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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.
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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.
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

Network participation indices: characterizing component roles for information processing in neural networks

TL;DR: In this paper, the authors propose a set of indices that characterize how a network node participates in a larger network and what roles it may take given the specific sub-network of interest.
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Information super-diffusion on structured networks

TL;DR: It is found that graph topology determines the properties of diffusion in a universal way, which is reflected by power laws in the transit time and velocity distributions of packets, and evidence that these power-laws are compatible with super-diffusive traffic on large networks is found.
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Edge vulnerability in neural and metabolic networks

TL;DR: This article tested several measures for identifying vulnerable edges and compared their prediction performance in biological and artificial networks, and identified intercluster connections in biological but not in random and scale-free benchmark networks.
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Instability and controllability of linearly coupled oscillators: Eigenvalue analysis

TL;DR: In this paper, the stability and controllability of linearly coupled oscillators are investigated in detail, based on eigenvalue analysis, and local injections are suggested to synchronize chaos.
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The Social Organization of Sociological Knowledge: Modeling the Intersection of Specialties

TL;DR: The authors examined the pattern of shared membership linking specialties in American sociology in 1990 and revealed seven coherent clusters focusing on deviance and control, setting and context, political and macrosociology, theory and culture, numbers, stratification and work, and social psychologylgenderlmedical sociology.
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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.