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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

Communication and optimal hierarchical networks

TL;DR: It is found that the need for hierarchy is related to the existence of costly connections, and the limitation in the capability of the agents to deal with packets causes the network to collapse under certain conditions.
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Internet’s critical path horizon

TL;DR: A model that reproduces the relevant key, statistical features of Internet’s traffic with a topologically realistic spatial structure and reports the existence of a critical path horizon defining a transition from low-efficient traffic to highly efficient flow.

Partitioning Networks by Eigenvectors

TL;DR: A survey of published methods for partitioning sparse arrays is presented, including early attempts to describe the partitioning properties of eigenvectors of the adjacency matrix, and a heuristic method to produce approximate correct colourings using sign patterns of eikenvectors with large negative eigenvalues.
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On variability in the density of corticocortical and thalamocortical connections.

TL;DR: It is proposed that the near exponential distribution of connection densities is a simple consequence of 'patchy' connectivity, and it is anticipated that connection data will be well described by the negative binomial, a class of distribution that applies to events occurring in clumped or patchy substrates.
Posted Content

Phase Transitions in a Gene Network Model of Morphogenesis

TL;DR: In this paper, a model of biological morphogenesis is presented based on a gene-network cell description plus the interaction among cells, where interactions between nearest cells are due to diffusion-like mechanisms and also to inductive, cell-to-cell interactions, and an extensive analysis of random gene networks shows that spatial patterns are common in both types of interaction but are much more common in the inductive case.
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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.