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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

Optimal wiring principle and plateaus in the degree of separation for cortical neurons.

TL;DR: Wiring principle incorporating minimization of both axonal length and the degree of separation leads naturally to a trade-off between saving axons and saving energy required in the communication.
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Topology and computational performance of attractor neural networks.

TL;DR: This work studied the computational performance of Hopfield-type attractor neural nets with regular lattice, random, small-world, and scale-free topologies to find the portion of a pattern encoded by the subset of highly connected nodes is more robust and efficiently recognized than the rest of the pattern.
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Influence of topology on the performance of a neural network

TL;DR: It is demonstrated that, at finite temperature, the capacity to store and retrieve binary patterns is higher for ANN with scale-free (SF) topology than for highly random-diluted Hopfield networks with the same number of synapses.
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Partial synchronization and spontaneous spatial ordering in coupled chaotic systems.

TL;DR: A model of many symmetrically and locally coupled chaotic oscillators is studied and partial chaotic synchronizations associated with spontaneous spatial ordering are demonstrated.
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Structure of cycles and local ordering in complex networks

TL;DR: The analysis of data from real networks reveals the ubiquitous presence of a statistically high level of grid-like ordering that is non-trivially correlated with the local degree properties, providing new insights on the hierarchical structure of complex networks.
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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.