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Complex networks: Structure and dynamics

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TLDR
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

Identity and Search in Social Networks

TL;DR: A model is presented that offers an explanation of social network searchability in terms of recognizable personal identities: sets of characteristics measured along a number of social dimensions that may be applicable to many network search problems.
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Random walks on complex networks.

TL;DR: The random walk centrality C is introduced, which is the ratio between its coordination number and a characteristic relaxation time, and it is shown that it determines essentially the mean first-passage time (MFPT) between two nodes.
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Scale-free topology of e-mail networks.

TL;DR: The resulting network exhibits a scale-free link distribution and pronounced small-world behavior, as observed in other social networks, implying that the spreading of e-mail viruses is greatly facilitated in real e- mail networks compared to random architectures.
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Chaos in random neural networks.

TL;DR: A self-consistent mean-field theory predicts a transition from a stationary phase to a chaotic phase occurring at a critical value of the gain parameter.
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Complex dynamics and phase synchronization in spatially extended ecological systems

TL;DR: This work examines the synchronization of complex population oscillations in networks of model communities and in natural systems, where phenomena such as unusual ‘4- and 10-year cycle’ of wildlife are often found.
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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.