enabled us to observe the time series of many components simultaneously. Thus, it is important to understand not only the dynamics of individual time series but also their interactions. Although there are many methods for analysing the interaction between two or more time series, there are very few methods that describe global changes of the interactions over time. Here, we propose an approach to visualise time evolution for the global changes of the interactions in complex systems. This approach consists of two steps. In the first step, we construct a meta-time series of networks. In the second step, we analyse and visualise this meta-time series by using distance and recurrence plots. Our two-step approach involving intermediate network representations elucidates the half-a-day periodicity of foreign exchange markets and a singular functional network in the brain related to perceptual alternations.

  11. Analysis and Reduction of Complex Networks Under Uncertainty.

    SciTech Connect

    Ghanem, Roger G

    2014-07-31

    This effort was a collaboration with Youssef Marzouk of MIT, Omar Knio of Duke University (at the time at Johns Hopkins University) and Habib Najm of Sandia National Laboratories. The objective of this effort was to develop the mathematical and algorithmic capacity to analyze complex networks under uncertainty. Of interest were chemical reaction networks and smart grid networks. The statements of work for USC focused on the development of stochastic reduced models for uncertain networks. The USC team was led by Professor Roger Ghanem and consisted of one graduate student and a postdoc. The contributions completed by the USC team consisted of 1) methodology and algorithms to address the eigenvalue problem, a problem of significance in the stability of networks under stochastic perturbations, 2) methodology and algorithms to characterize probability measures on graph structures with random flows. This is an important problem in characterizing random demand (encountered in smart grid) and random degradation (encountered in infrastructure systems), as well as modeling errors in Markov Chains (with ubiquitous relevance !). 3) methodology and algorithms for treating inequalities in uncertain systems. This is an important problem in the context of models for material failure and network flows under uncertainty where conditions of failure or flow are described in the form of inequalities between the state variables.

  12. The XIOM. Oceanographic measurement network

    NASA Astrophysics Data System (ADS)

    Jerez, F.; Gómez Aguar, J.; Espino, M.; Puigdefàbregas, J.; . Cateura, J.; López, J.

    2009-04-01

    DESCRIPTION The XIOM network for oceanographic and coastal meteorological measurements (Xarxa d'Instrumentació Oceanogràfica I Meteorològica) is owned by the Catalan regional government. His deployment is to better understanding of processes that take place in the Spanish Catalan coast, in the NW Mediterranean. The XIOM sea measurement network is formed by the following equipment: 3 directional buoys. 1 scalar buoy. 4 meteo-oceanographical buoys (providing the currents measurements). 2 tide gauge stations. INSTRUMENTATION Wave buoys sends a HF radio signal to a receiver station at the coast and are equipped with ARGOS allocators to allow recovery in case on drift. The receiver stations area composed by antenna, A/D signal converter and the computer. The signal is processed ant the spectral and statistical parameters are sent through internet connection to the main computer. Meteo-oceanographical buoys sends data by satellite (ORBCOMM system) and it's received by e-mail directly in the main computer. Tidal gauges are locally connected to internet connection and sends the data to a main computer. A vast amount of data is collected. In case of waves the main parameters are Hs (significant wave height) spectral and statistical, Tp (peak period), and mean direction of waves in the peak of spectrum. Another parameters are: Tz (mean period), main directional spread, spectral width, and up to 25 different parameters obtained from spectral moments and statistical calculations. In case of meteo-oceanographical buoys, the parameters are velocity of the current, direction of the current and temperature, all of them at -1 m and -15 m. The buoys are equipped also with a standard meteorological station in its upper part that measures parameters like wind velocity and its direction. Tidal gauges measure sea level and water temperature. DATA FLOW In Xiom network, one ftp server centralizes all the directional and scalar buoy's data. It's located at the UPC (Universitat Polit

  13. Complexity measurement based on information theory and kolmogorov complexity.

    PubMed

    Lui, Leong Ting; Terrazas, Germán; Zenil, Hector; Alexander, Cameron; Krasnogor, Natalio

    2015-01-01

    In the past decades many definitions of complexity have been proposed. Most of these definitions are based either on Shannon's information theory or on Kolmogorov complexity; these two are often compared, but very few studies integrate the two ideas. In this article we introduce a new measure of complexity that builds on both of these theories. As a demonstration of the concept, the technique is applied to elementary cellular automata and simulations of the self-organization of porphyrin molecules.

  14. Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers.

    PubMed

    Liu, Rui; Wang, Xiangdong; Aihara, Kazuyuki; Chen, Luonan

    2014-05-01

    Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high-throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false-positive rates or high false-negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve "real" early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high

  15. Robustness of complex networks with an improved breakdown probability against cascading failures

    NASA Astrophysics Data System (ADS)

    Liu, Jun; Xiong, Qingyu; Shi, Xin; Wang, Kai; Shi, Weiren

    2016-08-01

    The robustness of complex network is a core issue in complex network research. We agree that not all overload nodes will be removed from the network in real networks because some effective measures can be taken to protect them. But only a few researches consider this issue. Based on previous researches, we propose a cascading model with an improved breakdown probability. Different from previous breakdown probability model, the current model brings in some parameters to explore the optimal distribution strategy of the protection resources. Furthermore, we quantify the allocation of the protection resources. We explore the relationship between the parameters of our cascading model and the robustness of three networks (two typical networks and one real network), based on which we find out the optimal value of the parameter. It in turn helps us to quantify the allocation of protection resources and form an optimal protection strategy. Our work may be helpful for improving the robustness of complex networks.

  16. Structural and functional clusters of complex brain networks

    NASA Astrophysics Data System (ADS)

    Zemanová, Lucia; Zhou, Changsong; Kurths, Jürgen

    2006-12-01

    Recent research using the complex network approach has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. It is of importance to understand the implications of such complex network structures in the functional organization of the brain activities. Here we study this problem from the viewpoint of dynamical complex networks. We investigate synchronization dynamics on the corticocortical network of the cat by modeling each node (cortical area) of the network with a sub-network of interacting excitable neurons. We find that the network displays clustered synchronization behavior, and the dynamical clusters coincide with the topological community structures observed in the anatomical network. Our results provide insights into the relationship between the global organization and the functional specialization of the brain cortex.

  17. Reduced-Complexity Models for Network Performance Prediction

    DTIC Science & Technology

    2005-05-01

    traffic over the network . To understand such a complex system it is necessary to develop accurate, yet simple, models to describe the performance...interconnected in complex ways, with millions of users sending traffic over the network . To understand such a complex system, it is necessary to develop...number of downloaders . . . . . . . . . . . . . . . . . 17 11 A network of ISP clouds. In this figure, the ISPs are connected via peering points, denoted

  18. Exceptional reducibility of complex-valued neural networks.

    PubMed

    Kobayashi, Masaki

    2010-07-01

    A neural network is referred to as minimal if it cannot reduce the number of hidden neurons that maintain the input-output map. The condition in which the number of hidden neurons can be reduced is referred to as reducibility. Real-valued neural networks have only three simple types of reducibility. It can be naturally extended to complex-valued neural networks without bias terms of hidden neurons. However, general complex-valued neural networks have another type of reducibility, referred to herein as exceptional reducibility. In this paper, another type of reducibility is presented, and a method by which to minimize complex-valued neural networks is proposed.

  19. Uncertainty quantification for quantum chemical models of complex reaction networks.

    PubMed

    Proppe, Jonny; Husch, Tamara; Simm, Gregor N; Reiher, Markus

    2016-12-22

    For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.

  20. Context dependent preferential attachment model for complex networks

    NASA Astrophysics Data System (ADS)

    Pandey, Pradumn Kumar; Adhikari, Bibhas

    2015-10-01

    In this paper, we propose a growing random complex network model, which we call context dependent preferential attachment model (CDPAM), when the preference of a new node to get attached to old nodes is determined by the local and global property of the old nodes. We consider that local and global properties of a node as the degree and relative average degree of the node respectively. We prove that the degree distribution of complex networks generated by CDPAM follow power law with exponent lies in the interval [2,3] and the expected diameter grows logarithmically with the size of new nodes added to the initial small network. Numerical results show that the expected diameter stabilizes when alike weights to the local and global properties are assigned by the new nodes. Computing various measures including clustering coefficient, assortativity, number of triangles, algebraic connectivity, spectral radius, we show that the proposed model replicates properties of real networks when alike weights are given to local and global property. Finally, we observe that the BA model is a limiting case of CDPAM when new nodes tend to give large weight to the local property compared to the weight given to the global property during link formation.

  1. Consistency of heterogeneous synchronization patterns in complex weighted networks

    NASA Astrophysics Data System (ADS)

    Malagarriga, D.; Villa, A. E. P.; Garcia-Ojalvo, J.; Pons, A. J.

    2017-03-01

    Synchronization within the dynamical nodes of a complex network is usually considered homogeneous through all the nodes. Here we show, in contrast, that subsets of interacting oscillators may synchronize in different ways within a single network. This diversity of synchronization patterns is promoted by increasing the heterogeneous distribution of coupling weights and/or asymmetries in small networks. We also analyze consistency, defined as the persistence of coexistent synchronization patterns regardless of the initial conditions. Our results show that complex weighted networks display richer consistency than regular networks, suggesting why certain functional network topologies are often constructed when experimental data are analyzed.

  2. PREFACE: Complex Networks: from Biology to Information Technology

    NASA Astrophysics Data System (ADS)

    Barrat, A.; Boccaletti, S.; Caldarelli, G.; Chessa, A.; Latora, V.; Motter, A. E.

    2008-06-01

    The field of complex networks is one of the most active areas in contemporary statistical physics. Ten years after seminal work initiated the modern study of networks, interest in the field is in fact still growing, as indicated by the ever increasing number of publications in network science. The reason for such a resounding success is most likely the simplicity and broad significance of the approach that, through graph theory, allows researchers to address a variety of different complex systems within a common framework. This special issue comprises a selection of contributions presented at the workshop 'Complex Networks: from Biology to Information Technology' held in July 2007 in Pula (Cagliari), Italy as a satellite of the general conference STATPHYS23. The contributions cover a wide range of problems that are currently among the most important questions in the area of complex networks and that are likely to stimulate future research. The issue is organised into four sections. The first two sections describe 'methods' to study the structure and the dynamics of complex networks, respectively. After this methodological part, the issue proceeds with a section on applications to biological systems. The issue closes with a section concentrating on applications to the study of social and technological networks. The first section, entitled Methods: The Structure, consists of six contributions focused on the characterisation and analysis of structural properties of complex networks: The paper Motif-based communities in complex networks by Arenas et al is a study of the occurrence of characteristic small subgraphs in complex networks. These subgraphs, known as motifs, are used to define general classes of nodes and their communities by extending the mathematical expression of the Newman-Girvan modularity. The same line of research, aimed at characterising network structure through the analysis of particular subgraphs, is explored by Bianconi and Gulbahce in Algorithm

  3. Analysis of the establishment of the global synchronization in complex networks with different topologies of links

    NASA Astrophysics Data System (ADS)

    Hramov, Alexander E.; Kharchenko, Alexander A.; Khramova, Marina V.; Koronovskii, Alexey A.; Kurovskaya, Maria K.; Makarov, Vladimir V.; Moskalenko, Olga I.; Pavlov, Alexey N.; Dana, Syamal K.

    2016-03-01

    In the present paper the mechanism of the global synchronization onset through the formation of the synchronous clusters in complex networks with different topologies of links (scale-free networks, small-world networks, random networks) is studied. We consider the dependencies of integral characteristics of synchronous dynamics (synchronization measure, number of synchronous clusters, etc) on coupling strength between nodes. As a basic element of the node oscillator we consider Kuramoto phase oscillator.

  4. Identification of hybrid node and link communities in complex networks.

    PubMed

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-02

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  5. Mutual information model for link prediction in heterogeneous complex networks

    PubMed Central

    Shakibian, Hadi; Moghadam Charkari, Nasrollah

    2017-01-01

    Recently, a number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks. However, these indices suffer from two major drawbacks. Firstly, they are primarily dependent on the connectivity degrees of node pairs without considering the further information provided by the given meta-path. Secondly, most of them are required to use a single and usually symmetric meta-path in advance. Hence, employing a set of different meta-paths is not straightforward. To tackle with these problems, we propose a mutual information model for link prediction in heterogeneous complex networks. The proposed model, called as Meta-path based Mutual Information Index (MMI), introduces meta-path based link entropy to estimate the link likelihood and could be carried on a set of available meta-paths. This estimation measures the amount of information through the paths instead of measuring the amount of connectivity between the node pairs. The experimental results on a Bibliography network show that the MMI obtains high prediction accuracy compared with other popular similarity indices. PMID:28344326

  6. Navigation by anomalous random walks on complex networks

    PubMed Central

    Weng, Tongfeng; Zhang, Jie; Khajehnejad, Moein; Small, Michael; Zheng, Rui; Hui, Pan

    2016-01-01

    Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, traditional measurements based on mean first passage time are not useful as they fail to characterize the cost associated with each jump. Here we introduce a new concept of mean first traverse distance (MFTD) to characterize anomalous random walks that represents the expected traverse distance taken by walkers searching from source node to target node, and we provide a procedure for calculating the MFTD between two nodes. We use Lévy walks on networks as an example, and demonstrate that the proposed approach can unravel the interplay between diffusion dynamics of Lévy walks and the underlying network structure. Moreover, applying our framework to the famous PageRank search, we show how to inform the optimality of the PageRank search. The framework for analyzing anomalous random walks on complex networks offers a useful new paradigm to understand the dynamics of anomalous diffusion processes, and provides a unified scheme to characterize search and transport processes on networks. PMID:27876855

  7. Navigation by anomalous random walks on complex networks

    NASA Astrophysics Data System (ADS)

    Weng, Tongfeng; Zhang, Jie; Khajehnejad, Moein; Small, Michael; Zheng, Rui; Hui, Pan

    2016-11-01

    Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, traditional measurements based on mean first passage time are not useful as they fail to characterize the cost associated with each jump. Here we introduce a new concept of mean first traverse distance (MFTD) to characterize anomalous random walks that represents the expected traverse distance taken by walkers searching from source node to target node, and we provide a procedure for calculating the MFTD between two nodes. We use Lévy walks on networks as an example, and demonstrate that the proposed approach can unravel the interplay between diffusion dynamics of Lévy walks and the underlying network structure. Moreover, applying our framework to the famous PageRank search, we show how to inform the optimality of the PageRank search. The framework for analyzing anomalous random walks on complex networks offers a useful new paradigm to understand the dynamics of anomalous diffusion processes, and provides a unified scheme to characterize search and transport processes on networks.

  8. Limits on ecosystem trophic complexity: insights from ecological network analysis.

    PubMed

    Ulanowicz, Robert E; Holt, Robert D; Barfield, Michael

    2014-02-01

    Articulating what limits the length of trophic food chains has remained one of the most enduring challenges in ecology. Mere counts of ecosystem species and transfers have not much illumined the issue, in part because magnitudes of trophic transfers vary by orders of magnitude in power-law fashion. We address this issue by creating a suite of measures that extend the basic indexes usually obtained by counting taxa and transfers so as to apply to networks wherein magnitudes vary by orders of magnitude. Application of the extended measures to data on ecosystem trophic networks reveals that the actual complexity of ecosystem webs is far less than usually imagined, because most ecosystem networks consist of a multitude of weak connections dominated by a relatively few strong flows. Although quantitative ecosystem networks may consist of hundreds of nodes and thousands of transfers, they nevertheless behave similarly to simpler representations of systems with fewer than 14 nodes or 40 flows. Both theory and empirical data point to an upper bound on the number of effective trophic levels at about 3-4 links. We suggest that several whole-system processes may be at play in generating these ecosystem limits and regularities.

  9. The complexity of older adults' social support networks.

    PubMed

    Chaichanawirote, Uraiwan; Higgins, Patricia A

    2013-10-01

    The purpose of this study was to provide a detailed snapshot of the diversity of social support networks of 95 independent-living older adults (mean age = 76). Participants in the convenience sample were recruited from senior centers and a retirement community. Using the Arizona Social Support Interview Schedule and egocentric network analysis, participants' networks are described in terms of patterns, density, size of positive networks (available and utilized), size of negative networks (available and utilized), support need, and support satisfaction. Each participant and the identified members of his or her network were considered a complex adaptive system. Network boundary was 7 members; average network size was 6.22 members (SD = 1.50); network density was moderate (mean = 0.53, SD = 0.33); positive interaction networks were larger than negative networks; and overall, participants reported moderate support need (mean = 2.5, SD = 0.7) and high support satisfaction (mean = 5.9, SD = 1.0).

  10. Pattern recognition tool based on complex network-based approach

    NASA Astrophysics Data System (ADS)

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

    2013-02-01

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

  11. Algorithms and Requirements for Measuring Network Bandwidth

    SciTech Connect

    Jin, Guojun

    2002-12-08

    This report unveils new algorithms for actively measuring (not estimating) available bandwidths with very low intrusion, computing cross traffic, thus estimating the physical bandwidth, provides mathematical proof that the algorithms are accurate, and addresses conditions, requirements, and limitations for new and existing algorithms for measuring network bandwidths. The paper also discusses a number of important terminologies and issues for network bandwidth measurement, and introduces a fundamental parameter -Maximum Burst Size that is critical for implementing algorithms based on multiple packets.

  12. Dynamical properties of transportation on complex networks

    NASA Astrophysics Data System (ADS)

    Shen, Bo; Gao, Zi-You

    2008-02-01

    We study the dynamical properties of transportation considering the topology structure of networks and congestion effects, based on a proposed simple model. We analyze the behavior of the model for finding out the relationship between the properties of transportation and the structure of network. Analysis and numerical results demonstrate that the transition from free flow to congested regime can be observed for both single link load and network load, but it is discontinuous for single link and continuous for network. We also find that networks with large average degree have small average link betweenness and are more tolerant to congestion, and networks with homogeneous structure can hold more vehicles in stationary state at the subcritical region. Furthermore, by allotting capacity with different mode to links, a manner of enhancing the performance of networks is introduced, which should be helpful in the design of traffic networks.

  13. Structure and function of complex brain networks.

    PubMed

    Sporns, Olaf

    2013-09-01

    An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a "rich club," centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed.

  14. Graph theoretical analysis of complex networks in the brain.

    PubMed

    Stam, Cornelis J; Reijneveld, Jaap C

    2007-07-05

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

  15. Testing time series irreversibility using complex network methods

    NASA Astrophysics Data System (ADS)

    Donges, Jonathan F.; Donner, Reik V.; Kurths, Jürgen

    2013-04-01

    The absence of time-reversal symmetry is a fundamental property of many nonlinear time series. Here, we propose a new set of statistical tests for time series irreversibility based on standard and horizontal visibility graphs. Specifically, we statistically compare the distributions of time-directed variants of the common complex network measures degree and local clustering coefficient. Our approach does not involve surrogate data and is applicable to relatively short time series. We demonstrate its performance for paradigmatic model systems with known time-reversal properties as well as for picking up signatures of nonlinearity in neuro-physiological data.

  16. Collective Almost Synchronisation in Complex Networks

    PubMed Central

    Baptista, Murilo S.; Ren, Hai-Peng; Swarts, Johen C. M.; Carareto, Rodrigo; Nijmeijer, Henk; Grebogi, Celso

    2012-01-01

    This work introduces the phenomenon of Collective Almost Synchronisation (CAS), which describes a universal way of how patterns can appear in complex networks for small coupling strengths. The CAS phenomenon appears due to the existence of an approximately constant local mean field and is characterised by having nodes with trajectories evolving around periodic stable orbits. Common notion based on statistical knowledge would lead one to interpret the appearance of a local constant mean field as a consequence of the fact that the behaviour of each node is not correlated to the behaviours of the others. Contrary to this common notion, we show that various well known weaker forms of synchronisation (almost, time-lag, phase synchronisation, and generalised synchronisation) appear as a result of the onset of an almost constant local mean field. If the memory is formed in a brain by minimising the coupling strength among neurons and maximising the number of possible patterns, then the CAS phenomenon is a plausible explanation for it. PMID:23144851

  17. Measuring and Tracking Complexity in Science

    NASA Astrophysics Data System (ADS)

    Marczyk, Jacek; Deshpande, Balachandra

    Recent years have seen the development of a new approach to the study of diverse problems in natural, social and technological fields: the science of complexity [Gell-Man 1994]. The objective of complex systems science is to comprehend how groups of agents, e.g. people, cells, animals, organizations, the economy, function collectively. The underlying concept of complexity science is that any system is an ensemble of agents that interact. As a result, the system exhibits characteristics different from that of each agent, leading to collective behavior [Gell-Man 1994]. This property is known as emergence [Morowitz 2002]. Moreover, complex systems can adapt to changing environments, and are able to spontaneously self-organize [Sornette 2000]. The dynamics of complex system tends to converge to time patterns, that are known as attractors [Sornette 2000] and is strongly influenced by the agent inter-relationships, which can be represented as networks [Barabasi 2002]. The topological properties of such networks are crucial for determining the collective behavior of the systems, with particular reference to their robustness to external perturbations or to agent failure [Barabasi, Albert 2000], [Dorogovtsev 2003]. Although the theoretical exploration of highly complex systems is usually very difficult, the creation of plausible computer models has been made possible in the past 10-15 years. These models yield new insights into how these systems function. Traditionally, such models were studied within the areas of cellular automata [Chopard 1998], neural networks [Haykin 1999] chaos theory [Sornette 2000], control theory [Aguirre 2000], non-linear dynamics [Sornette 2000] and evolutionary programming [Zhou 2003].

  18. Synchronization between two coupled complex networks.

    PubMed

    Li, Changpin; Sun, Weigang; Kurths, Jürgen

    2007-10-01

    We study synchronization for two unidirectionally coupled networks. This is a substantial generalization of several recent papers investigating synchronization inside a network. We derive analytically a criterion for the synchronization of two networks which have the same (inside) topological connectivity. Then numerical examples are given which fit the theoretical analysis. In addition, numerical calculations for two networks with different topological connections are presented and interesting synchronization and desynchronization alternately appear with increasing value of the coupling strength.

  19. Complexities and uncertainties of neuronal network function

    PubMed Central

    Parker, David

    2005-01-01

    The nervous system generates behaviours through the activity in groups of neurons assembled into networks. Understanding these networks is thus essential to our understanding of nervous system function. Understanding a network requires information on its component cells, their interactions and their functional properties. Few networks come close to providing complete information on these aspects. However, even if complete information were available it would still only provide limited insight into network function. This is because the functional and structural properties of a network are not fixed but are plastic and can change over time. The number of interacting network components, their (variable) functional properties, and various plasticity mechanisms endows networks with considerable flexibility, but these features inevitably complicate network analyses. This review will initially discuss the general approaches and problems of network analyses. It will then examine the success of these analyses in a model spinal cord locomotor network in the lamprey, to determine to what extent in this relatively simple vertebrate system it is possible to claim detailed understanding of network function and plasticity. PMID:16553310

  20. Recent Progress in Some Active Topics on Complex Networks

    NASA Astrophysics Data System (ADS)

    Gu, J.; Zhu, Y.; Guo, L.; Jiang, J.; Chi, L.; Li, W.; Wang, Q. A.; Cai, X.

    2015-04-01

    Complex networks have been extensively studied across many fields, especially in interdisciplinary areas. It has since long been recognized that topological structures and dynamics are important aspects for capturing the essence of complex networks. The recent years have also witnessed the emergence of several new elements which play important roles in network study. By combining the results of different research orientations in our group, we provide here a review of the recent advances in regards to spectral graph theory, opinion dynamics, interdependent networks, graph energy theory and temporal networks. We hope this will be helpful for the newcomers of those fields to discover new intriguing topics.

  1. Oscillations in interconnected complex networks under intentional attack

    NASA Astrophysics Data System (ADS)

    Zhang, Wen-Ping; Xia, Yongxiang; Tan, Fei

    2016-01-01

    Many real-world networks are interconnected with each other. In this paper, we study the traffic dynamics in interconnected complex networks under an intentional attack. We find that with the shortest time delay routing strategy, the traffic dynamics can show the stable state, periodic, quasi-periodic and chaotic oscillations, when the capacity redundancy parameter changes. Moreover, compared with isolated complex networks, oscillations always take place in interconnected networks more easily. Thirdly, in interconnected networks, oscillations are affected strongly by the coupling probability and coupling preference.

  2. Instantiating a Global Network Measurement Framework

    SciTech Connect

    Tierney, Brian L.; Boote, Jeff; Boyd, Eric; Brown, Aaron; Grigoriev, Maxim; Metzger, Joe; Swany, Martin; Zekauskas, Matt; Zurawski, Jason

    2008-12-15

    perfSONAR is a web services-based infrastructure for collecting and publishing network performance monitoring. A primary goal of perfSONAR is making it easier to solve end-to-end performance problems on paths crossing several networks. It contains a set of services delivering performance measurements in a federated environment. These services act as an intermediate layer, between the performance measurement tools and the diagnostic or visualization applications. This layer is aimed at making and exchanging performance measurements across multiple networks and multiple user communities, using well-defined protocols. This paper summarizes the key perfSONAR components, and describes how they are deployed by the US-LHC community to monitor the networks distributing LHC data from CERN. All monitoring data described herein is publicly available, and we hope the availability of this data via a standard schema will inspire others to contribute to the effort by building network data analysis applications that use perfSONAR.

  3. Sequential defense against random and intentional attacks in complex networks.

    PubMed

    Chen, Pin-Yu; Cheng, Shin-Ming

    2015-02-01

    Network robustness against attacks is one of the most fundamental researches in network science as it is closely associated with the reliability and functionality of various networking paradigms. However, despite the study on intrinsic topological vulnerabilities to node removals, little is known on the network robustness when network defense mechanisms are implemented, especially for networked engineering systems equipped with detection capabilities. In this paper, a sequential defense mechanism is first proposed in complex networks for attack inference and vulnerability assessment, where the data fusion center sequentially infers the presence of an attack based on the binary attack status reported from the nodes in the network. The network robustness is evaluated in terms of the ability to identify the attack prior to network disruption under two major attack schemes, i.e., random and intentional attacks. We provide a parametric plug-in model for performance evaluation on the proposed mechanism and validate its effectiveness and reliability via canonical complex network models and real-world large-scale network topology. The results show that the sequential defense mechanism greatly improves the network robustness and mitigates the possibility of network disruption by acquiring limited attack status information from a small subset of nodes in the network.

  4. Linear relation on the correlation in complex networks

    NASA Astrophysics Data System (ADS)

    Ma, C. W.; Szeto, K. Y.

    2006-04-01

    Correlation in complex networks follows a linear relation between the degree of a node and the total degrees of its neighbors for six different classes of real networks. This general linear relation is an extension of the Aboav-Weaire law in two-dimensional cellular structures and provides a simple and different perspective on the correlation in complex networks, which is complementary to an existing description using Pearson correlation coefficients and a power law fit. Analytical expression for this linear relation for three standard models of complex networks: the Erdos-Renyi, Watts-Strogatz, and Barabasi-Albert networks is provided. The slope and intercept of this linear relation are described by a single parameter a together with the first and second moment of the degree distribution of the network. The assortivity of the network can be related to the sign of the intercept.

  5. A simple model clarifies the complicated relationships of complex networks

    PubMed Central

    Zheng, Bojin; Wu, Hongrun; Kuang, Li; Qin, Jun; Du, Wenhua; Wang, Jianmin; Li, Deyi

    2014-01-01

    Real-world networks such as the Internet and WWW have many common traits. Until now, hundreds of models were proposed to characterize these traits for understanding the networks. Because different models used very different mechanisms, it is widely believed that these traits origin from different causes. However, we find that a simple model based on optimisation can produce many traits, including scale-free, small-world, ultra small-world, Delta-distribution, compact, fractal, regular and random networks. Moreover, by revising the proposed model, the community-structure networks are generated. By this model and the revised versions, the complicated relationships of complex networks are illustrated. The model brings a new universal perspective to the understanding of complex networks and provide a universal method to model complex networks from the viewpoint of optimisation. PMID:25160506

  6. Complex Active Optical Networks as a New Laser Concept

    NASA Astrophysics Data System (ADS)

    Lepri, Stefano; Trono, Cosimo; Giacomelli, Giovanni

    2017-03-01

    Complex optical networks containing one or more gain sections are investigated, and the evidence of lasing action is reported; the emission spectrum reflects the topological disorder induced by the connections. A theoretical description compares well with the measurements, mapping the networks to directed graphs and showing the analogies with the problem of quantum chaos on graphs. We show that the interplay of chaotic diffusion and amplification leads to an emission statistic with characteristic heavy tails: for different topologies, an unprecedented experimental demonstration of Lévy statistics expected for random lasers is here provided for a continuous-wave pumped system. This result is also supported by a Monte Carlo simulation based on the ray random walk on the graph.

  7. From time series to complex networks: The visibility graph

    PubMed Central

    Lacasa, Lucas; Luque, Bartolo; Ballesteros, Fernando; Luque, Jordi; Nuño, Juan Carlos

    2008-01-01

    In this work we present a simple and fast computational method, the visibility algorithm, that converts a time series into a graph. The constructed graph inherits several properties of the series in its structure. Thereby, periodic series convert into regular graphs, and random series do so into random graphs. Moreover, fractal series convert into scale-free networks, enhancing the fact that power law degree distributions are related to fractality, something highly discussed recently. Some remarkable examples and analytical tools are outlined to test the method's reliability. Many different measures, recently developed in the complex network theory, could by means of this new approach characterize time series from a new point of view. PMID:18362361

  8. Effective distances for epidemics spreading on complex networks

    NASA Astrophysics Data System (ADS)

    Iannelli, Flavio; Koher, Andreas; Brockmann, Dirk; Hövel, Philipp; Sokolov, Igor M.

    2017-01-01

    We show that the recently introduced logarithmic metrics used to predict disease arrival times on complex networks are approximations of more general network-based measures derived from random walks theory. Using the daily air-traffic transportation data we perform numerical experiments to compare the infection arrival time with this alternative metric that is obtained by accounting for multiple walks instead of only the most probable path. The comparison with direct simulations reveals a higher correlation compared to the shortest-path approach used previously. In addition our method allows to connect fundamental observables in epidemic spreading with the cumulant-generating function of the hitting time for a Markov chain. Our results provides a general and computationally efficient approach using only algebraic methods.

  9. One Single Static Measurement Predicts Wave Localization in Complex Structures.

    PubMed

    Lefebvre, Gautier; Gondel, Alexane; Dubois, Marc; Atlan, Michael; Feppon, Florian; Labbé, Aimé; Gillot, Camille; Garelli, Alix; Ernoult, Maxence; Mayboroda, Svitlana; Filoche, Marcel; Sebbah, Patrick

    2016-08-12

    A recent theoretical breakthrough has brought a new tool, called the localization landscape, for predicting the localization regions of vibration modes in complex or disordered systems. Here, we report on the first experiment which measures the localization landscape and demonstrates its predictive power. Holographic measurement of the static deformation under uniform load of a thin plate with complex geometry provides direct access to the landscape function. When put in vibration, this system shows modes precisely confined within the subregions delineated by the landscape function. Also the maxima of this function match the measured eigenfrequencies, while the minima of the valley network gives the frequencies at which modes become extended. This approach fully characterizes the low frequency spectrum of a complex structure from a single static measurement. It paves the way for controlling and engineering eigenmodes in any vibratory system, especially where a structural or microscopic description is not accessible.

  10. Knowledge spillover processes as complex networks

    NASA Astrophysics Data System (ADS)

    Konno, Tomohiko

    2016-11-01

    We introduce the model of knowledge spillover on networks. Knowledge spillover is a major source of economic growth; and is a representative externality in economic phenomena. We show that the model has the following four characteristics: (1) the long-run growth rate is not relevant to the mean degree, but is determined by the mean degree of the nearest neighbors; (2) the productivity level of a firm is proportional to the degree of the firm; (3) the long-run growth rate increases with the increasing heterogeneity of the network; and (4) of three representative networks, the largest growth rate is in scale-free networks and the least in regular networks.

  11. Unification of theoretical approaches for epidemic spreading on complex networks.

    PubMed

    Wang, Wei; Tang, Ming; Eugene Stanley, H; Braunstein, Lidia A

    2017-03-01

    Models of epidemic spreading on complex networks have attracted great attention among researchers in physics, mathematics, and epidemiology due to their success in predicting and controlling scenarios of epidemic spreading in real-world scenarios. To understand the interplay between epidemic spreading and the topology of a contact network, several outstanding theoretical approaches have been developed. An accurate theoretical approach describing the spreading dynamics must take both the network topology and dynamical correlations into consideration at the expense of increasing the complexity of the equations. In this short survey we unify the most widely used theoretical approaches for epidemic spreading on complex networks in terms of increasing complexity, including the mean-field, the heterogeneous mean-field, the quench mean-field, dynamical message-passing, link percolation, and pairwise approximation. We build connections among these approaches to provide new insights into developing an accurate theoretical approach to spreading dynamics on complex networks.

  12. Unification of theoretical approaches for epidemic spreading on complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Wei; Tang, Ming; Stanley, H. Eugene; Braunstein, Lidia A.

    2017-03-01

    Models of epidemic spreading on complex networks have attracted great attention among researchers in physics, mathematics, and epidemiology due to their success in predicting and controlling scenarios of epidemic spreading in real-world scenarios. To understand the interplay between epidemic spreading and the topology of a contact network, several outstanding theoretical approaches have been developed. An accurate theoretical approach describing the spreading dynamics must take both the network topology and dynamical correlations into consideration at the expense of increasing the complexity of the equations. In this short survey we unify the most widely used theoretical approaches for epidemic spreading on complex networks in terms of increasing complexity, including the mean-field, the heterogeneous mean-field, the quench mean-field, dynamical message-passing, link percolation, and pairwise approximation. We build connections among these approaches to provide new insights into developing an accurate theoretical approach to spreading dynamics on complex networks.

  13. Converting PSO dynamics into complex network - Initial study

    NASA Astrophysics Data System (ADS)

    Pluhacek, Michal; Janostik, Jakub; Senkerik, Roman; Zelinka, Ivan

    2016-06-01

    In this paper it is presented the initial study on the possibility of capturing the inner dynamic of Particle Swarm Optimization algorithm into a complex network structure. Inspired in previous works there are two different approaches for creating the complex network presented in this paper. Visualizations of the networks are presented and commented. The possibilities for future applications of the proposed design are given in detail.

  14. On the robustness of complex heterogeneous gene expression networks.

    PubMed

    Gómez-Gardeñes, Jesús; Moreno, Yamir; Floría, Luis M

    2005-04-01

    We analyze a continuous gene expression model on the underlying topology of a complex heterogeneous network. Numerical simulations aimed at studying the chaotic and periodic dynamics of the model are performed. The results clearly indicate that there is a region in which the dynamical and structural complexity of the system avoid chaotic attractors. However, contrary to what has been reported for Random Boolean Networks, the chaotic phase cannot be completely suppressed, which has important bearings on network robustness and gene expression modeling.

  15. Analysis of complex contagions in random multiplex networks

    NASA Astrophysics Data System (ADS)

    Yaǧan, Osman; Gligor, Virgil

    2012-09-01

    We study the diffusion of influence in random multiplex networks where links can be of r different types, and, for a given content (e.g., rumor, product, or political view), each link type is associated with a content-dependent parameter ci in [0,∞] that measures the relative bias type i links have in spreading this content. In this setting, we propose a linear threshold model of contagion where nodes switch state if their “perceived” proportion of active neighbors exceeds a threshold τ. Namely a node connected to mi active neighbors and ki-mi inactive neighbors via type i links will turn active if ∑cimi/∑ciki exceeds its threshold τ. Under this model, we obtain the condition, probability and expected size of global spreading events. Our results extend the existing work on complex contagions in several directions by (i) providing solutions for coupled random networks whose vertices are neither identical nor disjoint, (ii) highlighting the effect of content on the dynamics of complex contagions, and (iii) showing that content-dependent propagation over a multiplex network leads to a subtle relation between the giant vulnerable component of the graph and the global cascade condition that is not seen in the existing models in the literature.

  16. Measurement and Statistics of Application Business in Complex Internet

    NASA Astrophysics Data System (ADS)

    Wang, Lei; Li, Yang; Li, Yipeng; Wu, Shuhang; Song, Shiji; Ren, Yong

    Owing to independent topologies and autonomic routing mechanism, the logical networks formed by Internet application business behavior cause the significant influence on the physical networks. In this paper, the backbone traffic of TUNET (Tsinghua University Networks) is measured, further more, the two most important application business: HTTP and P2P are analyzed at IP-packet level. It is shown that uplink HTTP and P2P packets behavior presents spatio-temporal power-law characteristics with exponents 1.25 and 1.53 respectively. Downlink HTTP packets behavior also presents power-law characteristics, but has more little exponents γ = 0.82 which differs from traditional complex networks research result. Moreover, downlink P2P packets distribution presents an approximate power-law which means that flow equilibrium profits little from distributed peer-to peer mechanism actually.

  17. Temporal node centrality in complex networks

    NASA Astrophysics Data System (ADS)

    Kim, Hyoungshick; Anderson, Ross

    2012-02-01

    Many networks are dynamic in that their topology changes rapidly—on the same time scale as the communications of interest between network nodes. Examples are the human contact networks involved in the transmission of disease, ad hoc radio networks between moving vehicles, and the transactions between principals in a market. While we have good models of static networks, so far these have been lacking for the dynamic case. In this paper we present a simple but powerful model, the time-ordered graph, which reduces a dynamic network to a static network with directed flows. This enables us to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case. We then demonstrate how our model applies to a number of interesting edge cases, such as where the network connectivity depends on a small number of highly mobile vertices or edges, and show that our centrality definition allows us to track the evolution of connectivity. Finally we apply our model and techniques to two real-world dynamic graphs of human contact networks and then discuss the implication of temporal centrality metrics in the real world.

  18. Heterogeneous multidimensional scaling for complex networks

    NASA Astrophysics Data System (ADS)

    Xuan, Qi; Ma, Xiaodi; Fu, Chenbo; Dong, Hui; Zhang, Guijun; Yu, Li

    2015-07-01

    Many real-world networks are essentially heterogeneous, where the nodes have different abilities to gain connections. Such networks are difficult to be embedded into low-dimensional Euclidean space if we ignore the heterogeneity and treat all the nodes equally. In this paper, based on a newly defined heterogeneous distance and a generalized network distance under the constraints of network and triangle inequalities, respectively, we propose a new heterogeneous multidimensional scaling method (HMDS) to embed different networks into proper Euclidean spaces. We find that HMDS behaves much better than the traditional multidimensional scaling method (MDS) in embedding different artificial and real-world networks into Euclidean spaces. Besides, we also propose a method to estimate the appropriate dimensions of Euclidean spaces for different networks, and find that the estimated dimensions are quite close to the real dimensions for those geometrical networks under study. These methods thus can help to better understand the evolution of real-world networks, and have practical importance in network visualization, community detection, link prediction and localization of wireless sensors.

  19. Natural Time Analysis and Complex Networks

    NASA Astrophysics Data System (ADS)

    Sarlis, Nicholas; Skordas, Efthimios; Lazaridou, Mary; Varotsos, Panayiotis

    2013-04-01

    Here, we review the analysis of complex time series in a new time domain, termed natural time, introduced by our group [1,2]. This analysis conforms to the desire to reduce uncertainty and extract signal information as much as possible [3]. It enables [4] the distinction between the two origins of self-similarity when analyzing data from complex systems, i.e., whether self-similarity solely results from long-range temporal correlations (the process's memory only) or solely from the process's increments infinite variance (heavy tails in their distribution). Natural time analysis captures the dynamical evolution of a complex system and identifies [5] when the system enters a critical stage. Hence, this analysis plays a key role in predicting forthcoming catastrophic events in general. Relevant examples, compiled in a recent monograph [6], have been presented in diverse fields, including Solid State Physics [7], Statistical Physics (for example systems exhibiting self-organized criticality [8]), Cardiology [9,10], Earth Sciences [11] (Geophysics, Seismology), Environmental Sciences (e.g. see Ref. [12]), etc. Other groups have proposed and developed a network approach to earthquake events with encouraging results. A recent study [13] reveals that this approach is strengthened if we combine it with natural time analysis. In particular, we find [13,14] that the study of the spatial distribution of the variability [15] of the order parameter fluctuations, defined in natural time, provides important information on the dynamical evolution of the system. 1. P. Varotsos, N. Sarlis, and E. Skordas, Practica of Athens Academy, 76, 294-321, 2001. 2. P.A. Varotsos, N.V. Sarlis, and E.S. Skordas, Phys. Rev. E, 66, 011902 , 2002. 3. S. Abe, N.V. Sarlis, E.S. Skordas, H.K. Tanaka and P.A. Varotsos, Phys. Rev. Lett. 94, 170601, 2005. 4. P.A. Varotsos, N.V. Sarlis, E.S. Skordas, H.K. Tanaka and M.S. Lazaridou, Phys. Rev. E, 74, 021123, 2006. 5. P.Varotsos, N. V. Sarlis, E. S. Skordas

  20. Bypass rewiring and robustness of complex networks

    NASA Astrophysics Data System (ADS)

    Park, Junsang; Hahn, Sang Geun

    2016-08-01

    A concept of bypass rewiring is introduced, and random bypass rewiring is analytically and numerically investigated with simulations. Our results show that bypass rewiring makes networks robust against removal of nodes including random failures and attacks. In particular, random bypass rewiring connects all nodes except the removed nodes on an even degree infinite network and makes the percolation threshold 0 for arbitrary occupation probabilities. In our example, the even degree network is more robust than the original network with random bypass rewiring, while the original network is more robust than the even degree networks without random bypass. We propose a greedy bypass rewiring algorithm which guarantees the maximum size of the largest component at each step, assuming which node will be removed next is unknown. The simulation result shows that the greedy bypass rewiring algorithm improves the robustness of the autonomous system of the Internet under attacks more than random bypass rewiring.

  1. Complex Network Modeling with an Emulab HPC

    DTIC Science & Technology

    2012-09-01

    field. Actual Joint Tactical Radio System (JTRS) radios, Operations Network ( OPNET ) emulations, and GNU (recursive definition for GNU is Not Unix...open-source software-defined-radio software/ firmware/ hardware emulations can be accommodated. Index Terms—network emulation, Emulab, OPNET I...other hand, simulation tools such as MATLAB, Optimized Network Engineering Tools ( OPNET ), NS2, and CORE (a modeling environment from Vitech

  2. Spectral Entropies as Information-Theoretic Tools for Complex Network Comparison

    NASA Astrophysics Data System (ADS)

    De Domenico, Manlio; Biamonte, Jacob

    2016-10-01

    Any physical system can be viewed from the perspective that information is implicitly represented in its state. However, the quantification of this information when it comes to complex networks has remained largely elusive. In this work, we use techniques inspired by quantum statistical mechanics to define an entropy measure for complex networks and to develop a set of information-theoretic tools, based on network spectral properties, such as Rényi q entropy, generalized Kullback-Leibler and Jensen-Shannon divergences, the latter allowing us to define a natural distance measure between complex networks. First, we show that by minimizing the Kullback-Leibler divergence between an observed network and a parametric network model, inference of model parameter(s) by means of maximum-likelihood estimation can be achieved and model selection can be performed with appropriate information criteria. Second, we show that the information-theoretic metric quantifies the distance between pairs of networks and we can use it, for instance, to cluster the layers of a multilayer system. By applying this framework to networks corresponding to sites of the human microbiome, we perform hierarchical cluster analysis and recover with high accuracy existing community-based associations. Our results imply that spectral-based statistical inference in complex networks results in demonstrably superior performance as well as a conceptual backbone, filling a gap towards a network information theory.

  3. Network models. Comment on "Control profiles of complex networks".

    PubMed

    Campbell, Colin; Shea, Katriona; Albert, Réka

    2014-10-31

    Ruths and Ruths (Reports, 21 March 2014, p. 1373) find that existing synthetic random network models fail to generate control profiles that match those found in real network models. Here, we show that a straightforward extension to the Barabási-Albert model allows the control profile to be "tuned" across the control profile space, permitting more meaningful control profile analyses of real networks.

  4. Balance between Noise and Information Flow Maximizes Set Complexity of Network Dynamics

    PubMed Central

    Mäki-Marttunen, Tuomo; Kesseli, Juha; Nykter, Matti

    2013-01-01

    Boolean networks have been used as a discrete model for several biological systems, including metabolic and genetic regulatory networks. Due to their simplicity they offer a firm foundation for generic studies of physical systems. In this work we show, using a measure of context-dependent information, set complexity, that prior to reaching an attractor, random Boolean networks pass through a transient state characterized by high complexity. We justify this finding with a use of another measure of complexity, namely, the statistical complexity. We show that the networks can be tuned to the regime of maximal complexity by adding a suitable amount of noise to the deterministic Boolean dynamics. In fact, we show that for networks with Poisson degree distributions, all networks ranging from subcritical to slightly supercritical can be tuned with noise to reach maximal set complexity in their dynamics. For networks with a fixed number of inputs this is true for near-to-critical networks. This increase in complexity is obtained at the expense of disruption in information flow. For a large ensemble of networks showing maximal complexity, there exists a balance between noise and contracting dynamics in the state space. In networks that are close to critical the intrinsic noise required for the tuning is smaller and thus also has the smallest effect in terms of the information processing in the system. Our results suggest that the maximization of complexity near to the state transition might be a more general phenomenon in physical systems, and that noise present in a system may in fact be useful in retaining the system in a state with high information content. PMID:23516395

  5. Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems.

    PubMed

    Marwan, Norbert; Kurths, Jürgen

    2015-09-01

    We present here two promising techniques for the application of the complex network approach to continuous spatio-temporal systems that have been developed in the last decade and show large potential for future application and development of complex systems analysis. First, we discuss the transforming of a time series from such systems to a complex network. The natural approach is to calculate the recurrence matrix and interpret such as the adjacency matrix of an associated complex network, called recurrence network. Using complex network measures, such as transitivity coefficient, we demonstrate that this approach is very efficient for identifying qualitative transitions in observational data, e.g., when analyzing paleoclimate regime transitions. Second, we demonstrate the use of directed spatial networks constructed from spatio-temporal measurements of such systems that can be derived from the synchronized-in-time occurrence of extreme events in different spatial regions. Although there are many possibilities to investigate such spatial networks, we present here the new measure of network divergence and how it can be used to develop a prediction scheme of extreme rainfall events.

  6. Identifying node importance in complex networks

    NASA Astrophysics Data System (ADS)

    Hu, Ping; Fan, Wenli; Mei, Shengwei

    2015-07-01

    In this paper, we propose a novel node importance evaluation method from the perspective of the existence of mutual dependence among nodes. The node importance comprises its initial importance and the importance contributions from both the adjacent and non-adjacent nodes according to the dependence strength between them. From the simulation analyses on an example network and the ARPA network, we observe that our method can well identify the node importance. Then, the cascading failures on the Netscience and E-mail networks demonstrate that the networks are more vulnerable when continuously removing the important nodes identified by our method, which further proves the accuracy of our method.

  7. An anti-attack model based on complex network theory in P2P networks

    NASA Astrophysics Data System (ADS)

    Peng, Hao; Lu, Songnian; Zhao, Dandan; Zhang, Aixin; Li, Jianhua

    2012-04-01

    Complex network theory is a useful way to study many real systems. In this paper, an anti-attack model based on complex network theory is introduced. The mechanism of this model is based on a dynamic compensation process and a reverse percolation process in P2P networks. The main purpose of the paper is: (i) a dynamic compensation process can turn an attacked P2P network into a power-law (PL) network with exponential cutoff; (ii) a local healing process can restore the maximum degree of peers in an attacked P2P network to a normal level; (iii) a restoring process based on reverse percolation theory connects the fragmentary peers of an attacked P2P network together into a giant connected component. In this way, the model based on complex network theory can be effectively utilized for anti-attack and protection purposes in P2P networks.

  8. Architecture of the Florida power grid as a complex network

    NASA Astrophysics Data System (ADS)

    Xu, Yan; Gurfinkel, Aleks Jacob; Rikvold, Per Arne

    2014-05-01

    We study the Florida high-voltage power grid as a technological network embedded in space. Measurements of geographical lengths of transmission lines, the mixing of generators and loads, the weighted clustering coefficient, as well as the organization of edge conductance weights show a complex architecture quite different from random-graph models usually considered. In particular, we introduce a parametrized mixing matrix to characterize the mixing pattern of generators and loads in the Florida Grid, which is intermediate between the random mixing case and the semi-bipartite case where generator-generator transmission lines are forbidden. Our observations motivate an investigation of optimization (design) principles leading to the structural organization of power grids. We thus propose two network optimization models for the Florida Grid as a case study. Our results show that the Florida Grid is optimized not only by reducing the construction cost (measured by the total length of power lines), but also through reducing the total pairwise edge resistance in the grid, which increases the robustness of power transmission between generators and loads against random line failures. We then embed our models in spatial areas of different aspect ratios and study how this geometric factor affects the network structure, as well as the box-counting fractal dimension of the grids generated by our models.

  9. Identify influential spreaders in complex networks, the role of neighborhood

    NASA Astrophysics Data System (ADS)

    Liu, Ying; Tang, Ming; Zhou, Tao; Do, Younghae

    2016-06-01

    Identifying the most influential spreaders is an important issue in controlling the spreading processes in complex networks. Centrality measures are used to rank node influence in a spreading dynamics. Here we propose a node influence measure based on the centrality of a node and its neighbors' centrality, which we call the neighborhood centrality. By simulating the spreading processes in six real-world networks, we find that the neighborhood centrality greatly outperforms the basic centrality of a node such as the degree and coreness in ranking node influence and identifying the most influential spreaders. Interestingly, we discover a saturation effect in considering the neighborhood of a node, which is not the case of the larger the better. Specifically speaking, considering the 2-step neighborhood of nodes is a good choice that balances the cost and performance. If further step of neighborhood is taken into consideration, there is no obvious improvement and even decrease in the ranking performance. The saturation effect may be informative for studies that make use of the local structure of a node to determine its importance in the network.

  10. Architecture of the Florida Power Grid as a Complex Network

    NASA Astrophysics Data System (ADS)

    Xu, Yan; Gurfinkel, Aleks Jacob; Rikvold, Per Arne

    2014-03-01

    Power grids are the largest engineered systems ever built. Our work presents a simple and self-consistent graph-theoretic analysis of the Florida high-voltage power grid as a technological network embedded in two-dimensional space. We take a new perspective on the mixing patterns of generators and loads in power grids, pointing out that the real grid is usually intermediate between the random mixing and semi-bipartite case (in which generator-generator power transmission lines are disallowed). We propose spatial network models for power grids, which are obtained via a Monte Carlo cooling optimization process. Our results suggest some possible design principles behind the complex architecture of the Florida grid, viz. balancing low construction cost (measured by the total length of transmission lines) and an indispensable redundancy (measured by the clustering coefficient and edge multiplicity) responsible for the robustness of the grid. We also study community structures (modularity) of the real and modeled power-grid networks. Such communities can be electrically separated from each other to limit cascading power failures, a technique known as intentional islanding. Supported by NSF Grant No. DMR-1104829.

  11. Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package.

    PubMed

    Donges, Jonathan F; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V; Marwan, Norbert; Dijkstra, Henk A; Kurths, Jürgen

    2015-11-01

    We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

  12. Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

    NASA Astrophysics Data System (ADS)

    Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen

    2015-11-01

    We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

  13. Decision support systems and methods for complex networks

    DOEpatents

    Huang, Zhenyu [Richland, WA; Wong, Pak Chung [Richland, WA; Ma, Jian [Richland, WA; Mackey, Patrick S [Richland, WA; Chen, Yousu [Richland, WA; Schneider, Kevin P [Seattle, WA

    2012-02-28

    Methods and systems for automated decision support in analyzing operation data from a complex network. Embodiments of the present invention utilize these algorithms and techniques not only to characterize the past and present condition of a complex network, but also to predict future conditions to help operators anticipate deteriorating and/or problem situations. In particular, embodiments of the present invention characterize network conditions from operation data using a state estimator. Contingency scenarios can then be generated based on those network conditions. For at least a portion of all of the contingency scenarios, risk indices are determined that describe the potential impact of each of those scenarios. Contingency scenarios with risk indices are presented visually as graphical representations in the context of a visual representation of the complex network. Analysis of the historical risk indices based on the graphical representations can then provide trends that allow for prediction of future network conditions.

  14. Assessing Low-Intensity Relationships in Complex Networks

    PubMed Central

    Spitz, Andreas; Gimmler, Anna; Stoeck, Thorsten; Zweig, Katharina Anna; Horvát, Emőke-Ágnes

    2016-01-01

    Many large network data sets are noisy and contain links representing low-intensity relationships that are difficult to differentiate from random interactions. This is especially relevant for high-throughput data from systems biology, large-scale ecological data, but also for Web 2.0 data on human interactions. In these networks with missing and spurious links, it is possible to refine the data based on the principle of structural similarity, which assesses the shared neighborhood of two nodes. By using similarity measures to globally rank all possible links and choosing the top-ranked pairs, true links can be validated, missing links inferred, and spurious observations removed. While many similarity measures have been proposed to this end, there is no general consensus on which one to use. In this article, we first contribute a set of benchmarks for complex networks from three different settings (e-commerce, systems biology, and social networks) and thus enable a quantitative performance analysis of classic node similarity measures. Based on this, we then propose a new methodology for link assessment called z* that assesses the statistical significance of the number of their common neighbors by comparison with the expected value in a suitably chosen random graph model and which is a consistently top-performing algorithm for all benchmarks. In addition to a global ranking of links, we also use this method to identify the most similar neighbors of each single node in a local ranking, thereby showing the versatility of the method in two distinct scenarios and augmenting its applicability. Finally, we perform an exploratory analysis on an oceanographic plankton data set and find that the distribution of microbes follows similar biogeographic rules as those of macroorganisms, a result that rejects the global dispersal hypothesis for microbes. PMID:27096435

  15. Assessing Low-Intensity Relationships in Complex Networks.

    PubMed

    Spitz, Andreas; Gimmler, Anna; Stoeck, Thorsten; Zweig, Katharina Anna; Horvát, Emőke-Ágnes

    2016-01-01

    Many large network data sets are noisy and contain links representing low-intensity relationships that are difficult to differentiate from random interactions. This is especially relevant for high-throughput data from systems biology, large-scale ecological data, but also for Web 2.0 data on human interactions. In these networks with missing and spurious links, it is possible to refine the data based on the principle of structural similarity, which assesses the shared neighborhood of two nodes. By using similarity measures to globally rank all possible links and choosing the top-ranked pairs, true links can be validated, missing links inferred, and spurious observations removed. While many similarity measures have been proposed to this end, there is no general consensus on which one to use. In this article, we first contribute a set of benchmarks for complex networks from three different settings (e-commerce, systems biology, and social networks) and thus enable a quantitative performance analysis of classic node similarity measures. Based on this, we then propose a new methodology for link assessment called z* that assesses the statistical significance of the number of their common neighbors by comparison with the expected value in a suitably chosen random graph model and which is a consistently top-performing algorithm for all benchmarks. In addition to a global ranking of links, we also use this method to identify the most similar neighbors of each single node in a local ranking, thereby showing the versatility of the method in two distinct scenarios and augmenting its applicability. Finally, we perform an exploratory analysis on an oceanographic plankton data set and find that the distribution of microbes follows similar biogeographic rules as those of macroorganisms, a result that rejects the global dispersal hypothesis for microbes.

  16. Using Complex Networks to Characterize International Business Cycles

    PubMed Central

    Caraiani, Petre

    2013-01-01

    Background There is a rapidly expanding literature on the application of complex networks in economics that focused mostly on stock markets. In this paper, we discuss an application of complex networks to study international business cycles. Methodology/Principal Findings We construct complex networks based on GDP data from two data sets on G7 and OECD economies. Besides the well-known correlation-based networks, we also use a specific tool for presenting causality in economics, the Granger causality. We consider different filtering methods to derive the stationary component of the GDP series for each of the countries in the samples. The networks were found to be sensitive to the detrending method. While the correlation networks provide information on comovement between the national economies, the Granger causality networks can better predict fluctuations in countries’ GDP. By using them, we can obtain directed networks allows us to determine the relative influence of different countries on the global economy network. The US appears as the key player for both the G7 and OECD samples. Conclusion The use of complex networks is valuable for understanding the business cycle comovements at an international level. PMID:23483979

  17. Optimal structure of complex networks for minimizing traffic congestion.

    PubMed

    Zhao, Liang; Cupertino, Thiago Henrique; Park, Kwangho; Lai, Ying-Cheng; Jin, Xiaogang

    2007-12-01

    To design complex networks to minimize traffic congestion, it is necessary to understand how traffic flow depends on network structure. We study data packet flow on complex networks, where the packet delivery capacity of each node is not fixed. The optimal configuration of capacities to minimize traffic congestion is derived and the critical packet generating rate is determined, below which the network is at a free flow state but above which congestion occurs. Our analysis reveals a direct relation between network topology and traffic flow. Optimal network structure, free of traffic congestion, should have two features: uniform distribution of load over all nodes and small network diameter. This finding is confirmed by numerical simulations. Our analysis also makes it possible to theoretically compare the congestion conditions for different types of complex networks. In particular, we find that network with low critical generating rate is more susceptible to congestion. The comparison has been made on the following complex-network topologies: random, scale-free, and regular.

  18. Structure and function of complex brain networks

    PubMed Central

    Sporns, Olaf

    2013-01-01

    An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a “rich club,” centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed. PMID:24174898

  19. A complex network representation of wind flows

    NASA Astrophysics Data System (ADS)

    Gelbrecht, Maximilian; Boers, Niklas; Kurths, Jürgen

    2017-03-01

    Climate networks have proven to be a valuable method to investigate spatial connectivity patterns of the climate system. However, so far such networks have mostly been applied to scalar observables. In this study, we propose a new method for constructing networks from atmospheric wind fields on two-dimensional isobaric surfaces. By connecting nodes along a spatial environment based on the local wind flow, we derive a network representation of the low-level circulation that captures its most important characteristics. In our approach, network links are placed according to a suitable statistical null model that takes into account the direction and magnitude of the flow. We compare a simulation-based (numerically costly) and a semi-analytical (numerically cheaper) approach to determine the statistical significance of possible connections, and find that both methods yield qualitatively similar results. As an application, we choose the regional climate system of South America and focus on the monsoon season in austral summer. Monsoon systems are generally characterized by substantial changes in the large-scale wind directions, and therefore provide ideal applications for the proposed wind networks. Based on these networks, we are able to reveal the key features of the low-level circulation of the South American Monsoon System, including the South American Low-Level Jet. Networks of the dry and the wet season are compared with each other and their differences are consistent with the literature on South American climate.

  20. A CAD system based on complex networks theory to characterize mass in mammograms

    NASA Astrophysics Data System (ADS)

    Watanabe, Carolina Y. V.; Ramos, Jonathan S.; Traina, Agma J. M.; Traina, Caetano, Jr.

    2012-03-01

    This paper presents a Computer-Aided Diagnosis (CAD) system for mammograms, which is based on complex networks to shape boundary characterization of mass in mammograms, suggesting a "second opinion" to the health specialist. A region of interest (the mass) is automatically segmented using an improved algorithm based on EM/MPM and the shape is modeled into a scale-free complex network. Topological measurements of the resulting network are used to compose the shape descriptors. The experiments comparing the complex network approach with other traditional descriptors, in detecting breast cancer in mammograms, show that the proposed approach accomplish the best values of accuracy. Hence, the results indicate that complex networks are wellsuited to characterize mammograms.

  1. Quantum complexity: Quantum mutual information, complex networks, and emergent phenomena in quantum cellular automata

    NASA Astrophysics Data System (ADS)

    Vargas, David L.

    Emerging quantum simulator technologies provide a new challenge to quantum many body theory. Quantifying the emergent order in and predicting the dynamics of such complex quantum systems requires a new approach. We develop such an approach based on complex network analysis of quantum mutual information. First, we establish the usefulness of quantum mutual information complex networks by reproducing the phase diagrams of transverse Ising and Bose-Hubbard models. By quantifying the complexity of quantum cellular automata we then demonstrate the applicability of complex network theory to non-equilibrium quantum dynamics. We conclude with a study of student collaboration networks, correlating a student's role in a collaboration network with their grades. This work thus initiates a quantitative theory of quantum complexity and provides a new tool for physics education research. (Abstract shortened by ProQuest.).

  2. Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network.

    PubMed

    Al-Harazi, Olfat; Al Insaif, Sadiq; Al-Ajlan, Monirah A; Kaya, Namik; Dzimiri, Nduna; Colak, Dilek

    2016-06-20

    A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

  3. Structural Interrelationship in the Ecosystem Network Using Method of Complex Networks

    NASA Astrophysics Data System (ADS)

    Petrova, I.; Loew, A.

    2012-04-01

    Complex Networks have been recently successfully applied to problems in climate science. They have been used as an alternative method to reveal persistent structural features in the climate system based on observed and model simulated fields of temperature, precipitation and other meteorological fields. CCN provide information on the topology, dynamics and stability characteristic features in the climate system and help to e.g. identify regions with large importance for teleconnections. The present paper uses climate networks to analyze results from the Earth System Model of the Max-Planck-Institute for Meteorology, conducted in the frame of the Coupled Model Intercomparison Project Phase 5 (CMIP5). By analyzing local and global measures such as centralities and link distance the climate and especially terrestrial teleconnection patterns are revealed and investigated. To construct the network the 30- year time-series (1979-2009) of evaporation flux, gross and net primary production were retrieved from the two experimental setups of the MPI-ESM using either a full coupled climate model (ocean, atmosphere, land) or prescribed sea surface temperature fields (atmosphere, land only). The major teleconnection patterns discovered were associated with climate related energy information flow and material cycling functionality within the Earth system. Non-local spatial linkages to the main teleconnection patterns, like NAO and ENSO were analyzed, as well as spatial-temporal structures obtained by the community detection method were established. An outlook of using complex networks as an alternative tool for the evaluation of coupled Earth System models will be given.

  4. Analysis of complex network performance and heuristic node removal strategies

    NASA Astrophysics Data System (ADS)

    Jahanpour, Ehsan; Chen, Xin

    2013-12-01

    Removing important nodes from complex networks is a great challenge in fighting against criminal organizations and preventing disease outbreaks. Six network performance metrics, including four new metrics, are applied to quantify networks' diffusion speed, diffusion scale, homogeneity, and diameter. In order to efficiently identify nodes whose removal maximally destroys a network, i.e., minimizes network performance, ten structured heuristic node removal strategies are designed using different node centrality metrics including degree, betweenness, reciprocal closeness, complement-derived closeness, and eigenvector centrality. These strategies are applied to remove nodes from the September 11, 2001 hijackers' network, and their performance are compared to that of a random strategy, which removes randomly selected nodes, and the locally optimal solution (LOS), which removes nodes to minimize network performance at each step. The computational complexity of the 11 strategies and LOS is also analyzed. Results show that the node removal strategies using degree and betweenness centralities are more efficient than other strategies.

  5. Complex networks from lévy noise

    NASA Astrophysics Data System (ADS)

    Zhang, J.-Fang; Shao, Z.-Gang

    2011-09-01

    We construct complex networks from lévy noise (LN) using visibility algorithm proposed by Lucas lacasa el al. It is found that as the stability index α of the symmetric LN decreases, the corresponding complex network will transit from exponential network to long-tailed-degree-distribution one, and then to Gaussian one. The associated network for symmetric LN is the high clustering, hierarchy, and 18 community network. The properties of the associated networks for asymmetric LN except the skewness parameter β = -1 are similar with that for symmetric one. The associated network for the asymmetric LN with the skewness parameter β = -1 is always the exponential, high clustering, and hierarchy one with small k-clique communities.

  6. Entropic origin of disassortativity in complex networks.

    PubMed

    Johnson, Samuel; Torres, Joaquín J; Marro, J; Muñoz, Miguel A

    2010-03-12

    Why are most empirical networks, with the prominent exception of social ones, generically degree-degree anticorrelated? To answer this long-standing question, we define the ensemble of correlated networks and obtain the associated Shannon entropy. Maximum entropy can correspond to either assortative (correlated) or disassortative (anticorrelated) configurations, but in the case of highly heterogeneous, scale-free networks a certain disassortativity is predicted--offering a parsimonious explanation for the question above. Our approach provides a neutral model from which, in the absence of further knowledge regarding network evolution, one can obtain the expected value of correlations. When empirical observations deviate from the neutral predictions--as happens for social networks--one can then infer that there are specific correlating mechanisms at work.

  7. Enhancing Traffic Capacity of Two-Layer Complex Networks

    NASA Astrophysics Data System (ADS)

    Jiang, Zhong-Yuan; Liang, Man-Gui; Zhang, Shuai; Zhou, Weixing; Jin, Huiqin

    2013-08-01

    As two-layer or multi-layer network model can more accurately reveal many real structures of complex systems such as peer-to-peer (P2P) networks on IP networks, to better understand the traffic dynamics and improve the network traffic capacity, we propose to efficiently construct the structure of upper logical layer network which can be possibly implemented. From the beginning, we assume that the logical layer network has the same structure as the lower physical layer network, and then we use link-removal strategy in which a fraction of links with maximal product (ki* kj) are removed from the logical layer, where ki and kj are the degrees of node i and node j, respectively. Traffic load is strongly redistributed from center nodes to noncenter nodes. The traffic capacity of whole complex system is enhanced several times at the expense of a little average path lengthening. In two-layer network model, the physical layer network structure is unchanged and the shortest path routing strategy is used. The structure of upper layer network can been constructed freely under our own methods. This mechanism can be employed in many real complex systems to improve the network traffic capacity.

  8. Hierarchical organization unveiled by functional connectivity in complex brain networks.

    PubMed

    Zhou, Changsong; Zemanová, Lucia; Zamora, Gorka; Hilgetag, Claus C; Kurths, Jürgen

    2006-12-08

    How do diverse dynamical patterns arise from the topology of complex networks? We study synchronization dynamics in the cortical brain network of the cat, which displays a hierarchically clustered organization, by modeling each node (cortical area) with a subnetwork of interacting excitable neurons. We find that in the biologically plausible regime the dynamics exhibits a hierarchical modular organization, in particular, revealing functional clusters coinciding with the anatomical communities at different scales. Our results provide insights into the relationship between network topology and functional organization of complex brain networks.

  9. Toward link predictability of complex networks

    PubMed Central

    Lü, Linyuan; Pan, Liming; Zhou, Tao; Zhang, Yi-Cheng; Stanley, H. Eugene

    2015-01-01

    The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners. PMID:25659742

  10. Toward link predictability of complex networks.

    PubMed

    Lü, Linyuan; Pan, Liming; Zhou, Tao; Zhang, Yi-Cheng; Stanley, H Eugene

    2015-02-24

    The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners.

  11. Immunizing complex networks with limited budget

    NASA Astrophysics Data System (ADS)

    Mirzasoleiman, Baharan; Babaei, Mahmoudreza; Jalili, Mahdi

    2012-05-01

    In this letter we studied the epidemic spreading on scale-free networks assuming a limited budget for immunization. We proposed a general model in which the immunity of an individual against the disease depends on its immunized friends in the network. Furthermore, we considered the possibility that each individual might be eager to pay a price to buy the vaccine and become immune against the disease. Under these assumptions we proposed an algorithm for improving the performance of all previous immunization algorithms. We also introduced a heuristic extension of the algorithm, which works well in scale-free networks.

  12. Inhomogeneity induces relay synchronization in complex networks

    NASA Astrophysics Data System (ADS)

    Gambuzza, Lucia Valentina; Frasca, Mattia; Fortuna, Luigi; Boccaletti, Stefano

    2016-04-01

    Relay synchronization is a collective state, originally found in chains of interacting oscillators, in which uncoupled dynamical units synchronize through the action of mismatched inner nodes that relay the information but do not synchronize with them. It is demonstrated herein that relay synchronization is not limited to such simple motifs, rather it can emerge in larger and arbitrary network topologies. In particular, we show how this phenomenon can be observed in networks of chaotic systems in the presence of some mismatched units, the relay nodes, and how it is actually responsible for an enhancement of synchronization in the network.

  13. Inhomogeneity induces relay synchronization in complex networks.

    PubMed

    Gambuzza, Lucia Valentina; Frasca, Mattia; Fortuna, Luigi; Boccaletti, Stefano

    2016-04-01

    Relay synchronization is a collective state, originally found in chains of interacting oscillators, in which uncoupled dynamical units synchronize through the action of mismatched inner nodes that relay the information but do not synchronize with them. It is demonstrated herein that relay synchronization is not limited to such simple motifs, rather it can emerge in larger and arbitrary network topologies. In particular, we show how this phenomenon can be observed in networks of chaotic systems in the presence of some mismatched units, the relay nodes, and how it is actually responsible for an enhancement of synchronization in the network.

  14. Wind turbine wake measurement in complex terrain

    NASA Astrophysics Data System (ADS)

    Hansen, KS; Larsen, GC; Menke, R.; Vasiljevic, N.; Angelou, N.; Feng, J.; Zhu, WJ; Vignaroli, A.; W, W. Liu; Xu, C.; Shen, WZ

    2016-09-01

    SCADA data from a wind farm and high frequency time series measurements obtained with remote scanning systems have been analysed with focus on identification of wind turbine wake properties in complex terrain. The analysis indicates that within the flow regime characterized by medium to large downstream distances (more than 5 diameters) from the wake generating turbine, the wake changes according to local atmospheric conditions e.g. vertical wind speed. In very complex terrain the wake effects are often “overruled” by distortion effects due to the terrain complexity or topology.

  15. Locating influential nodes in complex networks

    NASA Astrophysics Data System (ADS)

    Malliaros, Fragkiskos D.; Rossi, Maria-Evgenia G.; Vazirgiannis, Michalis

    2016-01-01

    Understanding and controlling spreading processes in networks is an important topic with many diverse applications, including information dissemination, disease propagation and viral marketing. It is of crucial importance to identify which entities act as influential spreaders that can propagate information to a large portion of the network, in order to ensure efficient information diffusion, optimize available resources or even control the spreading. In this work, we capitalize on the properties of the K-truss decomposition, a triangle-based extension of the core decomposition of graphs, to locate individual influential nodes. Our analysis on real networks indicates that the nodes belonging to the maximal K-truss subgraph show better spreading behavior compared to previously used importance criteria, including node degree and k-core index, leading to faster and wider epidemic spreading. We further show that nodes belonging to such dense subgraphs, dominate the small set of nodes that achieve the optimal spreading in the network.

  16. Data reliability in complex directed networks

    NASA Astrophysics Data System (ADS)

    Sanz, Joaquín; Cozzo, Emanuele; Moreno, Yamir

    2013-12-01

    The availability of data from many different sources and fields of science has made it possible to map out an increasing number of networks of contacts and interactions. However, quantifying how reliable these data are remains an open problem. From Biology to Sociology and Economics, the identification of false and missing positives has become a problem that calls for a solution. In this work we extend one of the newest, best performing models—due to Guimerá and Sales-Pardo in 2009—to directed networks. The new methodology is able to identify missing and spurious directed interactions with more precision than previous approaches, which renders it particularly useful for analyzing data reliability in systems like trophic webs, gene regulatory networks, communication patterns and several social systems. We also show, using real-world networks, how the method can be employed to help search for new interactions in an efficient way.

  17. Evolutionary vaccination dilemma in complex networks.

    PubMed

    Cardillo, Alessio; Reyes-Suárez, Catalina; Naranjo, Fernando; Gómez-Gardeñes, Jesús

    2013-09-01

    In this work we analyze the evolution of voluntary vaccination in networked populations by entangling the spreading dynamics of an influenza-like disease with an evolutionary framework taking place at the end of each influenza season so that individuals take or do not take the vaccine upon their previous experience. Our framework thus puts in competition two well-known dynamical properties of scale-free networks: the fast propagation of diseases and the promotion of cooperative behaviors. Our results show that when vaccine is perfect, scale-free networks enhance the vaccination behavior with respect to random graphs with homogeneous connectivity patterns. However, when imperfection appears we find a crossover effect so that the number of infected (vaccinated) individuals increases (decreases) with respect to homogeneous networks, thus showing the competition between the aforementioned properties of scale-free graphs.

  18. Locating influential nodes in complex networks

    PubMed Central

    Malliaros, Fragkiskos D.; Rossi, Maria-Evgenia G.; Vazirgiannis, Michalis

    2016-01-01

    Understanding and controlling spreading processes in networks is an important topic with many diverse applications, including information dissemination, disease propagation and viral marketing. It is of crucial importance to identify which entities act as influential spreaders that can propagate information to a large portion of the network, in order to ensure efficient information diffusion, optimize available resources or even control the spreading. In this work, we capitalize on the properties of the K-truss decomposition, a triangle-based extension of the core decomposition of graphs, to locate individual influential nodes. Our analysis on real networks indicates that the nodes belonging to the maximal K-truss subgraph show better spreading behavior compared to previously used importance criteria, including node degree and k-core index, leading to faster and wider epidemic spreading. We further show that nodes belonging to such dense subgraphs, dominate the small set of nodes that achieve the optimal spreading in the network. PMID:26776455

  19. Evolutionary vaccination dilemma in complex networks

    NASA Astrophysics Data System (ADS)

    Cardillo, Alessio; Reyes-Suárez, Catalina; Naranjo, Fernando; Gómez-Gardeñes, Jesús

    2013-09-01

    In this work we analyze the evolution of voluntary vaccination in networked populations by entangling the spreading dynamics of an influenza-like disease with an evolutionary framework taking place at the end of each influenza season so that individuals take or do not take the vaccine upon their previous experience. Our framework thus puts in competition two well-known dynamical properties of scale-free networks: the fast propagation of diseases and the promotion of cooperative behaviors. Our results show that when vaccine is perfect, scale-free networks enhance the vaccination behavior with respect to random graphs with homogeneous connectivity patterns. However, when imperfection appears we find a crossover effect so that the number of infected (vaccinated) individuals increases (decreases) with respect to homogeneous networks, thus showing the competition between the aforementioned properties of scale-free graphs.

  20. Complex quantum networks as structured environments: engineering and probing

    NASA Astrophysics Data System (ADS)

    Nokkala, Johannes; Galve, Fernando; Zambrini, Roberta; Maniscalco, Sabrina; Piilo, Jyrki

    2016-05-01

    We consider structured environments modeled by bosonic quantum networks and investigate the probing of their spectral density, structure, and topology. We demonstrate how to engineer a desired spectral density by changing the network structure. Our results show that the spectral density can be very accurately detected via a locally immersed quantum probe for virtually any network configuration. Moreover, we show how the entire network structure can be reconstructed by using a single quantum probe. We illustrate our findings presenting examples of spectral densities and topology probing for networks of genuine complexity.

  1. Introduction to Focus Issue: synchronization in complex networks.

    PubMed

    Suykens, Johan A K; Osipov, Grigory V

    2008-09-01

    Synchronization in large ensembles of coupled interacting units is a fundamental phenomenon relevant for the understanding of working mechanisms in neuronal networks, genetic networks, coupled electrical and laser networks, coupled mechanical systems, networks in social sciences, and others. It relates to mathematical and computational analysis of the existence of different states and its stability, clustering, bifurcations and chaos, robustness and sensitivity analysis, etc., at the intersection between synchronization and pattern formation in complex networks. This interdisciplinary oriented Focus Issue presents recent progress in this area with contributions on generic methods, specific model studies, and applications.

  2. Complex quantum networks as structured environments: engineering and probing

    PubMed Central

    Nokkala, Johannes; Galve, Fernando; Zambrini, Roberta; Maniscalco, Sabrina; Piilo, Jyrki

    2016-01-01

    We consider structured environments modeled by bosonic quantum networks and investigate the probing of their spectral density, structure, and topology. We demonstrate how to engineer a desired spectral density by changing the network structure. Our results show that the spectral density can be very accurately detected via a locally immersed quantum probe for virtually any network configuration. Moreover, we show how the entire network structure can be reconstructed by using a single quantum probe. We illustrate our findings presenting examples of spectral densities and topology probing for networks of genuine complexity. PMID:27230125

  3. Linguistic complex networks: Rationale, application, interpretation, and directions. Reply to comments on "Approaching human language with complex networks"

    NASA Astrophysics Data System (ADS)

    Cong, Jin; Liu, Haitao

    2014-12-01

    Amid the enthusiasm for real-world networks of the new millennium, the enquiry into linguistic networks is flourishing not only as a productive branch of the new networks science but also as a promising approach to linguistic research. Although the complex network approach constitutes a potential opportunity to make linguistics a science, the world of linguistics seems unprepared to embrace it. For one thing, linguistics has been largely unaffected by quantitative methods. Those who are accustomed to qualitative linguistic methods may find it hard to appreciate the application of quantitative properties of language such as frequency and length, not to mention quantitative properties of language modeled as networks. With this in mind, in our review [1] we restrict ourselves to the basics of complex networks and the new insights into human language with the application of complex networks. For another, while breaking new grounds and posing new challenges for linguistics, the complex network approach to human language as a new tradition of linguistic research is faced with challenges and unsolved issues of its own. It is no surprise that the comments on our review, especially their skepticism and suggestions, focus on various different aspects of the complex network approach to human language. We are grateful to all the insightful and penetrating comments, which, together with our review, mark a significant impetus to linguistic research from the complex network approach. In this reply, we would like to address four major issues of the complex network approach to human language, namely, a) its theoretical rationale, b) its application in linguistic research, c) interpretation of the results, and d) directions of future research.

  4. The guitar chord-generating algorithm based on complex network

    NASA Astrophysics Data System (ADS)

    Ren, Tao; Wang, Yi-fan; Du, Dan; Liu, Miao-miao; Siddiqi, Awais

    2016-02-01

    This paper aims to generate chords for popular songs automatically based on complex network. Firstly, according to the characteristics of guitar tablature, six chord networks of popular songs by six pop singers are constructed and the properties of all networks are concluded. By analyzing the diverse chord networks, the accompaniment regulations and features are shown, with which the chords can be generated automatically. Secondly, in terms of the characteristics of popular songs, a two-tiered network containing a verse network and a chorus network is constructed. With this network, the verse and chorus can be composed respectively with the random walk algorithm. Thirdly, the musical motif is considered for generating chords, with which the bad chord progressions can be revised. This method can make the accompaniments sound more melodious. Finally, a popular song is chosen for generating chords and the new generated accompaniment sounds better than those done by the composers.

  5. Nonparametric Sparsification of Complex Multiscale Networks

    PubMed Central

    Foti, Nicholas J.; Hughes, James M.; Rockmore, Daniel N.

    2011-01-01

    Many real-world networks tend to be very dense. Particular examples of interest arise in the construction of networks that represent pairwise similarities between objects. In these cases, the networks under consideration are weighted, generally with positive weights between any two nodes. Visualization and analysis of such networks, especially when the number of nodes is large, can pose significant challenges which are often met by reducing the edge set. Any effective “sparsification” must retain and reflect the important structure in the network. A common method is to simply apply a hard threshold, keeping only those edges whose weight exceeds some predetermined value. A more principled approach is to extract the multiscale “backbone” of a network by retaining statistically significant edges through hypothesis testing on a specific null model, or by appropriately transforming the original weight matrix before applying some sort of threshold. Unfortunately, approaches such as these can fail to capture multiscale structure in which there can be small but locally statistically significant similarity between nodes. In this paper, we introduce a new method for backbone extraction that does not rely on any particular null model, but instead uses the empirical distribution of similarity weight to determine and then retain statistically significant edges. We show that our method adapts to the heterogeneity of local edge weight distributions in several paradigmatic real world networks, and in doing so retains their multiscale structure with relatively insignificant additional computational costs. We anticipate that this simple approach will be of great use in the analysis of massive, highly connected weighted networks. PMID:21346815

  6. Mathematical modelling of complex contagion on clustered networks

    NASA Astrophysics Data System (ADS)

    O'sullivan, David J.; O'Keeffe, Gary; Fennell, Peter; Gleeson, James

    2015-09-01

    The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010), adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the “complex contagion” effects of social reinforcement are important in such diffusion, in contrast to “simple” contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory) regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010), to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.

  7. The complexity and robustness of metro networks

    NASA Astrophysics Data System (ADS)

    Derrible, Sybil; Kennedy, Christopher

    2010-09-01

    Transportation systems, being real-life examples of networks, are particularly interesting to analyze from the viewpoint of the new and rapidly emerging field of network science. Two particular concepts seem to be particularly relevant: scale-free patterns and small-worlds. By looking at 33 metro systems in the world, this paper adapts network science methodologies to the transportation literature, and offers one application to the robustness of metros; here, metro refers to urban rail transit with exclusive right-of-way, whether it is underground, at grade or elevated. We find that most metros are indeed scale-free (with scaling factors ranging from 2.10 to 5.52) and small-worlds; they show atypical behaviors, however, with increasing size. In particular, the presence of transfer-hubs (stations hosting more than three lines) results in relatively large scaling factors. The analysis provides insights/recommendations for increasing the robustness of metro networks. Smaller networks should focus on creating transfer stations, thus generating cycles to offer alternative routes. For larger networks, few stations seem to detain a certain monopole on transferring, it is therefore important to create additional transfers, possibly at the periphery of city centers; the Tokyo system seems to remarkably incorporate these properties.

  8. Mapping Creativity: Creativity Measurements Network Analysis

    ERIC Educational Resources Information Center

    Pinheiro, Igor Reszka; Cruz, Roberto Moraes

    2014-01-01

    This article borrowed network analysis tools to discover how the construct formed by the set of all measures of creativity configures itself. To this end, using a variant of the meta-analytical method, a database was compiled simulating 42,381 responses to 974 variables centered on 64 creativity measures. Results, although preliminary, indicate…

  9. Measuring the dissimilarity of multiplex networks: An empirical study of international trade networks

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaohang; Cui, Huiyuan; Zhu, Ji; Du, Yu; Wang, Qi; Shi, Wenhua

    2017-02-01

    In recent years, multiplex networks are becoming a research focus in the domain of complex networks. Discovering significant correlations between layers in multiplex networks can provide an insight to their structures. In this study, we propose some methods to measure the dissimilarities of different layers in directed and weighted multiplex networks. The dissimilarity is defined on two levels: node level and layer level. The node dissimilarity is computed based on the distance of the probability distribution of its link weights vectors in different layers; and the layer-level dissimilarity is the weighted sum of the nodes' dissimilarities. Furthermore, the dissimilarity is disintegrated into the connection-based dissimilarity and the weight-based dissimilarity, which represent the topological structure changes and the link weight changes, respectively. The proposed methods are applied to international trade networks.

  10. Complex network approach to classifying classical piano compositions

    NASA Astrophysics Data System (ADS)

    Xin, Chen; Zhang, Huishu; Huang, Jiping

    2016-10-01

    Complex network has been regarded as a useful tool handling systems with vague interactions. Hence, numerous applications have arised. In this paper we construct complex networks for 770 classical piano compositions of Mozart, Beethoven and Chopin based on musical note pitches and lengths. We find prominent distinctions among network edges of different composers. Some stylized facts can be explained by such parameters of network structures and topologies. Further, we propose two classification methods for music styles and genres according to the discovered distinctions. These methods are easy to implement and the results are sound. This work suggests that complex network could be a decent way to analyze the characteristics of musical notes, since it could provide a deep view into understanding of the relationships among notes in musical compositions and evidence for classification of different composers, styles and genres of music.

  11. Practical synchronization on complex dynamical networks via optimal pinning control

    NASA Astrophysics Data System (ADS)

    Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu

    2015-07-01

    We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.

  12. Visual analysis and exploration of complex corporate shareholder networks

    NASA Astrophysics Data System (ADS)

    Tekušová, Tatiana; Kohlhammer, Jörn

    2008-01-01

    The analysis of large corporate shareholder network structures is an important task in corporate governance, in financing, and in financial investment domains. In a modern economy, large structures of cross-corporation, cross-border shareholder relationships exist, forming complex networks. These networks are often difficult to analyze with traditional approaches. An efficient visualization of the networks helps to reveal the interdependent shareholding formations and the controlling patterns. In this paper, we propose an effective visualization tool that supports the financial analyst in understanding complex shareholding networks. We develop an interactive visual analysis system by combining state-of-the-art visualization technologies with economic analysis methods. Our system is capable to reveal patterns in large corporate shareholder networks, allows the visual identification of the ultimate shareholders, and supports the visual analysis of integrated cash flow and control rights. We apply our system on an extensive real-world database of shareholder relationships, showing its usefulness for effective visual analysis.

  13. Practical synchronization on complex dynamical networks via optimal pinning control.

    PubMed

    Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu

    2015-07-01

    We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.

  14. Research on the complex network of the UNSPSC ontology

    NASA Astrophysics Data System (ADS)

    Xu, Yingying; Zou, Shengrong; Gu, Aihua; Wei, Li; Zhou, Ta

    The UNSPSC ontology mainly applies to the classification system of the e-business and governments buying the worldwide products and services, and supports the logic structure of classification of the products and services. In this paper, the related technologies of the complex network were applied to analyzing the structure of the ontology. The concept of the ontology was corresponding to the node of the complex network, and the relationship of the ontology concept was corresponding to the edge of the complex network. With existing methods of analysis and performance indicators in the complex network, analyzing the degree distribution and community of the ontology, and the research will help evaluate the concept of the ontology, classify the concept of the ontology and improve the efficiency of semantic matching.

  15. Quantifying and analyzing the network basis of genetic complexity.

    PubMed

    Thompson, Ethan G; Galitski, Timothy

    2012-01-01

    Genotype-to-phenotype maps exhibit complexity. This genetic complexity is mentioned frequently in the literature, but a consistent and quantitative definition is lacking. Here, we derive such a definition and investigate its consequences for model genetic systems. The definition equates genetic complexity with a surplus of genotypic diversity over phenotypic diversity. Applying this definition to ensembles of Boolean network models, we found that the in-degree distribution and the number of periodic attractors produced determine the relative complexity of different topology classes. We found evidence that networks that are difficult to control, or that exhibit a hierarchical structure, are genetically complex. We analyzed the complexity of the cell cycle network of Sacchoromyces cerevisiae and pinpointed genes and interactions that are most important for its high genetic complexity. The rigorous definition of genetic complexity is a tool for unraveling the structure and properties of genotype-to-phenotype maps by enabling the quantitative comparison of the relative complexities of different genetic systems. The definition also allows the identification of specific network elements and subnetworks that have the greatest effects on genetic complexity. Moreover, it suggests ways to engineer biological systems with desired genetic properties.

  16. Scaling of Quantum Walks on Complex Networks

    NASA Astrophysics Data System (ADS)

    Boettcher, Stefan; Falkner, Stefan; Portugal, Renato

    2015-03-01

    I will describe the renormalization group method (RG) as applied to master equations with a unitary propagator. It allows to determine many asymptotic properties of quantum walks, although I will focus here on the walk dimension dw, which describes the similarity solution, ρ (x , t) ~ f| x | dw / t , for the probability density function ρ. We can calculate dw to arbitrary accuracy for a number of networks, such as the dual Sierpinksi gasket, small-world Hanoi networks, or Migdal-Kadanoff lattices, which we have verified with direct simulations. However, due to unitarity, the asymptotic solution of the RG equations as well as procedures to implement RG approximately for arbitrary networks remain elusive. Yet, based on the exact RG for those fractal networks, we can conjecture a few general conclusions, for instance, that dw for a discrete-time quantum walk is always half of that for the random walk on the same r-regular network, when driven with the Grover coin. (This talk summarizes our work in http://dx.doi.org/10.1103/PhysRevA.90.032324 and http://arxiv.org/abs/1410.7034.) We acknowledge financial support from the U.S. National Science Foundation through Grant DMR-1207431.

  17. Improved Landmine Detection by Complex-Valued Artificial Neural Networks

    DTIC Science & Technology

    2002-12-04

    IMPROVED LANDMINE DETECTION BY COMPLEX-VALUED ARTIFICIAL NEURAL NETWORKS Research was Sponsored by: U. S. ARMY RESEARCH OFFICE Program Manager... artificial neural networks in conjunction with fuzzy logic for improved system performance over and above the good results already attained are...of detecting mines. One of the more promising avenues of research in this area involves the use of artificial neural networks [3]. More specifically

  18. Sparse repulsive coupling enhances synchronization in complex networks.

    PubMed

    Leyva, I; Sendiña-Nadal, I; Almendral, J A; Sanjuán, M A F

    2006-11-01

    Through the last years, different strategies to enhance synchronization in complex networks have been proposed. In this work, we show that synchronization of nonidentical dynamical units that are attractively coupled in a small-world network is strongly improved by just making phase-repulsive a tiny fraction of the couplings. By a purely topological analysis that does not depend on the dynamical model, we link the emerging dynamical behavior with the structural properties of the sparsely coupled repulsive network.

  19. Applying complex networks to evaluate precipitation patterns over South America

    NASA Astrophysics Data System (ADS)

    Ciemer, Catrin; Boers, Niklas; Barbosa, Henrique; Kurths, Jürgen; Rammig, Anja

    2016-04-01

    The climate of South America exhibits pronounced differences between the wet- and the dry-season, which are accompanied by specific synoptic events like changes in the location of the South American Low Level Jet (SALLJ) and the establishment of the South American Convergence Zone (SACZ). The onset of these events can be related to the presence of typical large-scale precipitation patterns over South America, as previous studies have shown[1,2]. The application of complex network methods to precipitation data recently received increased scientific attention for the special case of extreme events, as it is possible with such methods to analyze the spatiotemporal correlation structure as well as possible teleconnections of these events[3,4]. In these approaches the correlation between precipitation datasets is calculated by means of Event Synchronization which restricts their applicability to extreme precipitation events. In this work, we propose a method which is able to consider not only extreme precipitation but complete time series. A direct application of standard similarity measures in order to correlate precipitation time series is impossible due to their intricate statistical properties as the large amount of zeros. Therefore, we introduced and evaluated a suitable modification of Pearson's correlation coefficient to construct spatial correlation networks of precipitation. By analyzing the characteristics of spatial correlation networks constructed on the basis of this new measure, we are able to determine coherent areas of similar precipitation patterns, spot teleconnections of correlated areas, and detect central regions for precipitation correlation. By analyzing the change of the network over the year[5], we are also able to determine local and global changes in precipitation correlation patterns. Additionally, global network characteristics as the network connectivity yield indications for beginning and end of wet- and dry season. In order to identify

  20. 2D pattern evolution constrained by complex network dynamics

    NASA Astrophysics Data System (ADS)

    da Rocha, L. E. C.; Costa, L. da F.

    2007-03-01

    Complex networks have established themselves in recent years as being particularly suitable and flexible for representing and modelling several complex natural and artificial systems. In the same time in which the structural intricacies of such networks are being revealed and understood, efforts have also been directed at investigating how such connectivity properties define and constrain the dynamics of systems unfolding on such structures. However, less attention has been focused on hybrid systems, i.e. involving more than one type of network and/or dynamics. Several real systems present such an organization, e.g. the dynamics of a disease coexisting with the dynamics of the immune system. The current paper investigates a specific system involving diffusive (linear and nonlinear) dynamics taking place in a regular network while interacting with a complex network of defensive agents following Erdös Rényi (ER) and Barabási Albert (BA) graph models with moveable nodes. More specifically, the complex network is expected to control, and if possible, to extinguish the diffusion of some given unwanted process (e.g. fire, oil spilling, pest dissemination, and virus or bacteria reproduction during an infection). Two types of pattern evolution are considered: Fick and Gray Scott. The nodes of the defensive network then interact with the diffusing patterns and communicate between themselves in order to control the diffusion. The main findings include the identification of higher efficiency for the BA control networks and the presence of relapses in the case of the ER model.

  1. Epidemic spreading on weighted complex networks

    NASA Astrophysics Data System (ADS)

    Sun, Ye; Liu, Chuang; Zhang, Chu-Xu; Zhang, Zi-Ke

    2014-01-01

    Nowadays, the emergence of online services provides various multi-relation information to support the comprehensive understanding of the epidemic spreading process. In this Letter, we consider the edge weights to represent such multi-role relations. In addition, we perform detailed analysis of two representative metrics, outbreak threshold and epidemic prevalence, on SIS and SIR models. Both theoretical and simulation results find good agreements with each other. Furthermore, experiments show that, on fully mixed networks, the weight distribution on edges would not affect the epidemic results once the average weight of whole network is fixed. This work may shed some light on the in-depth understanding of epidemic spreading on multi-relation and weighted networks.

  2. Pheromone Static Routing Strategy for Complex Networks

    NASA Astrophysics Data System (ADS)

    Hu, Mao-Bin; Henry, Y. K. Lau; Ling, Xiang; Jiang, Rui

    2012-12-01

    We adopt the concept of using pheromones to generate a set of static paths that can reach the performance of global dynamic routing strategy [Phys. Rev. E 81 (2010) 016113]. The path generation method consists of two stages. In the first stage, a pheromone is dropped to the nodes by packets forwarded according to the global dynamic routing strategy. In the second stage, pheromone static paths are generated according to the pheromone density. The output paths can greatly improve traffic systems' overall capacity on different network structures, including scale-free networks, small-world networks and random graphs. Because the paths are static, the system needs much less computational resources than the global dynamic routing strategy.

  3. Noise enhanced activity in a complex network

    NASA Astrophysics Data System (ADS)

    Choudhary, Anshul; Kohar, Vivek; Sinha, Sudeshna

    2014-09-01

    We consider the influence of local noise on a generalized network of populations having positive and negative feedbacks. The population dynamics at the nodes is nonlinear, typically chaotic, and allows cessation of activity if the population falls below a threshold value. We investigate the global stability of this large interactive system, as indicated by the average number of nodal populations that manage to remain active. Our central result is that the probability of obtaining active nodes in this network is significantly enhanced under fluctuations. Further, we find a sharp transition in the number of active nodes as noise strength is varied, along with clearly evident scaling behaviour near the critical noise strength. Lastly, we also observe noise induced temporal coherence in the active sub-network, namely, there is an enhancement in synchrony among the nodes at an intermediate noise strength.

  4. Targeting the dynamics of complex networks

    PubMed Central

    Gutiérrez, Ricardo; Sendiña-Nadal, Irene; Zanin, Massimiliano; Papo, David; Boccaletti, Stefano

    2012-01-01

    We report on a generic procedure to steer (target) a network's dynamics towards a given, desired evolution. The problem is here tackled through a Master Stability Function approach, assessing the stability of the aimed dynamics, and through a selection of nodes to be targeted. We show that the degree of a node is a crucial element in this selection process, and that the targeting mechanism is most effective in heterogeneous scale-free architectures. This makes the proposed approach applicable to the large majority of natural and man-made networked systems. PMID:22563525

  5. Vulnerability Assessment Tools for Complex Information Networks

    DTIC Science & Technology

    2007-11-02

    Takehiro Takahashi No Cliff Zou No Yong Huang No Jie Sun No Sheng Xiao No Yan Cai No Songlin Cai No Hanping Feng No Jonathan Lee No X.C. Lin No Jinghua Hu...Transactions on Modeling and Simulation, 2004. 31. Yongguang Zhang, Wenke Lee, and Yi-an Huang , “Intrusion Detection Techniques for Mobile Wireless Networks...Energy-Efficient Wireless Networks with Real-Time Constraints", Proc. of 45th IEEE Conf. Decision and Control, pp. 2997-3002, Dec. 2006. 29. Y. Huang , W

  6. Reverse preferential spread in complex networks

    NASA Astrophysics Data System (ADS)

    Toyoizumi, Hiroshi; Tani, Seiichi; Miyoshi, Naoto; Okamoto, Yoshio

    2012-08-01

    Large-degree nodes may have a larger influence on the network, but they can be bottlenecks for spreading information since spreading attempts tend to concentrate on these nodes and become redundant. We discuss that the reverse preferential spread (distributing information inversely proportional to the degree of the receiving node) has an advantage over other spread mechanisms. In large uncorrelated networks, we show that the mean number of nodes that receive information under the reverse preferential spread is an upper bound among any other weight-based spread mechanisms, and this upper bound is indeed a logistic growth independent of the degree distribution.

  7. An entropy-driven matrix completion (E-MC) approach to complex network mapping

    NASA Astrophysics Data System (ADS)

    Koochakzadeh, Ali; Pal, Piya

    2016-05-01

    Mapping the topology of a complex network in a resource-efficient manner is a challenging problem with applications in internet mapping, social network inference, and so forth. We propose a new entropy driven algorithm leveraging ideas from matrix completion, to map the network using monitors (or sensors) which, when placed on judiciously selected nodes, are capable of discovering their immediate neighbors. The main challenge is to maximize the portion of discovered network using only a limited number of available monitors. To this end, (i) a new measure of entropy or uncertainty is associated with each node, in terms of the currently discovered edges incident on that node, and (ii) a greedy algorithm is developed to select a candidate node for monitor placement based on its entropy. Utilizing the fact that many complex networks of interest (such as social networks), have a low-rank adjacency matrix, a matrix completion algorithm, namely 1-bit matrix completion, is combined with the greedy algorithm to further boost its performance. The low rank property of the network adjacency matrix can be used to extrapolate a portion of missing edges, and consequently update the node entropies, so as to efficiently guide the network discovery algorithm towards placing monitors on the nodes that can turn out to be more informative. Simulations performed on a variety of real world networks such as social networks and peer networks demonstrate the superior performance of the matrix-completion guided approach in discovering the network topology.

  8. Structural permeability of complex networks to control signals

    PubMed Central

    Lo Iudice, Francesco; Garofalo, Franco; Sorrentino, Francesco

    2015-01-01

    Many biological, social and technological systems can be described as complex networks. The goal of affecting their behaviour has motivated recent work focusing on the relationship between the network structure and its propensity to be controlled. While this work has provided insight into several relevant problems, a comprehensive approach to address partial and complete controllability of networks is still lacking. Here, we bridge this gap by developing a framework to maximize the diffusion of the control signals through a network, while taking into account physical and economic constraints that inevitably arise in applications. This approach allows us to introduce the network permeability, a unified metric of the propensity of a network to be controllable. The analysis of the permeability of several synthetic and real networks enables us to extract some structural features that deepen our quantitative understanding of the ease with which specific controllability requirements can be met. PMID:26391186

  9. Bidirectional selection between two classes in complex social networks.

    PubMed

    Zhou, Bin; He, Zhe; Jiang, Luo-Luo; Wang, Nian-Xin; Wang, Bing-Hong

    2014-12-19

    The bidirectional selection between two classes widely emerges in various social lives, such as commercial trading and mate choosing. Until now, the discussions on bidirectional selection in structured human society are quite limited. We demonstrated theoretically that the rate of successfully matching is affected greatly by individuals' neighborhoods in social networks, regardless of the type of networks. Furthermore, it is found that the high average degree of networks contributes to increasing rates of successful matches. The matching performance in different types of networks has been quantitatively investigated, revealing that the small-world networks reinforces the matching rate more than scale-free networks at given average degree. In addition, our analysis is consistent with the modeling result, which provides the theoretical understanding of underlying mechanisms of matching in complex networks.

  10. Evolving complex networks with conserved clique distributions.

    PubMed

    Kaczor, Gregor; Gros, Claudius

    2008-07-01

    We propose and study a hierarchical algorithm to generate graphs having a predetermined distribution of cliques, the fully connected subgraphs. The construction mechanism may be either random or incorporate preferential attachment. We evaluate the statistical properties of the graphs generated, such as the degree distribution and network diameters, and compare them to some real-world graphs.

  11. Mitochondrial network complexity and pathological decrease in complex I activity are tightly correlated in isolated human complex I deficiency.

    PubMed

    Koopman, Werner J H; Visch, Henk-Jan; Verkaart, Sjoerd; van den Heuvel, Lambertus W P J; Smeitink, Jan A M; Willems, Peter H G M

    2005-10-01

    Complex I (NADH:ubiquinone oxidoreductase) is the largest multisubunit assembly of the oxidative phosphorylation system, and its malfunction is associated with a wide variety of clinical syndromes ranging from highly progressive, often early lethal, encephalopathies to neurodegenerative disorders in adult life. The changes in mitochondrial structure and function that are at the basis of the clinical symptoms are poorly understood. Video-rate confocal microscopy of cells pulse-loaded with mitochondria-specific rhodamine 123 followed by automated analysis of form factor (combined measure of length and degree of branching), aspect ratio (measure of length), and number of revealed marked differences between primary cultures of skin fibroblasts from 13 patients with an isolated complex I deficiency. These differences were independent of the affected subunit, but plotting of the activity of complex I, normalized to that of complex IV, against the ratio of either form factor or aspect ratio to number revealed a linear relationship. Relatively small reductions in activity appeared to be associated with an increase in form factor and never with a decrease in number, whereas relatively large reductions occurred in association with a decrease in form factor and/or an increase in number. These results demonstrate that complex I activity and mitochondrial structure are tightly coupled in human isolated complex I deficiency. To further prove the relationship between aberrations in mitochondrial morphology and pathological condition, fibroblasts from two patients with a different mutation but a highly fragmented mitochondrial phenotype were fused. Full restoration of the mitochondrial network demonstrated that this change in mitochondrial morphology was indeed associated with human complex I deficiency.

  12. The complexity of crime network data: a case study of its consequences for crime control and the study of networks.

    PubMed

    Rostami, Amir; Mondani, Hernan

    2015-01-01

    The field of social network analysis has received increasing attention during the past decades and has been used to tackle a variety of research questions, from prevention of sexually transmitted diseases to humanitarian relief operations. In particular, social network analyses are becoming an important component in studies of criminal networks and in criminal intelligence analysis. At the same time, intelligence analyses and assessments have become a vital component of modern approaches in policing, with policy implications for crime prevention, especially in the fight against organized crime. In this study, we have a unique opportunity to examine one specific Swedish street gang with three different datasets. These datasets are the most common information sources in studies of criminal networks: intelligence, surveillance and co-offending data. We use the data sources to build networks, and compare them by computing distance, centrality, and clustering measures. This study shows the complexity factor by which different data sources about the same object of study have a fundamental impact on the results. The same individuals have different importance ranking depending on the dataset and measure. Consequently, the data source plays a vital role in grasping the complexity of the phenomenon under study. Researchers, policy makers, and practitioners should therefore pay greater attention to the biases affecting the sources of the analysis, and be cautious when drawing conclusions based on intelligence assessments and limited network data. This study contributes to strengthening social network analysis as a reliable tool for understanding and analyzing criminality and criminal networks.

  13. The Complexity of Crime Network Data: A Case Study of Its Consequences for Crime Control and the Study of Networks

    PubMed Central

    Rostami, Amir; Mondani, Hernan

    2015-01-01

    The field of social network analysis has received increasing attention during the past decades and has been used to tackle a variety of research questions, from prevention of sexually transmitted diseases to humanitarian relief operations. In particular, social network analyses are becoming an important component in studies of criminal networks and in criminal intelligence analysis. At the same time, intelligence analyses and assessments have become a vital component of modern approaches in policing, with policy implications for crime prevention, especially in the fight against organized crime. In this study, we have a unique opportunity to examine one specific Swedish street gang with three different datasets. These datasets are the most common information sources in studies of criminal networks: intelligence, surveillance and co-offending data. We use the data sources to build networks, and compare them by computing distance, centrality, and clustering measures. This study shows the complexity factor by which different data sources about the same object of study have a fundamental impact on the results. The same individuals have different importance ranking depending on the dataset and measure. Consequently, the data source plays a vital role in grasping the complexity of the phenomenon under study. Researchers, policy makers, and practitioners should therefore pay greater attention to the biases affecting the sources of the analysis, and be cautious when drawing conclusions based on intelligence assessments and limited network data. This study contributes to strengthening social network analysis as a reliable tool for understanding and analyzing criminality and criminal networks. PMID:25775130

  14. Vulnerability of complex networks under three-level-tree attacks

    NASA Astrophysics Data System (ADS)

    Hao, Yao-hui; Han, Ji-hong; Lin, Yi; Liu, Lin

    2016-11-01

    We investigate vulnerability of complex networks including model networks and real world networks subject to three-level-tree attack. Specifically, we remove three different three-level-tree structures: RRN (Random Root Node), MaxDRN (Max Degree Root Node) and MinDRN (Min Degree Root Node) from a network iteratively until there is no three-level-tree left. Results demonstrate that random network is more robust than scale-free network against three tree attacks, and the robustness of random network decreases as the < k > increases. And scale-free network shows different characteristics in different tree attack modes. The robustness of scale-free is not affected by the < k > parameters for RRN, but increases as the < k > increases for MinDRN. The important thing is that MaxDRN is the most effective in the three tree attack modes, especially for scale-free network. These findings supplement and extend the previous attack results on nodes and edges, and can thus help us better explain the vulnerability of different networks, and provide an insight into more tolerant real complex systems design.

  15. Deployment of check-in nodes in complex networks

    NASA Astrophysics Data System (ADS)

    Jiang, Zhong-Yuan; Ma, Jian-Feng

    2017-01-01

    In many real complex networks such as the city road networks and highway networks, vehicles often have to pass through some specially functioned nodes to receive check-in like services such as gas supplement at gas stations. Based on existing network structures, to guarantee every shortest path including at least a check-in node, the location selection of all check-in nodes is very essential and important to make vehicles to easily visit these check-in nodes, and it is still remains an open problem in complex network studies. In this work, we aim to find possible solutions for this problem. We first convert it into a set cover problem which is NP-complete and propose to employ the greedy algorithm to achieve an approximate result. Inspired by heuristic information of network structure, we discuss other four check-in node location deployment methods including high betweenness first (HBF), high degree first (HDF), random and low degree first (LDF). Finally, we compose extensive simulations in classical scale-free networks, random networks and real network models, and the results can well confirm the effectiveness of the greedy algorithm. This work has potential applications into many real networks.

  16. Deployment of check-in nodes in complex networks.

    PubMed

    Jiang, Zhong-Yuan; Ma, Jian-Feng

    2017-01-11

    In many real complex networks such as the city road networks and highway networks, vehicles often have to pass through some specially functioned nodes to receive check-in like services such as gas supplement at gas stations. Based on existing network structures, to guarantee every shortest path including at least a check-in node, the location selection of all check-in nodes is very essential and important to make vehicles to easily visit these check-in nodes, and it is still remains an open problem in complex network studies. In this work, we aim to find possible solutions for this problem. We first convert it into a set cover problem which is NP-complete and propose to employ the greedy algorithm to achieve an approximate result. Inspired by heuristic information of network structure, we discuss other four check-in node location deployment methods including high betweenness first (HBF), high degree first (HDF), random and low degree first (LDF). Finally, we compose extensive simulations in classical scale-free networks, random networks and real network models, and the results can well confirm the effectiveness of the greedy algorithm. This work has potential applications into many real networks.

  17. Deployment of check-in nodes in complex networks

    PubMed Central

    Jiang, Zhong-Yuan; Ma, Jian-Feng

    2017-01-01

    In many real complex networks such as the city road networks and highway networks, vehicles often have to pass through some specially functioned nodes to receive check-in like services such as gas supplement at gas stations. Based on existing network structures, to guarantee every shortest path including at least a check-in node, the location selection of all check-in nodes is very essential and important to make vehicles to easily visit these check-in nodes, and it is still remains an open problem in complex network studies. In this work, we aim to find possible solutions for this problem. We first convert it into a set cover problem which is NP-complete and propose to employ the greedy algorithm to achieve an approximate result. Inspired by heuristic information of network structure, we discuss other four check-in node location deployment methods including high betweenness first (HBF), high degree first (HDF), random and low degree first (LDF). Finally, we compose extensive simulations in classical scale-free networks, random networks and real network models, and the results can well confirm the effectiveness of the greedy algorithm. This work has potential applications into many real networks. PMID:28074861

  18. A complex-network perspective on Alexander's wholeness

    NASA Astrophysics Data System (ADS)

    Jiang, Bin

    2016-12-01

    The wholeness, conceived and developed by Christopher Alexander, is what exists to some degree or other in space and matter, and can be described by precise mathematical language. However, it remains somehow mysterious and elusive, and therefore hard to grasp. This paper develops a complex network perspective on the wholeness to better understand the nature of order or beauty for sustainable design. I bring together a set of complexity-science subjects such as complex networks, fractal geometry, and in particular underlying scaling hierarchy derived by head/tail breaks - a classification scheme and a visualization tool for data with a heavy-tailed distribution, in order to make Alexander's profound thoughts more accessible to design practitioners and complexity-science researchers. Through several case studies (some of which Alexander studied), I demonstrate that the complex-network perspective helps reduce the mystery of wholeness and brings new insights to Alexander's thoughts on the concept of wholeness or objective beauty that exists in fine and deep structure. The complex-network perspective enables us to see things in their wholeness, and to better understand how the kind of structural beauty emerges from local actions guided by the 15 fundamental properties, and in particular by differentiation and adaptation processes. The wholeness goes beyond current complex network theory towards design or creation of living structures.

  19. Systematic identification of statistically significant network measures

    NASA Astrophysics Data System (ADS)

    Ziv, Etay; Koytcheff, Robin; Middendorf, Manuel; Wiggins, Chris

    2005-01-01

    We present a graph embedding space (i.e., a set of measures on graphs) for performing statistical analyses of networks. Key improvements over existing approaches include discovery of “motif hubs” (multiple overlapping significant subgraphs), computational efficiency relative to subgraph census, and flexibility (the method is easily generalizable to weighted and signed graphs). The embedding space is based on scalars, functionals of the adjacency matrix representing the network. Scalars are global, involving all nodes; although they can be related to subgraph enumeration, there is not a one-to-one mapping between scalars and subgraphs. Improvements in network randomization and significance testing—we learn the distribution rather than assuming Gaussianity—are also presented. The resulting algorithm establishes a systematic approach to the identification of the most significant scalars and suggests machine-learning techniques for network classification.

  20. 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.

  1. Centrality Measures of Dynamic Social Networks

    DTIC Science & Technology

    2012-11-01

    adapt to the disruption. In the future, I plan to incorporate these additional topics with a secondary case study of the more complex Enron data set...Army Research Laboratory: Aberdeen Proving Ground, 2012. 4. Diesner, J.; Carley, K.M. Exploration of Communication Networks from the Enron Email...Refinement of the Ali Baba Data Set; ARL-TN- 0476; U.S. Army Research Laboratory: Aberdeen Proving Ground, 2012. 16. Cohen, W. Enron Email Dataset

  2. Dynamical complexity in the perception-based network formation model

    NASA Astrophysics Data System (ADS)

    Jo, Hang-Hyun; Moon, Eunyoung

    2016-12-01

    Many link formation mechanisms for the evolution of social networks have been successful to reproduce various empirical findings in social networks. However, they have largely ignored the fact that individuals make decisions on whether to create links to other individuals based on cost and benefit of linking, and the fact that individuals may use perception of the network in their decision making. In this paper, we study the evolution of social networks in terms of perception-based strategic link formation. Here each individual has her own perception of the actual network, and uses it to decide whether to create a link to another individual. An individual with the least perception accuracy can benefit from updating her perception using that of the most accurate individual via a new link. This benefit is compared to the cost of linking in decision making. Once a new link is created, it affects the accuracies of other individuals' perceptions, leading to a further evolution of the actual network. As for initial actual networks, we consider both homogeneous and heterogeneous cases. The homogeneous initial actual network is modeled by Erdős-Rényi (ER) random networks, while we take a star network for the heterogeneous case. In any cases, individual perceptions of the actual network are modeled by ER random networks with controllable linking probability. Then the stable link density of the actual network is found to show discontinuous transitions or jumps according to the cost of linking. As the number of jumps is the consequence of the dynamical complexity, we discuss the effect of initial conditions on the number of jumps to find that the dynamical complexity strongly depends on how much individuals initially overestimate or underestimate the link density of the actual network. For the heterogeneous case, the role of the highly connected individual as an information spreader is also discussed.

  3. Measures of Complexity to quantify Bone Structure

    NASA Astrophysics Data System (ADS)

    Saparin, Peter; Gowin, Wolfgang; Kurths, Jürgen; Felsenberg, Dieter

    1998-03-01

    We propose a technique to assess structure of the bone in its spatial distribution by describing and quantifying the structural architecture as a whole. The concept of measures of complexity based on symbolic dynamics is applied to computed tomography (CT) - images obtained from human lumbar vertebra. CT-images have been transformed into images consisting of 5 different symbols, whereby both statical and dynamical coding are included. Different aspects of the bone structure are quantified by several measures which have been introduced: index of global ensemble of elements composing the bone; complexity, homogeneity and dynamics within the bone architecture; complexity and inhomogeneity of the trabecular net. This leads to new insides to the understanding of bone's internal structure. The results give the first experimental and quantitative evidence of the theoretical prediction that complexity of bone structure declines rapidly with the increased disintegration of bone structures leading to the loss of bone mass and specify experimentally that bone structure is exponentially related to its density. Especially, osteoporotic vertebrae are less complex organized than normal ones. In addition, this method is significantly sensitive to changes in bone structure and provides improvements of diagnostic of pathological structural loss.

  4. Measuring complexity in Brazilian economic crises

    PubMed Central

    Mortoza, Letícia P. D.; Piqueira, José R. C.

    2017-01-01

    Capital flows are responsible for a strong influence on the foreign exchange rates and stock prices macroeconomic parameters. In volatile economies, capital flows can change due to several types of social, political and economic events, provoking oscillations on these parameters, which are recognized as economic crises. This work aims to investigate how these two macroeconomic variables are related with crisis events by using the traditional complex measures due to Lopez-Mancini-Calbet (LMC) and to Shiner-Davison-Landsberg (SDL), that can be applied to any temporal series. Here, Ibovespa (Bovespa Stock Exchange main Index) and the “dollar-real” parity are the background for calculating the LMC and SDL complexity measures. By analyzing the temporal evolution of these measures, it is shown that they might be related to important events that occurred in the Brazilian economy. PMID:28301506

  5. Measure of Node Similarity in Multilayer Networks

    PubMed Central

    Mollgaard, Anders; Zettler, Ingo; Dammeyer, Jesper; Jensen, Mogens H.; Lehmann, Sune; Mathiesen, Joachim

    2016-01-01

    The weight of links in a network is often related to the similarity of the nodes. Here, we introduce a simple tunable measure for analysing the similarity of nodes across different link weights. In particular, we use the measure to analyze homophily in a group of 659 freshman students at a large university. Our analysis is based on data obtained using smartphones equipped with custom data collection software, complemented by questionnaire-based data. The network of social contacts is represented as a weighted multilayer network constructed from different channels of telecommunication as well as data on face-to-face contacts. We find that even strongly connected individuals are not more similar with respect to basic personality traits than randomly chosen pairs of individuals. In contrast, several socio-demographics variables have a significant degree of similarity. We further observe that similarity might be present in one layer of the multilayer network and simultaneously be absent in the other layers. For a variable such as gender, our measure reveals a transition from similarity between nodes connected with links of relatively low weight to dis-similarity for the nodes connected by the strongest links. We finally analyze the overlap between layers in the network for different levels of acquaintanceships. PMID:27300084

  6. A Protein Complex Network of Drosophila melanogaster

    PubMed Central

    Guruharsha, K. G.; Rual, J. -F.; Zhai, B.; Mintseris, J.; Vaidya, P.; Vaidya, N.; Beekman, C.; Wong, C.; Rhee, D. Y.; Cenaj, O.; McKillip, E.; Shah, S.; Stapleton, M.; Wan, K. H.; Yu, C.; Parsa, B.; Carlson, J. W.; Chen, X.; Kapadia, B.; VijayRaghavan, K.; Gygi, S. P.; Celniker, S. E.; Obar, R. A.; Artavanis-Tsakonas, S.

    2011-01-01

    SUMMARY Determining the composition of protein complexes is an essential step towards understanding the cell as an integrated system. Using co-affinity purification coupled to mass spectrometry analysis, we examined protein associations involving nearly five thousand individual, FLAG-HA epitope-tagged Drosophila proteins. Stringent analysis of these data, based on a novel statistical framework to define individual protein-protein interactions, led to the generation of a Drosophila Protein interaction Map (DPiM) encompassing 556 protein complexes. The high quality of DPiM and its usefulness as a paradigm for metazoan proteomes is apparent from the recovery of many known complexes, significant enrichment for shared functional attributes and validation in human cells. DPiM defines potential novel members for several important protein complexes and assigns functional links to 586 protein-coding genes lacking previous experimental annotation. DPiM represents, to our knowledge, the largest metazoan protein complex map and provides a valuable resource for analysis of protein complex evolution. PMID:22036573

  7. Effective number of accessed nodes in complex networks.

    PubMed

    Viana, Matheus P; Batista, João L B; Costa, Luciano da F

    2012-03-01

    The measurement called accessibility has been proposed as a means to quantify the efficiency of the communication between nodes in complex networks. This article reports results regarding the properties of accessibility, including its relationship with the average minimal time to visit all nodes reachable after h steps along a random walk starting from a source, as well as the number of nodes that are visited after a finite period of time. We characterize the relationship between accessibility and the average number of walks required in order to visit all reachable nodes (the exploration time), conjecture that the maximum accessibility implies the minimal exploration time, and confirm the relationship between the accessibility values and the number of nodes visited after a basic time unit. The latter relationship is investigated with respect to three types of dynamics: traditional random walks, self-avoiding random walks, and preferential random walks.

  8. Growth, collapse, and self-organized criticality in complex networks

    PubMed Central

    Wang, Yafeng; Fan, Huawei; Lin, Weijie; Lai, Ying-Cheng; Wang, Xingang

    2016-01-01

    Network growth is ubiquitous in nature (e.g., biological networks) and technological systems (e.g., modern infrastructures). To understand how certain dynamical behaviors can or cannot persist as the underlying network grows is a problem of increasing importance in complex dynamical systems as well as sustainability science and engineering. We address the question of whether a complex network of nonlinear oscillators can maintain its synchronization stability as it expands. We find that a large scale avalanche over the entire network can be triggered in the sense that the individual nodal dynamics diverges from the synchronous state in a cascading manner within a relatively short time period. In particular, after an initial stage of linear growth, the network typically evolves into a critical state where the addition of a single new node can cause a group of nodes to lose synchronization, leading to synchronization collapse for the entire network. A statistical analysis reveals that the collapse size is approximately algebraically distributed, indicating the emergence of self-organized criticality. We demonstrate the generality of the phenomenon of synchronization collapse using a variety of complex network models, and uncover the underlying dynamical mechanism through an eigenvector analysis. PMID:27079515

  9. Growth, collapse, and self-organized criticality in complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Yafeng; Fan, Huawei; Lin, Weijie; Lai, Ying-Cheng; Wang, Xingang

    2016-04-01

    Network growth is ubiquitous in nature (e.g., biological networks) and technological systems (e.g., modern infrastructures). To understand how certain dynamical behaviors can or cannot persist as the underlying network grows is a problem of increasing importance in complex dynamical systems as well as sustainability science and engineering. We address the question of whether a complex network of nonlinear oscillators can maintain its synchronization stability as it expands. We find that a large scale avalanche over the entire network can be triggered in the sense that the individual nodal dynamics diverges from the synchronous state in a cascading manner within a relatively short time period. In particular, after an initial stage of linear growth, the network typically evolves into a critical state where the addition of a single new node can cause a group of nodes to lose synchronization, leading to synchronization collapse for the entire network. A statistical analysis reveals that the collapse size is approximately algebraically distributed, indicating the emergence of self-organized criticality. We demonstrate the generality of the phenomenon of synchronization collapse using a variety of complex network models, and uncover the underlying dynamical mechanism through an eigenvector analysis.

  10. Growth, collapse, and self-organized criticality in complex networks.

    PubMed

    Wang, Yafeng; Fan, Huawei; Lin, Weijie; Lai, Ying-Cheng; Wang, Xingang

    2016-04-15

    Network growth is ubiquitous in nature (e.g., biological networks) and technological systems (e.g., modern infrastructures). To understand how certain dynamical behaviors can or cannot persist as the underlying network grows is a problem of increasing importance in complex dynamical systems as well as sustainability science and engineering. We address the question of whether a complex network of nonlinear oscillators can maintain its synchronization stability as it expands. We find that a large scale avalanche over the entire network can be triggered in the sense that the individual nodal dynamics diverges from the synchronous state in a cascading manner within a relatively short time period. In particular, after an initial stage of linear growth, the network typically evolves into a critical state where the addition of a single new node can cause a group of nodes to lose synchronization, leading to synchronization collapse for the entire network. A statistical analysis reveals that the collapse size is approximately algebraically distributed, indicating the emergence of self-organized criticality. We demonstrate the generality of the phenomenon of synchronization collapse using a variety of complex network models, and uncover the underlying dynamical mechanism through an eigenvector analysis.

  11. Radial basis function networks and complexity regularization in function learning.

    PubMed

    Krzyzak, A; Linder, T

    1998-01-01

    In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function network. Our approach differs from previous complexity regularization neural-network function learning schemes in that we operate with random covering numbers and l(1) metric entropy, making it possible to consider much broader families of activation functions, namely functions of bounded variation. Some constraints previously imposed on the network parameters are also eliminated this way. The network is trained by means of complexity regularization involving empirical risk minimization. Bounds on the expected risk in terms of the sample size are obtained for a large class of loss functions. Rates of convergence to the optimal loss are also derived.

  12. Complex Learning in Bio-plausible Memristive Networks

    PubMed Central

    Deng, Lei; Li, Guoqi; Deng, Ning; Wang, Dong; Zhang, Ziyang; He, Wei; Li, Huanglong; Pei, Jing; Shi, Luping

    2015-01-01

    The emerging memristor-based neuromorphic engineering promises an efficient computing paradigm. However, the lack of both internal dynamics in the previous feedforward memristive networks and efficient learning algorithms in recurrent networks, fundamentally limits the learning ability of existing systems. In this work, we propose a framework to support complex learning functions by introducing dedicated learning algorithms to a bio-plausible recurrent memristive network with internal dynamics. We fabricate iron oxide memristor-based synapses, with well controllable plasticity and a wide dynamic range of excitatory/inhibitory connection weights, to build the network. To adaptively modify the synaptic weights, the comprehensive recursive least-squares (RLS) learning algorithm is introduced. Based on the proposed framework, the learning of various timing patterns and a complex spatiotemporal pattern of human motor is demonstrated. This work paves a new way to explore the brain-inspired complex learning in neuromorphic systems. PMID:26090862

  13. Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

    NASA Astrophysics Data System (ADS)

    Donges, Jonathan; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik; Marwan, Norbert; Dijkstra, Henk; Kurths, Jürgen

    2016-04-01

    We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology. pyunicorn is available online at https://github.com/pik-copan/pyunicorn. Reference: J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng, L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra, and J. Kurths, Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015), DOI: 10.1063/1.4934554, Preprint: arxiv.org:1507.01571 [physics.data-an].

  14. Fuzzy nodes recognition based on spectral clustering in complex networks

    NASA Astrophysics Data System (ADS)

    Ma, Yang; Cheng, Guangquan; Liu, Zhong; Xie, Fuli

    2017-01-01

    In complex networks, information regarding the nodes is usually incomplete because of the effects of interference, noise, and other factors. This results in parts of the network being blurred and some information having an unknown source. In this paper, a spectral clustering algorithm is used to identify fuzzy nodes and solve network reconstruction problems. By changing the fuzzy degree of placeholders, we achieve various degrees of credibility and accuracy for the restored network. Our approach is verified by experiments using open source datasets and simulated data.

  15. Approach of Complex Networks for the Determination of Brain Death

    NASA Astrophysics Data System (ADS)

    Sun, Wei-Gang; Cao, Jian-Ting; Wang, Ru-Bin

    2011-06-01

    In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our findings might provide valuable insights on the determination of brain death.

  16. Complex Network for a Crisis Contagion on AN Interbank System

    NASA Astrophysics Data System (ADS)

    Tirado, Mariano

    2012-09-01

    The main focus of this research is the contagion of a financial crisis on an interbank debt network. In order to simulate the crisis propagation a weighted community complex network based on growth strategy has been created. The contagion is described by a new way of disease propagation perspective based on the concept of a financial virus. The model reproduces the existence of TBTF banks and shows the impact that an initial TBTF bank crash produces in the interbank network depending on the magnitude of the initial crash and on the resistance that the network offers against the contagion propagation.

  17. Connecting Core Percolation and Controllability of Complex Networks

    PubMed Central

    Jia, Tao; Pósfai, Márton

    2014-01-01

    Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks. PMID:24946797

  18. Modeling the propagation of mobile malware on complex networks

    NASA Astrophysics Data System (ADS)

    Liu, Wanping; Liu, Chao; Yang, Zheng; Liu, Xiaoyang; Zhang, Yihao; Wei, Zuxue

    2016-08-01

    In this paper, the spreading behavior of malware across mobile devices is addressed. By introducing complex networks to model mobile networks, which follows the power-law degree distribution, a novel epidemic model for mobile malware propagation is proposed. The spreading threshold that guarantees the dynamics of the model is calculated. Theoretically, the asymptotic stability of the malware-free equilibrium is confirmed when the threshold is below the unity, and the global stability is further proved under some sufficient conditions. The influences of different model parameters as well as the network topology on malware propagation are also analyzed. Our theoretical studies and numerical simulations show that networks with higher heterogeneity conduce to the diffusion of malware, and complex networks with lower power-law exponents benefit malware spreading.

  19. Role of dimensionality in complex networks

    PubMed Central

    Brito, Samuraí; da Silva, L. R.; Tsallis, Constantino

    2016-01-01

    Deep connections are known to exist between scale-free networks and non-Gibbsian statistics. For example, typical degree distributions at the thermodynamical limit are of the form , where the q-exponential form optimizes the nonadditive entropy Sq (which, for q → 1, recovers the Boltzmann-Gibbs entropy). We introduce and study here d-dimensional geographically-located networks which grow with preferential attachment involving Euclidean distances through . Revealing the connection with q-statistics, we numerically verify (for d = 1, 2, 3 and 4) that the q-exponential degree distributions exhibit, for both q and k, universal dependences on the ratio αA/d. Moreover, the q = 1 limit is rapidly achieved by increasing αA/d to infinity. PMID:27320047

  20. Hybrid percolation transition in complex networks

    NASA Astrophysics Data System (ADS)

    Kahng, Byungnam

    Percolation has been one of the most applied statistical models. Percolation transition is one of the most robust continuous transitions known thus far. However, recent extensive researches reveal that it exhibits diverse types of phase transitions such as discontinuous and hybrid phase transitions. Here hybrid phase transition means the phase transition exhibiting natures of both continuous and discontinuous phase transitions simultaneously. Examples include k-core percolation, cascading failures in interdependent networks, synchronization, etc. Thus far, it is not manifest if the critical behavior of hybrid percolation transitions conforms to the conventional scaling laws of second-order phase transition. Here, we investigate the critical behaviors of hybrid percolation transitions in the cascading failure model in inter-dependent networks and the restricted Erdos-Renyi model. We find that the critical behaviors of the hybrid percolation transitions contain some features that cannot be described by the conventional theory of second-order percolation transitions.

  1. Does network complexity help organize Babel's library?

    NASA Astrophysics Data System (ADS)

    Cárdenas, Juan Pablo; González, Iván; Vidal, Gerardo; Fuentes, Miguel Angel

    2016-04-01

    In this work we show that global topological properties of co-occurrent word networks constructed from texts, seem to be the fingerprint of meaningful sentences. We observe that many statistical properties of these networks depend on the frequency of words, however, others seem to be strictly determined by the grammar. Our results suggest that seems to be a lower bound of sense that depends on the correlation between mean word connectivity and word connectivity correlation. This property, in addition to being only present in meaningful texts, and absent in, until now, not decoded texts such as the Voynich manuscript, would also be exclusive for natural languages, allowing us to discriminate between these and formal texts.

  2. Vital nodes identification in complex networks

    NASA Astrophysics Data System (ADS)

    Lü, Linyuan; Chen, Duanbing; Ren, Xiao-Long; Zhang, Qian-Ming; Zhang, Yi-Cheng; Zhou, Tao

    2016-09-01

    Real networks exhibit heterogeneous nature with nodes playing far different roles in structure and function. To identify vital nodes is thus very significant, allowing us to control the outbreak of epidemics, to conduct advertisements for e-commercial products, to predict popular scientific publications, and so on. The vital nodes identification attracts increasing attentions from both computer science and physical societies, with algorithms ranging from simply counting the immediate neighbors to complicated machine learning and message passing approaches. In this review, we clarify the concepts and metrics, classify the problems and methods, as well as review the important progresses and describe the state of the art. Furthermore, we provide extensive empirical analyses to compare well-known methods on disparate real networks, and highlight the future directions. In spite of the emphasis on physics-rooted approaches, the unification of the language and comparison with cross-domain methods would trigger interdisciplinary solutions in the near future.

  3. Analysis and Design of Complex Network Environments

    DTIC Science & Technology

    2012-03-01

    differential equations , are not effective for our purposes because they are too detailed, resulting in a heavy information load on the experimental data...presented in the previous section. The corresponding sets of ordinary differential equation describing the dynamics of the considered networks are used to...simplification, in this example, only steady-state data was used. Data was obtained by numerically integrating the differential equations in (26)-(31

  4. Structure and Dynamics of Complex Networks

    DTIC Science & Technology

    2012-06-07

    5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 2930 31 32 33 34 Figure 1: The deterministic partition of the famous Karate club...belonging to the two clusters respectively) for each node in karate club network. The darker the color, the larger the value of ρK . The transition

  5. Mesoscale meteorological measurements characterizing complex flows

    SciTech Connect

    Hubbe, J.M.; Allwine, K.J.

    1993-09-01

    Meteorological measurements are an integral and essential component of any emergency response system for addressing accidental releases from nuclear facilities. An important element of the US Department of Energy`s (DOE`s) Atmospheric Studies in Complex Terrain (ASCOT) program is the refinement and use of state-of-the-art meteorological instrumentation. ASCOT is currently making use of ground-based remote wind sensing instruments such as doppler acoustic sounders (sodars). These instruments are capable of continuously and reliably measuring winds up to several hundred meters above the ground, unattended. Two sodars are currently measuring the winds, as part of ASCOT`s Front Range Study, in the vicinity of DOE`s Rocky Flats Plant (RFP) near Boulder, Colorado. A brief description of ASCOT`s ongoing Front Range Study is given followed by a case study analysis that demonstrates the utility of the meteorological measurement equipment and the complexity of flow phenomena that are experienced near RFP. These complex flow phenomena can significantly influence the transport of the released material and consequently need to be identified for accurate assessments of the consequences of a release.

  6. Heuristic Strategies for Persuader Selection in Contagions on Complex Networks

    PubMed Central

    Wang, Peng; Zhang, Li-Jie; Xiao, Gaoxi

    2017-01-01

    Individual decision to accept a new idea or product is often driven by both self-adoption and others’ persuasion, which has been simulated using a double threshold model [Huang et al., Scientific Reports 6, 23766 (2016)]. We extend the study to consider the case with limited persuasion. That is, a set of individuals is chosen from the population to be equipped with persuasion capabilities, who may succeed in persuading their friends to take the new entity when certain conditions are satisfied. Network node centrality is adopted to characterize each node’s influence, based on which three heuristic strategies are applied to pick out persuaders. We compare these strategies for persuader selection on both homogeneous and heterogeneous networks. Two regimes of the underline networks are identified in which the system exhibits distinct behaviors: when networks are sufficiently sparse, selecting persuader nodes in descending order of node centrality achieves the best performance; when networks are sufficiently dense, however, selecting nodes with medium centralities to serve as the persuaders performs the best. Under respective optimal strategies for different types of networks, we further probe which centrality measure is most suitable for persuader selection. It turns out that for the first regime, degree centrality offers the best measure for picking out persuaders from homogeneous networks; while in heterogeneous networks, betweenness centrality takes its place. In the second regime, there is no significant difference caused by centrality measures in persuader selection for homogeneous network; while for heterogeneous networks, closeness centrality offers the best measure. PMID:28072847

  7. Heuristic Strategies for Persuader Selection in Contagions on Complex Networks.

    PubMed

    Wang, Peng; Zhang, Li-Jie; Xu, Xin-Jian; Xiao, Gaoxi

    2017-01-01

    Individual decision to accept a new idea or product is often driven by both self-adoption and others' persuasion, which has been simulated using a double threshold model [Huang et al., Scientific Reports 6, 23766 (2016)]. We extend the study to consider the case with limited persuasion. That is, a set of individuals is chosen from the population to be equipped with persuasion capabilities, who may succeed in persuading their friends to take the new entity when certain conditions are satisfied. Network node centrality is adopted to characterize each node's influence, based on which three heuristic strategies are applied to pick out persuaders. We compare these strategies for persuader selection on both homogeneous and heterogeneous networks. Two regimes of the underline networks are identified in which the system exhibits distinct behaviors: when networks are sufficiently sparse, selecting persuader nodes in descending order of node centrality achieves the best performance; when networks are sufficiently dense, however, selecting nodes with medium centralities to serve as the persuaders performs the best. Under respective optimal strategies for different types of networks, we further probe which centrality measure is most suitable for persuader selection. It turns out that for the first regime, degree centrality offers the best measure for picking out persuaders from homogeneous networks; while in heterogeneous networks, betweenness centrality takes its place. In the second regime, there is no significant difference caused by centrality measures in persuader selection for homogeneous network; while for heterogeneous networks, closeness centrality offers the best measure.

  8. Psychometric Measurement Models and Artificial Neural Networks

    ERIC Educational Resources Information Center

    Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

    2004-01-01

    The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

  9. Complex networks as an emerging property of hierarchical preferential attachment

    NASA Astrophysics Data System (ADS)

    Hébert-Dufresne, Laurent; Laurence, Edward; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J.

    2015-12-01

    Real complex systems are not rigidly structured; no clear rules or blueprints exist for their construction. Yet, amidst their apparent randomness, complex structural properties universally emerge. We propose that an important class of complex systems can be modeled as an organization of many embedded levels (potentially infinite in number), all of them following the same universal growth principle known as preferential attachment. We give examples of such hierarchy in real systems, for instance, in the pyramid of production entities of the film industry. More importantly, we show how real complex networks can be interpreted as a projection of our model, from which their scale independence, their clustering, their hierarchy, their fractality, and their navigability naturally emerge. Our results suggest that complex networks, viewed as growing systems, can be quite simple, and that the apparent complexity of their structure is largely a reflection of their unobserved hierarchical nature.

  10. Rumor spreading model with noise interference in complex social networks

    NASA Astrophysics Data System (ADS)

    Zhu, Liang; Wang, Youguo

    2017-03-01

    In this paper, a modified susceptible-infected-removed (SIR) model has been proposed to explore rumor diffusion on complex social networks. We take variation of connectivity into consideration and assume the variation as noise. On the basis of related literature on virus networks, the noise is described as standard Brownian motion while stochastic differential equations (SDE) have been derived to characterize dynamics of rumor diffusion both on homogeneous networks and heterogeneous networks. Then, theoretical analysis on homogeneous networks has been demonstrated to investigate the solution of SDE model and the steady state of rumor diffusion. Simulations both on Barabási-Albert (BA) network and Watts-Strogatz (WS) network display that the addition of noise accelerates rumor diffusion and expands diffusion size, meanwhile, the spreading speed on BA network is much faster than on WS network under the same noise intensity. In addition, there exists a rumor diffusion threshold in statistical average meaning on homogeneous network which is absent on heterogeneous network. Finally, we find a positive correlation between peak value of infected individuals and noise intensity while a negative correlation between rumor lifecycle and noise intensity overall.

  11. Optimizing controllability of edge dynamics in complex networks by perturbing network structure

    NASA Astrophysics Data System (ADS)

    Pang, Shaopeng; Hao, Fei

    2017-03-01

    Using the minimum input signals to drive the dynamics in complex networks toward some desired state is a fundamental issue in the field of network controllability. For a complex network with the dynamical process defined on its edges, the controllability of this network is optimal if it can be fully controlled by applying one input signal to an arbitrary non-isolated vertex of it. In this paper, the adding-edge strategy and turning-edge strategy are proposed to optimize the controllability by minimum structural perturbations. Simulations and analyses indicate that the minimum number of adding-edges required for the optimal controllability is equal to the minimum number of turning-edges, and networks with positively correlated in- and out-degrees are easier to achieve optimal controllability. Furthermore, both the strategies have the capacity to reveal the relationship between certain structural properties of a complex network and its controllability of edge dynamics.

  12. Combining complex networks and data mining: Why and how

    NASA Astrophysics Data System (ADS)

    Zanin, M.; Papo, D.; Sousa, P. A.; Menasalvas, E.; Nicchi, A.; Kubik, E.; Boccaletti, S.

    2016-05-01

    The increasing power of computer technology does not dispense with the need to extract meaningful information out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

  13. Local modularity for community detection in complex networks

    NASA Astrophysics Data System (ADS)

    Xiang, Ju; Hu, Tao; Zhang, Yan; Hu, Ke; Li, Jian-Ming; Xu, Xiao-Ke; Liu, Cui-Cui; Chen, Shi

    2016-02-01

    Community detection is a topic of interest in the study of complex networks such as the protein-protein interaction networks and metabolic networks. In recent years, various methods were proposed to detect community structures of the networks. Here, a kind of local modularity with tunable parameter is derived from the Newman-Girvan modularity by a special self-loop strategy that depends on the community division of the networks. By the self-loop strategy, one can easily control the definition of modularity, and the resulting modularity can be optimized by using the existing modularity optimization algorithms. The local modularity is used as the target function for community detection, and a self-consistent method is proposed for the optimization of the local modularity. We analyze the behaviors of the local modularity and show the validity of the local modularity in detecting community structures on various networks.

  14. Efficiency of attack strategies on complex model and real-world networks

    NASA Astrophysics Data System (ADS)

    Bellingeri, Michele; Cassi, Davide; Vincenzi, Simone

    2014-11-01

    We investigated the efficiency of attack strategies to network nodes when targeting several complex model and real-world networks. We tested 5 attack strategies, 3 of which were introduced in this work for the first time, to attack 3 model networks (Erdos and Renyi, Barabasi and Albert preferential attachment network, and scale-free network configuration models) and 3 real networks (Gnutella peer-to-peer network, email network of the University of Rovira i Virgili, and immunoglobulin interaction network). Nodes were removed sequentially according to the importance criterion defined by the attack strategy, and we used the size of the largest connected component (LCC) as a measure of network damage. We found that the efficiency of attack strategies (fraction of nodes to be deleted for a given reduction of LCC size) depends on the topology of the network, although attacks based on either the number of connections of a node or betweenness centrality were often the most efficient strategies. Sequential deletion of nodes in decreasing order of betweenness centrality was the most efficient attack strategy when targeting real-world networks. The relative efficiency of attack strategies often changed during the sequential removal of nodes, especially for networks with power-law degree distribution.

  15. Complexity Theory and Network Centric Warfare

    DTIC Science & Technology

    2003-09-01

    below. THE CONCEPTUAL FRAMEWORK OF COMPLEXITY Prof. Murray Gell - Mann [5] traces the meaning to the root of the word. Plexus means braided or entwined...Washington, DC, USA. 5 GELL - MANN M (1994). The Quark and the Jaguar. WH Freeman. New York, USA. 6 SETHNA J P, DAHMEN K A, and MYERS C R (2001). “Crackling

  16. COMPLEX NETWORKS IN CLIMATE SCIENCE: PROGRESS, OPPORTUNITIES AND CHALLENGES

    SciTech Connect

    Steinhaeuser, Karsten J K; Chawla, Nitesh; Ganguly, Auroop R

    2010-01-01

    Networks have been used to describe and model a wide range of complex systems, both natural as well as man-made. One particularly interesting application in the earth sciences is the use of complex networks to represent and study the global climate system. In this paper, we motivate this general approach, explain the basic methodology, report on the state of the art (including our contributions), and outline open questions and opportunities for future research. Datasets and systems that can be represented as interaction networks (or graphs), broadly defined as any collection of interrelated objects or entities, have received considerable attention both from a theoretical viewpoint as well as various application domains; examples include the analysis of social networks, chemical interactions between proteins, the behavior of financial markets, and many others. Recently, the study of complex networks - that is, networks which exhibit non-trivial topological properties - has permeated numerous fields and disciplines spanning the physical, social, and computational sciences. So why do networks enjoy such broad appeal? Briefly, it is their ability to serve at once as a data representation, as an analysis framework, and as a visualization tool. The analytic capabilities in particular are quite powerful, as networks can uncover structure and patterns at multiple scales, ranging from local properties to global phenomena, and thus help better understand the characteristics of complex systems. We focus on one particular application of networks in the earth sciences, namely, the construction and analysis of climate networks. Identifying and analyzing patterns in global climate is an important task of growing scientific, social, and political interest, with the goal of deepening our understanding of the complex processes underlying observed phenomena. To this end, we make the case that complex networks offer a compelling perspective for capturing the dynamics of the climate

  17. Functional holography analysis: Simplifying the complexity of dynamical networks

    NASA Astrophysics Data System (ADS)

    Baruchi, Itay; Grossman, Danny; Volman, Vladislav; Shein, Mark; Hunter, John; Towle, Vernon L.; Ben-Jacob, Eshel

    2006-03-01

    We present a novel functional holography (FH) analysis devised to study the dynamics of task-performing dynamical networks. The latter term refers to networks composed of dynamical systems or elements, like gene networks or neural networks. The new approach is based on the realization that task-performing networks follow some underlying principles that are reflected in their activity. Therefore, the analysis is designed to decipher the existence of simple causal motives that are expected to be embedded in the observed complex activity of the networks under study. First we evaluate the matrix of similarities (correlations) between the activities of the network's components. We then perform collective normalization of the similarities (or affinity transformation) to construct a matrix of functional correlations. Using dimension reduction algorithms on the affinity matrix, the matrix is projected onto a principal three-dimensional space of the leading eigenvectors computed by the algorithm. To retrieve back information that is lost in the dimension reduction, we connect the nodes by colored lines that represent the level of the similarities to construct a holographic network in the principal space. Next we calculate the activity propagation in the network (temporal ordering) using different methods like temporal center of mass and cross correlations. The causal information is superimposed on the holographic network by coloring the nodes locations according to the temporal ordering of their activities. First, we illustrate the analysis for simple, artificially constructed examples. Then we demonstrate that by applying the FH analysis to modeled and real neural networks as well as recorded brain activity, hidden causal manifolds with simple yet characteristic geometrical and topological features are deciphered in the complex activity. The term "functional holography" is used to indicate that the goal of the analysis is to extract the maximum amount of functional

  18. Input graph: the hidden geometry in controlling complex networks

    NASA Astrophysics Data System (ADS)

    Zhang, Xizhe; Lv, Tianyang; Pu, Yuanyuan

    2016-11-01

    The ability to control a complex network towards a desired behavior relies on our understanding of the complex nature of these social and technological networks. The existence of numerous control schemes in a network promotes us to wonder: what is the underlying relationship of all possible input nodes? Here we introduce input graph, a simple geometry that reveals the complex relationship between all control schemes and input nodes. We prove that the node adjacent to an input node in the input graph will appear in another control scheme, and the connected nodes in input graph have the same type in control, which they are either all possible input nodes or not. Furthermore, we find that the giant components emerge in the input graphs of many real networks, which provides a clear topological explanation of bifurcation phenomenon emerging in dense networks and promotes us to design an efficient method to alter the node type in control. The findings provide an insight into control principles of complex networks and offer a general mechanism to design a suitable control scheme for different purposes.

  19. Input graph: the hidden geometry in controlling complex networks

    PubMed Central

    Zhang, Xizhe; Lv, Tianyang; Pu, Yuanyuan

    2016-01-01

    The ability to control a complex network towards a desired behavior relies on our understanding of the complex nature of these social and technological networks. The existence of numerous control schemes in a network promotes us to wonder: what is the underlying relationship of all possible input nodes? Here we introduce input graph, a simple geometry that reveals the complex relationship between all control schemes and input nodes. We prove that the node adjacent to an input node in the input graph will appear in another control scheme, and the connected nodes in input graph have the same type in control, which they are either all possible input nodes or not. Furthermore, we find that the giant components emerge in the input graphs of many real networks, which provides a clear topological explanation of bifurcation phenomenon emerging in dense networks and promotes us to design an efficient method to alter the node type in control. The findings provide an insight into control principles of complex networks and offer a general mechanism to design a suitable control scheme for different purposes. PMID:27901102

  20. Phase transition of light on complex quantum networks.

    PubMed

    Halu, Arda; Garnerone, Silvano; Vezzani, Alessandro; Bianconi, Ginestra

    2013-02-01

    Recent advances in quantum optics and atomic physics allow for an unprecedented level of control over light-matter interactions, which can be exploited to investigate new physical phenomena. In this work we are interested in the role played by the topology of quantum networks describing coupled optical cavities and local atomic degrees of freedom. In particular, using a mean-field approximation, we study the phase diagram of the Jaynes-Cummings-Hubbard model on complex networks topologies, and we characterize the transition between a Mott-like phase of localized polaritons and a superfluid phase. We found that, for complex topologies, the phase diagram is nontrivial and well defined in the thermodynamic limit only if the hopping coefficient scales like the inverse of the maximal eigenvalue of the adjacency matrix of the network. Furthermore we provide numerical evidences that, for some complex network topologies, this scaling implies an asymptotically vanishing hopping coefficient in the limit of large network sizes. The latter result suggests the interesting possibility of observing quantum phase transitions of light on complex quantum networks even with very small couplings between the optical cavities.

  1. Multi-frequency complex network from time series for uncovering oil-water flow structure

    PubMed Central

    Gao, Zhong-Ke; Yang, Yu-Xuan; Fang, Peng-Cheng; Jin, Ning-De; Xia, Cheng-Yi; Hu, Li-Dan

    2015-01-01

    Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective. PMID:25649900

  2. Unveiling the hidden structure of complex stochastic biochemical networks

    SciTech Connect

    Valleriani, Angelo; Li, Xin; Kolomeisky, Anatoly B.

    2014-02-14

    Complex Markov models are widely used and powerful predictive tools to analyze stochastic biochemical processes. However, when the network of states is unknown, it is necessary to extract information from the data to partially build the network and estimate the values of the rates. The short-time behavior of the first-passage time distributions between two states in linear chains has been shown recently to behave as a power of time with an exponent equal to the number of intermediate states. For a general Markov model we derive the complete Taylor expansion of the first-passage time distribution between two arbitrary states. By combining algebraic methods and graph theory approaches it is shown that the first term of the Taylor expansion is determined by the shortest path from the initial state to the final state. When this path is unique, we prove that the coefficient of the first term can be written in terms of the product of the transition rates along the path. It is argued that the application of our results to first-return times may be used to estimate the dependence of rates on external parameters in experimentally measured time distributions.

  3. Network theory to understand microarray studies of complex diseases.

    PubMed

    Benson, Mikael; Breitling, Rainer

    2006-09-01

    Complex diseases, such as allergy, diabetes and obesity depend on altered interactions between multiple genes, rather than changes in a single causal gene. DNA microarray studies of a complex disease often implicate hundreds of genes in the pathogenesis. This indicates that many different mechanisms and pathways are involved. How can we understand such complexity? How can hypotheses be formulated and tested? One approach is to organize the data in network models and to analyze these in a top-down manner. Globally, networks in nature are often characterized by a small number of highly connected nodes, while the majority of nodes have few connections. The highly connected nodes serve as hubs that affect many other nodes. Such hubs have key roles in the network. In yeast cells, for example, deletion of highly connected proteins is associated with increased lethality, compared to deletion of less connected proteins. This suggests the biological relevance of networks. Moving down in the network structure, there may be sub-networks or modules with specific functions. These modules may be further dissected to analyze individual nodes. In the context of DNA microarray studies of complex diseases, gene-interaction networks may contain modules of co-regulated or interacting genes that have distinct biological functions. Such modules may be linked to specific gene polymorphisms, transcription factors, cellular functions and disease mechanisms. Genes that are reliably active only in the context of their modules can be considered markers for the activity of the modules and may thus be promising candidates for biomarkers or therapeutic targets. This review aims to give an introduction to network theory and how it can be applied to microarray studies of complex diseases.

  4. Theory of rumour spreading in complex social networks

    NASA Astrophysics Data System (ADS)

    Nekovee, M.; Moreno, Y.; Bianconi, G.; Marsili, M.

    2007-01-01

    We introduce a general stochastic model for the spread of rumours, and derive mean-field equations that describe the dynamics of the model on complex social networks (in particular, those mediated by the Internet). We use analytical and numerical solutions of these equations to examine the threshold behaviour and dynamics of the model on several models of such networks: random graphs, uncorrelated scale-free networks and scale-free networks with assortative degree correlations. We show that in both homogeneous networks and random graphs the model exhibits a critical threshold in the rumour spreading rate below which a rumour cannot propagate in the system. In the case of scale-free networks, on the other hand, this threshold becomes vanishingly small in the limit of infinite system size. We find that the initial rate at which a rumour spreads is much higher in scale-free networks than in random graphs, and that the rate at which the spreading proceeds on scale-free networks is further increased when assortative degree correlations are introduced. The impact of degree correlations on the final fraction of nodes that ever hears a rumour, however, depends on the interplay between network topology and the rumour spreading rate. Our results show that scale-free social networks are prone to the spreading of rumours, just as they are to the spreading of infections. They are relevant to the spreading dynamics of chain emails, viral advertising and large-scale information dissemination algorithms on the Internet.

  5. Synchronization in Complex Oscillator Networks and Smart Grids

    SciTech Connect

    Dorfler, Florian; Chertkov, Michael; Bullo, Francesco

    2012-07-24

    The emergence of synchronization in a network of coupled oscillators is a fascinating topic in various scientific disciplines. A coupled oscillator network is characterized by a population of heterogeneous oscillators and a graph describing the interaction among them. It is known that a strongly coupled and sufficiently homogeneous network synchronizes, but the exact threshold from incoherence to synchrony is unknown. Here we present a novel, concise, and closed-form condition for synchronization of the fully nonlinear, non-equilibrium, and dynamic network. Our synchronization condition can be stated elegantly in terms of the network topology and parameters, or equivalently in terms of an intuitive, linear, and static auxiliary system. Our results significantly improve upon the existing conditions advocated thus far, they are provably exact for various interesting network topologies and parameters, they are statistically correct for almost all networks, and they can be applied equally to synchronization phenomena arising in physics and biology as well as in engineered oscillator networks such as electric power networks. We illustrate the validity, the accuracy, and the practical applicability of our results in complex networks scenarios and in smart grid applications.

  6. Complex interdependent supply chain networks: Cascading failure and robustness

    NASA Astrophysics Data System (ADS)

    Tang, Liang; Jing, Ke; He, Jie; Stanley, H. Eugene

    2016-02-01

    A supply chain network is a typical interdependent network composed of an undirected cyber-layer network and a directed physical-layer network. To analyze the robustness of this complex interdependent supply chain network when it suffers from disruption events that can cause nodes to fail, we use a cascading failure process that focuses on load propagation. We consider load propagation via connectivity links as node failure spreads through one layer of an interdependent network, and we develop a priority redistribution strategy for failed loads subject to flow constraint. Using a giant component function and a one-to-one directed interdependence relation between nodes in a cyber-layer network and physical-layer network, we construct time-varied functional equations to quantify the dynamic process of failed loads propagation in an interdependent network. Finally, we conduct a numerical simulation for two cases, i.e., single node removal and multiple node removal at the initial disruption. The simulation results show that when we increase the number of removed nodes in an interdependent supply chain network its robustness undergoes a first-order discontinuous phase transition, and that even removing a small number of nodes will cause it to crash.

  7. Synchronization in complex oscillator networks and smart grids.

    PubMed

    Dörfler, Florian; Chertkov, Michael; Bullo, Francesco

    2013-02-05

    The emergence of synchronization in a network of coupled oscillators is a fascinating topic in various scientific disciplines. A widely adopted model of a coupled oscillator network is characterized by a population of heterogeneous phase oscillators, a graph describing the interaction among them, and diffusive and sinusoidal coupling. It is known that a strongly coupled and sufficiently homogeneous network synchronizes, but the exact threshold from incoherence to synchrony is unknown. Here, we present a unique, concise, and closed-form condition for synchronization of the fully nonlinear, nonequilibrium, and dynamic network. Our synchronization condition can be stated elegantly in terms of the network topology and parameters or equivalently in terms of an intuitive, linear, and static auxiliary system. Our results significantly improve upon the existing conditions advocated thus far, they are provably exact for various interesting network topologies and parameters; they are statistically correct for almost all networks; and they can be applied equally to synchronization phenomena arising in physics and biology as well as in engineered oscillator networks, such as electrical power networks. We illustrate the validity, the accuracy, and the practical applicability of our results in complex network scenarios and in smart grid applications.

  8. Self-similarity and scaling theory of complex networks

    NASA Astrophysics Data System (ADS)

    Song, Chaoming

    Scale-free networks have been studied extensively due to their relevance to many real systems as diverse as the World Wide Web (WWW), the Internet, biological and social networks. We present a novel approach to the analysis of scale-free networks, revealing that their structure is self-similar. This result is achieved by the application of a renormalization procedure which coarse-grains the system into boxes containing nodes within a given "size". Concurrently, we identify a power-law relation between the number of boxes needed to cover the network and the size of the box defining a self-similar exponent, which classifies fractal and non-fractal networks. By using the concept of renormalization as a mechanism for the growth of fractal and non-fractal modular networks, we show that the key principle that gives rise to the fractal architecture of networks is a strong effective "repulsion" between the most connected nodes (hubs) on all length scales, rendering them very dispersed. We show that a robust network comprised of functional modules, such as a cellular network, necessitates a fractal topology, suggestive of a evolutionary drive for their existence. These fundamental properties help to understand the emergence of the scale-free property in complex networks.

  9. Generalized synchronization of complex dynamical networks via impulsive control.

    PubMed

    Chen, Juan; Lu, Jun-An; Wu, Xiaoqun; Zheng, Wei Xing

    2009-12-01

    This paper investigates the generalized synchronization (GS) of two typical complex dynamical networks, small-world networks and scale-free networks, in terms of impulsive control strategy. By applying the auxiliary-system approach to networks, we demonstrate theoretically that for any given coupling strength, GS can take place in complex dynamical networks consisting of nonidentical systems. Particularly, for Barabasi-Albert scale-free networks, we look into the relations between GS error and topological parameter m, which denotes the number of edges linking to a new node at each time step, and find out that GS speeds up with increasing m. And for Newman-Watts small-world networks, the time needed to achieve GS decreases as the probability of adding random edges increases. We further reveal how node dynamics affects GS speed on both small-world and scale-free networks. Finally, we analyze how the development of GS depends on impulsive control gains. Some abnormal but interesting phenomena regarding the GS process are also found in simulations.

  10. Synchronization in complex delayed dynamical networks with nonsymmetric coupling

    NASA Astrophysics Data System (ADS)

    Wu, Jianshe; Jiao, Licheng

    2007-12-01

    A new general complex delayed dynamical network model with nonsymmetric coupling is introduced, and then we investigate its synchronization phenomena. Several synchronization criteria for delay-independent and delay-dependent synchronization are provided which generalize some previous results. The matrix Jordan canonical formalization method is used instead of the matrix diagonalization method, so in our synchronization criteria, the coupling configuration matrix of the network does not required to be diagonalizable and may have complex eigenvalues. Especially, we show clearly that the synchronizability of a delayed dynamical network is not always characterized by the second-largest eigenvalue even though all the eigenvalues of the coupling configuration matrix are real. Furthermore, the effects of time-delay on synchronizability of networks with unidirectional coupling are studied under some typical network structures. The results are illustrated by delayed networks in which each node is a two-dimensional limit cycle oscillator system consisting of a two-cell cellular neural network, numerical simulations show that these networks can realize synchronization with smaller time-delay, and will lose synchronization when the time-delay increase larger than a threshold.

  11. 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.

  12. Effective centrality and explosive synchronization in complex networks.

    PubMed

    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.

  13. Optimal navigation for characterizing the role of the nodes in complex networks

    NASA Astrophysics Data System (ADS)

    Cajueiro, Daniel O.

    2010-05-01

    In this paper, we explore how the approach of optimal navigation (Cajueiro (2009) [33]) can be used to evaluate the centrality of a node and to characterize its role in a network. Using the subway network of Boston and the London rapid transit rail as proxies for complex networks, we show that the centrality measures inherited from the approach of optimal navigation may be considered if one desires to evaluate the centrality of the nodes using other pieces of information beyond the geometric properties of the network. Furthermore, evaluating the correlations between these inherited measures and classical measures of centralities such as the degree of a node and the characteristic path length of a node, we have found two classes of results. While for the London rapid transit rail, these inherited measures can be easily explained by these classical measures of centrality, for the Boston underground transportation system we have found nontrivial results.

  14. Metastable localization of diseases in complex networks

    NASA Astrophysics Data System (ADS)

    Ferreira, R. S.; da Costa, R. A.; Dorogovtsev, S. N.; Mendes, J. F. F.

    2016-12-01

    We describe the phenomenon of localization in the epidemic susceptible-infective-susceptible model on highly heterogeneous networks in which strongly connected nodes (hubs) play the role of centers of localization. We find that in this model the localized states below the epidemic threshold are metastable. The longevity and scale of the metastable outbreaks do not show a sharp localization transition; instead there is a smooth crossover from localized to delocalized states as we approach the epidemic threshold from below. Analyzing these long-lasting local outbreaks for a random regular graph with a hub, we show how this localization can be detected from the shape of the distribution of the number of infective nodes.

  15. Network geometry with flavor: From complexity to quantum geometry.

    PubMed

    Bianconi, Ginestra; Rahmede, Christoph

    2016-03-01

    Network geometry is attracting increasing attention because it has a wide range of applications, ranging from data mining to routing protocols in the Internet. At the same time advances in the understanding of the geometrical properties of networks are essential for further progress in quantum gravity. In network geometry, simplicial complexes describing the interaction between two or more nodes play a special role. In fact these structures can be used to discretize a geometrical d-dimensional space, and for this reason they have already been widely used in quantum gravity. Here we introduce the network geometry with flavor s=-1,0,1 (NGF) describing simplicial complexes defined in arbitrary dimension d and evolving by a nonequilibrium dynamics. The NGF can generate discrete geometries of different natures, ranging from chains and higher-dimensional manifolds to scale-free networks with small-world properties, scale-free degree distribution, and nontrivial community structure. The NGF admits as limiting cases both the Bianconi-Barabási models for complex networks, the stochastic Apollonian network, and the recently introduced model for complex quantum network manifolds. The thermodynamic properties of NGF reveal that NGF obeys a generalized area law opening a new scenario for formulating its coarse-grained limit. The structure of NGF is strongly dependent on the dimensionality d. In d=1 NGFs grow complex networks for which the preferential attachment mechanism is necessary in order to obtain a scale-free degree distribution. Instead, for NGF with dimension d>1 it is not necessary to have an explicit preferential attachment rule to generate scale-free topologies. We also show that NGF admits a quantum mechanical description in terms of associated quantum network states. Quantum network states evolve by a Markovian dynamics and a quantum network state at time t encodes all possible NGF evolutions up to time t. Interestingly the NGF remains fully classical but its

  16. Network geometry with flavor: From complexity to quantum geometry

    NASA Astrophysics Data System (ADS)

    Bianconi, Ginestra; Rahmede, Christoph

    2016-03-01

    Network geometry is attracting increasing attention because it has a wide range of applications, ranging from data mining to routing protocols in the Internet. At the same time advances in the understanding of the geometrical properties of networks are essential for further progress in quantum gravity. In network geometry, simplicial complexes describing the interaction between two or more nodes play a special role. In fact these structures can be used to discretize a geometrical d -dimensional space, and for this reason they have already been widely used in quantum gravity. Here we introduce the network geometry with flavor s =-1 ,0 ,1 (NGF) describing simplicial complexes defined in arbitrary dimension d and evolving by a nonequilibrium dynamics. The NGF can generate discrete geometries of different natures, ranging from chains and higher-dimensional manifolds to scale-free networks with small-world properties, scale-free degree distribution, and nontrivial community structure. The NGF admits as limiting cases both the Bianconi-Barabási models for complex networks, the stochastic Apollonian network, and the recently introduced model for complex quantum network manifolds. The thermodynamic properties of NGF reveal that NGF obeys a generalized area law opening a new scenario for formulating its coarse-grained limit. The structure of NGF is strongly dependent on the dimensionality d . In d =1 NGFs grow complex networks for which the preferential attachment mechanism is necessary in order to obtain a scale-free degree distribution. Instead, for NGF with dimension d >1 it is not necessary to have an explicit preferential attachment rule to generate scale-free topologies. We also show that NGF admits a quantum mechanical description in terms of associated quantum network states. Quantum network states evolve by a Markovian dynamics and a quantum network state at time t encodes all possible NGF evolutions up to time t . Interestingly the NGF remains fully classical but

  17. Complex networks identify spatial patterns of extreme rainfall of the South American monsoon system

    NASA Astrophysics Data System (ADS)

    Boers, Niklas; Bokkhagen, Bodo; Marwan, Norbert; Kurths, Jürgen; Marengo, Jose

    2014-05-01

    In this study, we investigate the spatial characteristics of extreme rainfall synchronicity of the South American Monsoon System (SAMS) by means of Complex Networks. We first show how this approach leads to the identification of linkages between large-scale atmospheric conditions and natural hazards occurring at the earth's surface. Thereafter, we exemplify how our methodology can be used to compare different datasets and to test the performance of climate models. In recent years, complex networks have attracted great attention for analyzing the spatial characteristics of interrelations of various time series. Outstanding examples in this context are functional brain networks as well as so-called climate networks. In most approaches, the basic idea is to represent time series at different locations by network nodes, which will be connected by network links if the corresponding time series behave similar. Information on the spatial characteristics of these similarities can be inferred by network measures quantifying different aspects of the networks' topology. By combining several network measures and interpreting them in a climatic context, we investigate climatic linkages and classify the spatial characteristics of extreme rainfall synchronicity. Although our approach is based on only one variable (high spatiotemporal resolution rainfall), it reveals the most important features of the SAMS, such as the main moisture pathways, areas with frequent development of Mesoscale Convective Systems, and the major convergence zones. We will show that these features are only partially reproduced by reanalysis and (regional and global) climate model data.

  18. Complex network theory, streamflow, and hydrometric monitoring system design

    NASA Astrophysics Data System (ADS)

    Halverson, M. J.; Fleming, S. W.

    2015-07-01

    Network theory is applied to an array of streamflow gauges located in the Coast Mountains of British Columbia (BC) and Yukon, Canada. The goal of the analysis is to assess whether insights from this branch of mathematical graph theory can be meaningfully applied to hydrometric data, and, more specifically, whether it may help guide decisions concerning stream gauge placement so that the full complexity of the regional hydrology is efficiently captured. The streamflow data, when represented as a complex network, have a global clustering coefficient and average shortest path length consistent with small-world networks, which are a class of stable and efficient networks common in nature, but the observed degree distribution did not clearly indicate a scale-free network. Stability helps ensure that the network is robust to the loss of nodes; in the context of a streamflow network, stability is interpreted as insensitivity to station removal at random. Community structure is also evident in the streamflow network. A network theoretic community detection algorithm identified separate communities, each of which appears to be defined by the combination of its median seasonal flow regime (pluvial, nival, hybrid, or glacial, which in this region in turn mainly reflects basin elevation) and geographic proximity to other communities (reflecting shared or different daily meteorological forcing). Furthermore, betweenness analyses suggest a handful of key stations which serve as bridges between communities and might be highly valued. We propose that an idealized sampling network should sample high-betweenness stations, small-membership communities which are by definition rare or undersampled relative to other communities, and index stations having large numbers of intracommunity links, while retaining some degree of redundancy to maintain network robustness.

  19. Energy balance for analysis of complex metabolic networks.

    PubMed Central

    Beard, Daniel A; Liang, Shou-dan; Qian, Hong

    2002-01-01

    Predicting behavior of large-scale biochemical networks represents one of the greatest challenges of bioinformatics and computational biology. Computational tools for predicting fluxes in biochemical networks are applied in the fields of integrated and systems biology, bioinformatics, and genomics, and to aid in drug discovery and identification of potential drug targets. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are promising tools for the analysis of large complex networks. Here we introduce energy balance analysis (EBA)--the theory and methodology for enforcing the laws of thermodynamics in such simulations--making the results more physically realistic and revealing greater insight into the regulatory and control mechanisms operating in complex large-scale systems. We show that EBA eliminates thermodynamically infeasible results associated with FBA. PMID:12080101

  20. Renormalization Group for Critical Phenomena in Complex Networks

    PubMed Central

    Boettcher, S.; Brunson, C. T.

    2011-01-01

    We discuss the behavior of statistical models on a novel class of complex “Hanoi” networks. Such modeling is often the cornerstone for the understanding of many dynamical processes in complex networks. Hanoi networks are special because they integrate small-world hierarchies common to many social and economical structures with the inevitable geometry of the real world these structures exist in. In addition, their design allows exact results to be obtained with the venerable renormalization group (RG). Our treatment will provide a detailed, pedagogical introduction to RG. In particular, we will study the Ising model with RG, for which the fixed points are determined and the RG flow is analyzed. We show that the small-world bonds result in non-universal behavior. It is shown that a diversity of different behaviors can be observed with seemingly small changes in the structure of hierarchical networks generally, and we provide a general theory to describe our findings. PMID:22194725

  1. Improved efficient routing strategy on two-layer complex networks

    NASA Astrophysics Data System (ADS)

    Ma, Jinlong; Han, Weizhan; Guo, Qing; Zhang, Shuai; Wang, Junfang; Wang, Zhihao

    2016-10-01

    The traffic dynamics of multi-layer networks has become a hot research topic since many networks are comprised of two or more layers of subnetworks. Due to its low traffic capacity, the traditional shortest path routing (SPR) protocol is susceptible to congestion on two-layer complex networks. In this paper, we propose an efficient routing strategy named improved global awareness routing (IGAR) strategy which is based on the betweenness centrality of nodes in the two layers. With the proposed strategy, the routing paths can bypass hub nodes of both layers to enhance the transport efficiency. Simulation results show that the IGAR strategy can bring much better traffic capacity than the SPR and the global awareness routing (GAR) strategies. Because of the significantly improved traffic performance, this study is helpful to alleviate congestion of the two-layer complex networks.

  2. Complex quantum networks: From universal breakdown to optimal transport.

    PubMed

    Mülken, Oliver; Dolgushev, Maxim; Galiceanu, Mircea

    2016-02-01

    We study the transport efficiency of excitations on complex quantum networks with loops. For this we consider sequentially growing networks with different topologies of the sequential subgraphs. This can lead either to a universal complete breakdown of transport for complete-graph-like sequential subgraphs or to optimal transport for ringlike sequential subgraphs. The transition to optimal transport can be triggered by systematically reducing the number of loops of complete-graph-like sequential subgraphs in a small-world procedure. These effects are explained on the basis of the spectral properties of the network's Hamiltonian. Our theoretical considerations are supported by numerical Monte Carlo simulations for complex quantum networks with a scale-free size distribution of sequential subgraphs and a small-world-type transition to optimal transport.

  3. Complex quantum networks: From universal breakdown to optimal transport

    NASA Astrophysics Data System (ADS)

    Mülken, Oliver; Dolgushev, Maxim; Galiceanu, Mircea

    2016-02-01

    We study the transport efficiency of excitations on complex quantum networks with loops. For this we consider sequentially growing networks with different topologies of the sequential subgraphs. This can lead either to a universal complete breakdown of transport for complete-graph-like sequential subgraphs or to optimal transport for ringlike sequential subgraphs. The transition to optimal transport can be triggered by systematically reducing the number of loops of complete-graph-like sequential subgraphs in a small-world procedure. These effects are explained on the basis of the spectral properties of the network's Hamiltonian. Our theoretical considerations are supported by numerical Monte Carlo simulations for complex quantum networks with a scale-free size distribution of sequential subgraphs and a small-world-type transition to optimal transport.

  4. Topological interactive analysis of power system and its communication module: A complex network approach

    NASA Astrophysics Data System (ADS)

    Hu, Jianqiang; Yu, Jie; Cao, Jinde; Ni, Ming; Yu, Wenjie

    2014-12-01

    Power system and its communication system, which can be called a cyber-physical system, are interconnected and interdependent on each other. This paper considers the interaction problem between power system and its communication module from the perspective of the topological structure. Firstly, some structural properties and centrality measures of complex networks are briefly reviewed. Furthermore, novel interactive measures are proposed to describe the interactive system in terms of topologies. Finally, based on these metrics, the statistical properties and the interactive relationships of the main power system and its communication module (abstracted as two complex heterogeneous networks) of one province in China are investigated.

  5. Fixed-time synchronization of multi-links complex network

    NASA Astrophysics Data System (ADS)

    Zhao, Hui; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zheng, Mingwen

    2017-01-01

    In the paper, the fixed-time and finite-time synchronizations of multi-links complex network are investigated. Compared with finite-time synchronization, the settling time of fixed-time synchronization is independent of initial conditions. For uncertain multi-links complex networks, this paper further analyzes synchronization mechanism and unknown parameters based on the drive-response concept and finite-time stability theory. Novel synchronization control criteria and the result of parameters identification are, respectively, obtained in a finite time by utilizing Lyapunov function and linear matrix inequality (LMI). Besides, we give other two versions of finite-time synchronization and parameters identification for uncertain multi-links complex network with impulsive control input. Finally, numerical examples are given to illustrate the effectiveness of our theoretical results.

  6. Exponential Stability of Complex-Valued Memristive Recurrent Neural Networks.

    PubMed

    Wang, Huamin; Duan, Shukai; Huang, Tingwen; Wang, Lidan; Li, Chuandong

    2017-03-01

    In this brief, we establish a novel complex-valued memristive recurrent neural network (CVMRNN) to study its stability. As a generalization of real-valued memristive neural networks, CVMRNN can be separated into real and imaginary parts. By means of M -matrix and Lyapunov function, the existence, uniqueness, and exponential stability of the equilibrium point for CVMRNNs are investigated, and sufficient conditions are presented. Finally, the effectiveness of obtained results is illustrated by two numerical examples.

  7. Stability of similarity measurements for bipartite networks

    NASA Astrophysics Data System (ADS)

    Liu, Jian-Guo; Hou, Lei; Pan, Xue; Guo, Qiang; Zhou, Tao

    2016-01-01

    Similarity is a fundamental measure in network analyses and machine learning algorithms, with wide applications ranging from personalized recommendation to socio-economic dynamics. We argue that an effective similarity measurement should guarantee the stability even under some information loss. With six bipartite networks, we investigate the stabilities of fifteen similarity measurements by comparing the similarity matrixes of two data samples which are randomly divided from original data sets. Results show that, the fifteen measurements can be well classified into three clusters according to their stabilities, and measurements in the same cluster have similar mathematical definitions. In addition, we develop a top-n-stability method for personalized recommendation, and find that the unstable similarities would recommend false information to users, and the performance of recommendation would be largely improved by using stable similarity measurements. This work provides a novel dimension to analyze and evaluate similarity measurements, which can further find applications in link prediction, personalized recommendation, clustering algorithms, community detection and so on.

  8. Stability of similarity measurements for bipartite networks

    PubMed Central

    Liu, Jian-Guo; Hou, Lei; Pan, Xue; Guo, Qiang; Zhou, Tao

    2016-01-01

    Similarity is a fundamental measure in network analyses and machine learning algorithms, with wide applications ranging from personalized recommendation to socio-economic dynamics. We argue that an effective similarity measurement should guarantee the stability even under some information loss. With six bipartite networks, we investigate the stabilities of fifteen similarity measurements by comparing the similarity matrixes of two data samples which are randomly divided from original data sets. Results show that, the fifteen measurements can be well classified into three clusters according to their stabilities, and measurements in the same cluster have similar mathematical definitions. In addition, we develop a top-n-stability method for personalized recommendation, and find that the unstable similarities would recommend false information to users, and the performance of recommendation would be largely improved by using stable similarity measurements. This work provides a novel dimension to analyze and evaluate similarity measurements, which can further find applications in link prediction, personalized recommendation, clustering algorithms, community detection and so on. PMID:26725688

  9. Stability of similarity measurements for bipartite networks.

    PubMed

    Liu, Jian-Guo; Hou, Lei; Pan, Xue; Guo, Qiang; Zhou, Tao

    2016-01-04

    Similarity is a fundamental measure in network analyses and machine learning algorithms, with wide applications ranging from personalized recommendation to socio-economic dynamics. We argue that an effective similarity measurement should guarantee the stability even under some information loss. With six bipartite networks, we investigate the stabilities of fifteen similarity measurements by comparing the similarity matrixes of two data samples which are randomly divided from original data sets. Results show that, the fifteen measurements can be well classified into three clusters according to their stabilities, and measurements in the same cluster have similar mathematical definitions. In addition, we develop a top-n-stability method for personalized recommendation, and find that the unstable similarities would recommend false information to users, and the performance of recommendation would be largely improved by using stable similarity measurements. This work provides a novel dimension to analyze and evaluate similarity measurements, which can further find applications in link prediction, personalized recommendation, clustering algorithms, community detection and so on.

  10. Continuous symmetry measures for complex symmetry group.

    PubMed

    Dryzun, Chaim

    2014-04-05

    Symmetry is a fundamental property of nature, used extensively in physics, chemistry, and biology. The Continuous symmetry measures (CSM) is a method for estimating the deviation of a given system from having a certain perfect symmetry, which enables us to formulate quantitative relation between symmetry and other physical properties. Analytical procedures for calculating the CSM of all simple cyclic point groups are available for several years. Here, we present a methodology for calculating the CSM of any complex point group, including the dihedral, tetrahedral, octahedral, and icosahedral symmetry groups. We present the method and analyze its performances and errors. We also introduce an analytical method for calculating the CSM of the linear symmetry groups. As an example, we apply these methods for examining the symmetry of water, the symmetry maps of AB4 complexes, and the symmetry of several Lennard-Jones clusters.

  11. Characterizing complex networks through statistics of Möbius transformations

    NASA Astrophysics Data System (ADS)

    Jaćimović, Vladimir; Crnkić, Aladin

    2017-04-01

    It is well-known now that dynamics of large populations of globally (all-to-all) coupled oscillators can be reduced to low-dimensional submanifolds (WS transformation and OA ansatz). Marvel et al. (2009) described an intriguing algebraic structure standing behind this reduction: oscillators evolve by the action of the group of Möbius transformations. Of course, dynamics in complex networks of coupled oscillators is highly complex and not reducible. Still, closer look unveils that even in complex networks some (possibly overlapping) groups of oscillators evolve by Möbius transformations. In this paper, we study properties of the network by identifying Möbius transformations in the dynamics of oscillators. This enables us to introduce some new (statistical) concepts that characterize the network. In particular, the notion of coherence of the network (or subnetwork) is proposed. This conceptual approach is meaningful for the broad class of networks, including those with time-delayed, noisy or mixed interactions. In this paper, several simple (random) graphs are studied illustrating the meaning of the concepts introduced in the paper.

  12. Power-Hop: A Pervasive Observation for Real Complex Networks

    PubMed Central

    Papalexakis, Evangelos; Hooi, Bryan; Pelechrinis, Konstantinos; Faloutsos, Christos

    2016-01-01

    Complex networks have been shown to exhibit universal properties, with one of the most consistent patterns being the scale-free degree distribution, but are there regularities obeyed by the r-hop neighborhood in real networks? We answer this question by identifying another power-law pattern that describes the relationship between the fractions of node pairs C(r) within r hops and the hop count r. This scale-free distribution is pervasive and describes a large variety of networks, ranging from social and urban to technological and biological networks. In particular, inspired by the definition of the fractal correlation dimension D2 on a point-set, we consider the hop-count r to be the underlying distance metric between two vertices of the network, and we examine the scaling of C(r) with r. We find that this relationship follows a power-law in real networks within the range 2 ≤ r ≤ d, where d is the effective diameter of the network, that is, the 90-th percentile distance. We term this relationship as power-hop and the corresponding power-law exponent as power-hop exponent h. We provide theoretical justification for this pattern under successful existing network models, while we analyze a large set of real and synthetic network datasets and we show the pervasiveness of the power-hop. PMID:26974560

  13. Complex and unexpected dynamics in simple genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Borg, Yanika; Ullner, Ekkehard; Alagha, Afnan; Alsaedi, Ahmed; Nesbeth, Darren; Zaikin, Alexey

    2014-03-01

    One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

  14. Knowledge Discovery in Spectral Data by Means of Complex Networks

    PubMed Central

    Zanin, Massimiliano; Papo, David; Solís, José Luis González; Espinosa, Juan Carlos Martínez; Frausto-Reyes, Claudio; Anda, Pascual Palomares; Sevilla-Escoboza, Ricardo; Boccaletti, Stefano; Menasalvas, Ernestina; Sousa, Pedro

    2013-01-01

    In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease. PMID:24957895

  15. Symmetries, Cluster Synchronization, and Isolated Desynchronization in Complex Networks

    NASA Astrophysics Data System (ADS)

    Pecora, Louis

    2015-03-01

    Many networks are observed to produce patterns of synchronized clusters, but it has been difficult to predict these clusters in general or understand the conditions for their formation. We show the intimate connection between network symmetry and cluster synchronization. We apply computational group theory to reveal the clusters and determine their stability. In complex networks the symmetries can number in the millions, billions, and more. The connection between symmetry and cluster synchronization is experimentally explored using an electro-optic network. We observe and explain a surprising and common phenomenon (isolated desynchronization) in which some clusters lose synchrony while leaving others connected to them synchronized. We show the isolated desynchronization is intimately related to the decomposition of the group of symmetries into subgroups. The results could guide the design of new power grid systems or lead to new understanding of the dynamical behavior of networks ranging from neural to social.

  16. Modeling Pedestrian's Conformity Violation Behavior: A Complex Network Based Approach

    PubMed Central

    Zhou, Zhuping; Hu, Qizhou; Wang, Wei

    2014-01-01

    Pedestrian injuries and fatalities present a problem all over the world. Pedestrian conformity violation behaviors, which lead to many pedestrian crashes, are common phenomena at the signalized intersections in China. The concepts and metrics of complex networks are applied to analyze the structural characteristics and evolution rules of pedestrian network about the conformity violation crossings. First, a network of pedestrians crossing the street is established, and the network's degree distributions are analyzed. Then, by using the basic idea of SI model, a spreading model of pedestrian illegal crossing behavior is proposed. Finally, through simulation analysis, pedestrian's illegal crossing behavior trends are obtained in different network structures and different spreading rates. Some conclusions are drawn: as the waiting time increases, more pedestrians will join in the violation crossing once a pedestrian crosses on red firstly. And pedestrian's conformity violation behavior will increase as the spreading rate increases. PMID:25530755

  17. Modeling pedestrian's conformity violation behavior: a complex network based approach.

    PubMed

    Zhou, Zhuping; Hu, Qizhou; Wang, Wei

    2014-01-01

    Pedestrian injuries and fatalities present a problem all over the world. Pedestrian conformity violation behaviors, which lead to many pedestrian crashes, are common phenomena at the signalized intersections in China. The concepts and metrics of complex networks are applied to analyze the structural characteristics and evolution rules of pedestrian network about the conformity violation crossings. First, a network of pedestrians crossing the street is established, and the network's degree distributions are analyzed. Then, by using the basic idea of SI model, a spreading model of pedestrian illegal crossing behavior is proposed. Finally, through simulation analysis, pedestrian's illegal crossing behavior trends are obtained in different network structures and different spreading rates. Some conclusions are drawn: as the waiting time increases, more pedestrians will join in the violation crossing once a pedestrian crosses on red firstly. And pedestrian's conformity violation behavior will increase as the spreading rate increases.

  18. Network-Thinking: Graphs to Analyze Microbial Complexity and Evolution

    PubMed Central

    Corel, Eduardo; Lopez, Philippe; Méheust, Raphaël; Bapteste, Eric

    2016-01-01

    The tree model and tree-based methods have played a major, fruitful role in evolutionary studies. However, with the increasing realization of the quantitative and qualitative importance of reticulate evolutionary processes, affecting all levels of biological organization, complementary network-based models and methods are now flourishing, inviting evolutionary biology to experience a network-thinking era. We show how relatively recent comers in this field of study, that is, sequence-similarity networks, genome networks, and gene families–genomes bipartite graphs, already allow for a significantly enhanced usage of molecular datasets in comparative studies. Analyses of these networks provide tools for tackling a multitude of complex phenomena, including the evolution of gene transfer, composite genes and genomes, evolutionary transitions, and holobionts. PMID:26774999

  19. A two-level complex network model and its application

    NASA Astrophysics Data System (ADS)

    Yang, Jianmei; Wang, Wenjie; Chen, Guanrong

    2009-06-01

    This paper investigates the competitive relationship and rivalry of industrial markets, using Chinese household electrical appliance firms as a platform for the study. The common complex network models belong to one-level networks in layered classification, while this paper formulates and evaluates a new two-level network model, in which the first level is the whole unweighted-undirected network useful for macro-analyzing the industrial market structure while the second level is a local weighted-directed network capable of micro-analyzing the inter-firm rivalry in the market. It is believed that the relationship is determined by objective factors whereas the action is rather subjective, and the idea in this paper lies in that the objective relationship and the subjective action subjected to this relationship are being simultaneously considered but at deferent levels of the model which may be applicable to many real applications.

  20. Effect of Congestion Costs on Shortest Paths Through Complex Networks

    NASA Astrophysics Data System (ADS)

    Ashton, Douglas J.; Jarrett, Timothy C.; Johnson, Neil F.

    2005-02-01

    We analyze analytically the effect of congestion costs within a physically relevant, yet exactly solvable, network model featuring central hubs. These costs lead to a competition between centralized and decentralized transport pathways. In stark contrast to conventional no-cost networks, there now exists an optimal number of connections to the central hub in order to minimize the shortest path. Our results shed light on an open problem in biology, informatics, and sociology, concerning the extent to which decentralized versus centralized design benefits real-world complex networks.

  1. Effect of congestion costs on shortest paths through complex networks.

    PubMed

    Ashton, Douglas J; Jarrett, Timothy C; Johnson, Neil F

    2005-02-11

    We analyze analytically the effect of congestion costs within a physically relevant, yet exactly solvable, network model featuring central hubs. These costs lead to a competition between centralized and decentralized transport pathways. In stark contrast to conventional no-cost networks, there now exists an optimal number of connections to the central hub in order to minimize the shortest path. Our results shed light on an open problem in biology, informatics, and sociology, concerning the extent to which decentralized versus centralized design benefits real-world complex networks.

  2. Modeling Uncertainty and Its Implications in Complex Interdependent Networks

    DTIC Science & Technology

    2016-04-30

    Networks Anita Raja, Professor, The Cooper Union Mohammad Rashedul Hasan, Assistant Professor of Practice, UNL Robert Flowe, Office of Acquisition...êÅÜ=mêçÖê~ãW= `êÉ~íáåÖ=póåÉêÖó=Ñçê=fåÑçêãÉÇ=`Ü~åÖÉ= - 86 - Panel 13. Setting Requirements and Managing Risk in Complex, Networked Projects Thursday...Army for Acquisition, Logistics and Technology Acquisition in a World of Joint Capabilities: Methods for Understanding Cross-Organizational Network

  3. Synchronization of complex networks coupled by periodically intermittent noise

    NASA Astrophysics Data System (ADS)

    Li, Shuang; Yan, Huiyun; Li, Jiaorui

    2016-04-01

    Noise is ubiquitous in real systems, so it is important to investigate the effects of noise on the network system. In this paper, the synchronization of complex network coupled by periodically intermittent noise is investigated and a sufficient condition of noise-induced synchronization is obtained analytically via stability theory of stochastic differential equation. The sufficient condition provides a theoretical reference for the analysis of the impact of coupling noise intensity, duration, coupled oscillator number and other parameters on the synchronization behavior. As examples, Rossler-like and Lorenz network systems are presented to verify the theoretical result.

  4. Complex Networks - A Key to Understanding Brain Function

    SciTech Connect

    Olaf Sporns

    2008-01-23

    The brain is a complex network of neurons, engaging in spontaneous and evoked activity that is thought to be the main substrate of mental life.  How this complex system works together to process information and generate coherent cognitive states, even consciousness, is not yet well understood.  In my talk I will review recent studies that have revealed characteristic structural and functional attributes of brain networks, and discuss efforts to build computational models of the brain that are informed by our growing knowledge of brain anatomy and physiology.

  5. Pinning control of complex networks via edge snapping

    NASA Astrophysics Data System (ADS)

    DeLellis, P.; di Bernardo, M.; Porfiri, M.

    2011-09-01

    In this paper, we propose a hierarchy of novel decentralized adaptive pinning strategies for controlled synchronization of complex networks. This hierarchy addresses the fundamental need of selecting the sites to pin through a fully decentralized approach based on edge snapping. Specifically, we present three different strategies of increasing complexity which use a combination of network evolution and adaptation of the coupling and control gains. Theoretical results are complemented by extensive numerical investigations of the performance of the proposed strategies on a set of testbed examples.

  6. Complex Networks - A Key to Understanding Brain Function

    SciTech Connect

    Sporns, Olaf

    2008-01-23

    The brain is a complex network of neurons, engaging in spontaneous and evoked activity that is thought to be the main substrate of mental life. How this complex system works together to process information and generate coherent cognitive states, even consciousness, is not yet well understood. In my talk I will review recent studies that have revealed characteristic structural and functional attributes of brain networks, and discuss efforts to build computational models of the brain that are informed by our growing knowledge of brain anatomy and physiology.

  7. Complex Networks - A Key to Understanding Brain Function

    ScienceCinema

    Olaf Sporns

    2016-07-12

    The brain is a complex network of neurons, engaging in spontaneous and evoked activity that is thought to be the main substrate of mental life.  How this complex system works together to process information and generate coherent cognitive states, even consciousness, is not yet well understood.  In my talk I will review recent studies that have revealed characteristic structural and functional attributes of brain networks, and discuss efforts to build computational models of the brain that are informed by our growing knowledge of brain anatomy and physiology.

  8. A computational model for cancer growth by using complex networks

    NASA Astrophysics Data System (ADS)

    Galvão, Viviane; Miranda, José G. V.

    2008-09-01

    In this work we propose a computational model to investigate the proliferation of cancerous cell by using complex networks. In our model the network represents the structure of available space in the cancer propagation. The computational scheme considers a cancerous cell randomly included in the complex network. When the system evolves the cells can assume three states: proliferative, non-proliferative, and necrotic. Our results were compared with experimental data obtained from three human lung carcinoma cell lines. The computational simulations show that the cancerous cells have a Gompertzian growth. Also, our model simulates the formation of necrosis, increase of density, and resources diffusion to regions of lower nutrient concentration. We obtain that the cancer growth is very similar in random and small-world networks. On the other hand, the topological structure of the small-world network is more affected. The scale-free network has the largest rates of cancer growth due to hub formation. Finally, our results indicate that for different average degrees the rate of cancer growth is related to the available space in the network.

  9. Hierarchicality of Trade Flow Networks Reveals Complexity of Products

    PubMed Central

    Shi, Peiteng; Zhang, Jiang; Yang, Bo; Luo, Jingfei

    2014-01-01

    With globalization, countries are more connected than before by trading flows, which amounts to at least trillion dollars today. Interestingly, around percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely. PMID:24905753

  10. Hierarchicality of trade flow networks reveals complexity of products.

    PubMed

    Shi, Peiteng; Zhang, Jiang; Yang, Bo; Luo, Jingfei

    2014-01-01

    With globalization, countries are more connected than before by trading flows, which amounts to at least 36 trillion dollars today. Interestingly, around 30-60 percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent η can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely.

  11. Complex networks, streamflow, and hydrometric monitoring system design

    NASA Astrophysics Data System (ADS)

    Halverson, M.; Fleming, S.

    2014-12-01

    Network theory is applied to an array of streamflow gauges located in the Coast Mountains of British Columbia and Yukon, Canada. The goal of the analysis is to assess whether insights from this branch of mathematical graph theory can be meaningfully applied to hydrometric data, and more specifically, whether it may help guide decisions concerning stream gauge placement so that the full complexity of the regional hydrology is efficiently captured. The streamflow data, when represented as a complex network, has a global clustering coefficient and average shortest path length consistent with small-world networks, which are a class of stable and efficient networks common in nature, but the results did not clearly suggest a scale-free network. Stability helps ensure that the network is robust to the loss of nodes; in the context of a streamflow network, stability is interpreted as insensitivity to station removal at random. Community structure is also evident in the streamflow network. A community detection algorithm identified 10 separate communities, each of which appears to be defined by the combination of its median seasonal flow regime (pluvial, nival, hybrid, or glacial, which in this region in turn mainly reflects basin elevation) and geographic proximity to other communities (reflecting shared or different daily meteorological forcing). Betweenness analyses additionally suggest a handful of key stations which serve as bridges between communities and might therefore be highly valued. We propose that an idealized sampling network should sample high-betweenness stations, as well as small-membership communities which are by definition rare or undersampled relative to other communities, while retaining some degree of redundancy to maintain network robustness.

  12. Detecting community structure in complex networks using an interaction optimization process

    NASA Astrophysics Data System (ADS)

    Kim, Paul; Kim, Sangwook

    2017-01-01

    Most complex networks contain community structures. Detecting these community structures is important for understanding and controlling the networks. Most community detection methods use network topology and edge density to identify optimal communities; however, these methods have a high computational complexity and are sensitive to network forms and types. To address these problems, in this paper, we propose an algorithm that uses an interaction optimization process to detect community structures in complex networks. This algorithm efficiently searches the candidates of optimal communities by optimizing the interactions of the members within each community based on the concept of greedy optimization. During this process, each candidate is evaluated using an interaction-based community model. This model quickly and accurately measures the difference between the quantity and quality of intra- and inter-community interactions. We test our algorithm on several benchmark networks with known community structures that include diverse communities detected by other methods. Additionally, after applying our algorithm to several real-world complex networks, we compare our algorithm with other methods. We find that the structure quality and coverage results achieved by our algorithm surpass those of the other methods.

  13. Identification of leader and self-organizing communities in complex networks.

    PubMed

    Fu, Jingcheng; Zhang, Weixiong; Wu, Jianliang

    2017-04-06

    Community or module structure is a natural property of complex networks. Leader communities and self-organizing communities have been introduced recently to characterize networks and understand how communities arise in complex networks. However, identification of leader and self-organizing communities is technically challenging since no adequate quantification has been developed to properly separate the two types of communities. We introduced a new measure, called ratio of node degree variances, to distinguish leader communities from self-organizing communities, and developed a statistical model to quantitatively characterize the two types of communities. We experimentally studied the power and robustness of the new method on several real-world networks in combination of some of the existing community identification methods. Our results revealed that social networks and citation networks contain more leader communities whereas technological networks such as power grid network have more self-organizing communities. Moreover, our results also indicated that self-organizing communities tend to be smaller than leader communities. The results shed new lights on community formation and module structures in complex systems.

  14. Emergence of complexity in controlling simple regular networks

    NASA Astrophysics Data System (ADS)

    Gao, Xin-Dong; Shen, Zhesi; Wang, Wen-Xu

    2016-06-01

    Quantifying the capacity of a given node or a bunch of nodes in maintaining a system's controllability is a crucial problem in complex networks and control theory. We give a systematic analysis of the ability of a single node or a pairs of nodes to control an undirected unweighted chain and ring. By combining algebraic theory and graph spectrum analysis, we derive analytic expressions for the control range of some given control inputs and find that complex phenomena emerge even from these simplest graph structures. Specifically, the control range is sensitive to the location of driver nodes and shows complex periodic behaviors. Our findings have implications for evaluating the control range and practically controlling complex networks.

  15. Evolving enhanced topologies for the synchronization of dynamical complex networks.

    PubMed

    Gorochowski, Thomas E; di Bernardo, Mario; Grierson, Claire S

    2010-05-01

    Enhancing the synchronization of dynamical networks is of great interest to those designing and analyzing many man-made and natural systems. In this work, we investigate how network topology can be evolved to improve this property through the rewiring of edges. A computational tool called NETEVO performs this task using a simulated annealing metaheuristic. In contrast to other work which considers topological attributes when assessing current performance, we instead take a dynamical approach using simulated output from the system to direct the evolution of the network. Resultant topologies are analyzed using standard network measures, B matrices, and motif distributions. These uncover the convergence of many similar features for all our networks, highlighting also significant differences between those evolved using topological rather than dynamical performance measures.

  16. Critical links and nonlocal rerouting in complex supply networks

    NASA Astrophysics Data System (ADS)

    Witthaut, Dirk; Rohden, Martin; Zhang, Xiaozhu; Hallerberg, Sarah; Timme, Marc

    2015-10-01

    Link failures repeatedly induce large-scale outages in power grids and other supply networks. Yet, it is still not well understood, which links are particularly prone to inducing such outages. Here we analyze how the nature and location of each link impact the network's capability to maintain stable supply. We propose two criteria to identify critical links on the basis of the topology and the load distribution of the network prior to link failure. They are determined via a link's redundant capacity and a renormalized linear response theory we derive. These criteria outperform critical link prediction based on local measures such as loads. The results not only further our understanding of the physics of supply networks in general. As both criteria are available before any outage from the state of normal operation, they may also help real-time monitoring of grid operation, employing counter-measures and support network planning and design.

  17. Complexity and dynamics of topological and community structure in complex networks

    NASA Astrophysics Data System (ADS)

    Berec, Vesna

    2017-01-01

    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.

  18. Measurement of a topological edge invariant in a microwave network

    NASA Astrophysics Data System (ADS)

    Pillay, Jason C.; Hu, Wenchao; Wu, Kan; Pasek, Michael; Shum, Perry Ping; Chong, Yidong

    2015-03-01

    We report on the experimental measurement of topological edge invariants in an electromagnetic analog of a topological insulator, realized by a classical microwave network. This experiment serves as a classical electromagnetic realization of Laughlin's ``topological pumping'' thought experiment for the quantum Hall effect. The experiment consists of determining the electromagnetic scattering matrix, based on the input and output wave amplitudes measured at the edges of the network via a network analyzer. The winding on the scattering matrix eigenvalues, resulting from a tunable phase shift built into the network, forms a topological invariant. We demonstrate the existence of a topologically trivial phase, where the winding is zero (no edge states), as well as a topologically nontrivial phase, where the winding is non-zero (topological edge states present). Unlike most other systems used to study topological insulator physics, the full complex scattering parameters can be measured in this setup. As in most microwave experiments, however, our system is susceptible to losses. We show that topological behavior can be meaningfully defined in the experiment despite the effects of loss.

  19. Using contrast patterns between true complexes and random subgraphs in PPI networks to predict unknown protein complexes

    PubMed Central

    Liu, Quanzhong; Song, Jiangning; Li, Jinyan

    2016-01-01

    Most protein complex detection methods utilize unsupervised techniques to cluster densely connected nodes in a protein-protein interaction (PPI) network, in spite of the fact that many true complexes are not dense subgraphs. Supervised methods have been proposed recently, but they do not answer why a group of proteins are predicted as a complex, and they have not investigated how to detect new complexes of one species by training the model on the PPI data of another species. We propose a novel supervised method to address these issues. The key idea is to discover emerging patterns (EPs), a type of contrast pattern, which can clearly distinguish true complexes from random subgraphs in a PPI network. An integrative score of EPs is defined to measure how likely a subgraph of proteins can form a complex. New complexes thus can grow from our seed proteins by iteratively updating this score. The performance of our method is tested on eight benchmark PPI datasets and compared with seven unsupervised methods, two supervised and one semi-supervised methods under five standards to assess the quality of the predicted complexes. The results show that in most cases our method achieved a better performance, sometimes significantly. PMID:26868667

  20. Exploring complex networks via topological embedding on surfaces.

    PubMed

    Aste, Tomaso; Gramatica, Ruggero; Di Matteo, T

    2012-09-01

    We demonstrate that graphs embedded on surfaces are a powerful and practical tool to generate, to characterize, and to simulate networks with a broad range of properties. Any network can be embedded on a surface with sufficiently high genus and therefore the study of topologically embedded graphs is non-restrictive. We show that the local properties of the network are affected by the surface genus which determines the average degree, which influences the degree distribution, and which controls the clustering coefficient. The global properties of the graph are also strongly affected by the surface genus which is constraining the degree of interwovenness, changing the scaling properties of the network from large-world kind (small genus) to small- and ultrasmall-world kind (large genus). Two elementary moves allow the exploration of all networks embeddable on a given surface and naturally introduce a tool to develop a statistical mechanics description for these networks. Within such a framework, we study the properties of topologically embedded graphs which dynamically tend to lower their energy towards a ground state with a given reference degree distribution. We show that the cooling dynamics between high and low "temperatures" is strongly affected by the surface genus with the manifestation of a glass-like transition occurring when the distance from the reference distribution is low. We prove, with examples, that topologically embedded graphs can be built in a way to contain arbitrary complex networks as subgraphs. This method opens a new avenue to build geometrically embedded networks on hyperbolic manifolds.