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Open AccessJournal ArticleDOI

Complex networks: Structure and dynamics

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.
Journal ArticleDOI

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

Deterministic scale-free networks

TL;DR: This work proposes a simple model that generates scale-free networks in a deterministic fashion, and solves exactly the model, showing that the tail of the degree distribution follows a power law.
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Synchronization is Enhanced in Weighted Complex Networks

TL;DR: A weighting procedure based upon the global structure of network pathways enhances complete synchronization of identical dynamical units in scale-free networks and very similar conditions hold also for phase synchronization of nonidentical chaotic oscillators.
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The Predictive Value of Changes in Effective Connectivity for Human Learning

TL;DR: Functional magnetic resonance imaging was used to study the neural basis of associative learning of visual objects and their locations and it was shown that increases in effective connectivity occurred between distinct cortical systems specialized for spatial and object processing.
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Evolution of networks with aging of sites

TL;DR: It is found both from simulation and analytically that the network shows scaling behavior only in the region alpha<1, when alpha increases from -infinity to 0, and the exponent gamma of the distribution of connectivities grows from 2 to the value for the network without aging.
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Patterns of Link Reciprocity in Directed Networks

TL;DR: It is found that real networks are always either correlated or anticorrelated, and that networks of the same type display similar values of the reciprocity.
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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.