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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.
Journal ArticleDOI

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

Highly optimized tolerance:A mechanism for power laws in designed systems

TL;DR: This work introduces a mechanism for generating power law distributions, referred to as highly optimized tolerance (HOT), which is motivated by biological organisms and advanced engineering technologies, and investigates the mechanism in the context of percolation and sand pile models in order to emphasize the sharp contrasts between SOC and self-organized criticality.
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Analysis of connectivity in the cat cerebral cortex.

TL;DR: It is shown that hierarchical rules, when applied to the cat visual system, define a largely consistent hierarchy, and in both auditory and visual systems, the ordering of areas by hierarchical analysis and by optimization analysis was statistically significantly related.
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Betweenness centrality in large complex networks

TL;DR: The betweenness centrality of nodes in large complex networks is analyzed and it is found that for trees or networks with a small loop density, a larger density of loops leads to $\eta < 2$.
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Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs

TL;DR: A novel algorithm is presented that allows estimation of subgraph concentrations and detection of network motifs at a runtime that is asymptotically independent of the network size and can be applied to estimate the concentrations of larger subgraphs in larger networks than was previously possible with exhaustive enumeration algorithms.
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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.