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Complex networks: Structure and dynamics

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The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
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This article is published in Physics Reports.The article was published on 2006-02-01 and is currently open access. It has received 9441 citations till now. The article focuses on the topics: Network dynamics & Complex network.

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Complex brain networks: graph theoretical analysis of structural and functional systems

TL;DR: This article reviews studies investigating complex brain networks in diverse experimental modalities and provides an accessible introduction to the basic principles of graph theory and highlights the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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Complex network measures of brain connectivity: uses and interpretations.

TL;DR: Construction of brain networks from connectivity data is discussed and the most commonly used network measures of structural and functional connectivity are described, which variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, and test resilience of networks to insult.
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Community detection in graphs

TL;DR: A thorough exposition of community structure, or clustering, is attempted, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists.
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Community detection in graphs

TL;DR: A thorough exposition of the main elements of the clustering problem can be found in this paper, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
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Journal ArticleDOI

Synchronization of oscillators with long range interaction: Phase transition and anomalous finite size effects.

TL;DR: Signals are given that for the decay exponent less than the dimensions of the lattice and for large populations, synchronization is possible even if the coupling is arbitarily weak, suggesting that in organisms interacting through slowly decaying signals such as light or sound, collective oscillations can always be established if the population is sufficiently large.
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Synchronization of chaotic structurally nonequivalent systems

TL;DR: The creation of a lower dimensional chaotic state is described, showing that it is associated with an abrupt transition in the Lyapunov spectrum, and the robustness of this state against noise is discussed.
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An evaluation of the use of multidimensional scaling for understanding brain connectivity

TL;DR: It is demonstrated that great caution is needed in interpreting the resulting configuration of a connectivity dataset derived from the primate cortical visual system, and the strength of support that an NMDS analysis of the visual system data provides for the two streams view of visual processing is questioned.
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The dynamics of sparse random networks

TL;DR: This paper uses the technique of return maps to study the dynamics of random networks with sparse, asymmetric connectivity and nonspecific inhibition, and relates these behaviors to network parameters and presents empirical evidence for the accuracy of this statistical model.
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Small-world phenomena and the statistics of linear polymer networks

TL;DR: In this article, a phase boundary separating regions with and without small-world behaviour was obtained for polymeric networks in the regular lattice space, and it was shown that the polymeric network does not exhibit smallworld behavior.
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The authors review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.