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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

Associative memory on a small-world neural network

TL;DR: A model of associative memory based on a neural network with small-world structure exhibits a phase transition at a finite value of the disorder, and the more ordered networks are unable to recover the patterns, and are always attracted to non-symmetric mixture states.
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Spatial structure of the internet traffic

TL;DR: It is shown that the Internet activity of a region increases with the number of published papers by laboratories of that region, demonstrating the positive impact of the Web on scientific activity and illustrating quantitatively the adage ‘the more you read, the more you write’.
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Reply networks on a bulletin board system

TL;DR: It is indicated that the hierarchical and clustering structure of the interest space, together with overlapping interests of IDs not only result in small-world characteristics of reply networks on BBS, but also give rise to preferential attachment, which is a popular explanation for scale-free features.
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Synchronization in Nonidentical Extended Systems

TL;DR: In this article, the synchronization of two nonidentical spatially extended fields, ruled by one-dimensional complex Ginzburg-Landau equations, both in the phase and in the amplitude turbulence regimes, is reported.
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Effects of alternative connectivity on behavior of randomly constructed Boolean networks

TL;DR: RBNs are used to examine regulatory gene networks with four different topologies, which are characterized by different rank distributions of output connections that vary from uniform to highly skewed and also examine effects of bias in the distribution of Boolean functions for the network.
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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.