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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

Synchronization, diversity, and topology of networks of integrate and fire oscillators

TL;DR: The analysis shows that regular lattices perform better than a disordered network and this fact can be understood by considering the variability in the number of links between two adjacent neighbors.
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The hierarchical backbone of complex networks.

TL;DR: In this article, the authors presented how the interpretation of the network weight matrix as a transition matrix allows the hierarchical backbone to be identified and characterized in terms of the concepts of hierarchical degree, which expresses the total weights of virtual edges established along successive transitions.
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SIMULATION OF CONSENSUS MODEL OF DEFFUANT et al. ON A BARABÁSI–ALBERT NETWORK

TL;DR: The consensus model with bounded confidence, studied by Deffuant et al. (2000), two randomly selected people who differ not too much in their opinion both shift their opinions towards each other, restricting this exchange of information to people connected by a scale-free network.
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Large-scale optimization of neuron arbors.

TL;DR: This model applies comparably to arterial and river networks and finds that neuron tree samples globally minimize their total volume-rather than, for example, surface area or branch length.
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Damage spreading, coarsening dynamics and distribution of political votes in sznajd model on square lattice

TL;DR: In this paper, a model for elections based on the Sznajd model is proposed and the exponent obtained for the distribution of votes during the transient agrees with that obtained for elections.
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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.