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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

Mean-field solution of the small-world network model.

TL;DR: A mean-field solution for the average path length and for the distribution of path lengths in the small-world network model is presented, which is exact in the limit of large system size and either a large or small number of shortcuts.
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Communication in networks with hierarchical branching.

TL;DR: A continuous phase transition between a sparse and a congested regime is observed and accurately described by an order parameter and the power spectra and scaling properties are observed when the size of the system varies.
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Evolutionary conservation of motif constituents in the yeast protein interaction network

TL;DR: It is found that the conservation of proteins in distinct topological motifs correlates with the interconnectedness and function of that motif and also depends on the structure of the overall interactome topology.
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Active Currents Regulate Sensitivity and Dynamic Range in C. elegans Neurons

TL;DR: The electrical properties of an identified sensory neuron (ASER) across four developmental stages and 42 unidentified neurons at one stage are analyzed to find that ASER is nearly isopotential and fails to generate classical Na+ action potentials, but displays a high sensitivity to input currents coupled to a depolarization-dependent reduction in sensitivity.
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Bubbling of attractors and synchronisation of chaotic oscillators

TL;DR: In this paper, the concept of bubbling of an attractor is introduced, a new type of intermittency that is triggered by low levels of noise, and demonstrated numerical and experimental examples of this behaviour.
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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.