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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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Scale-rich metabolic networks.

TL;DR: The primary aim of this Letter is to give the simplest possible biochemical explanations and minimal toy models based on a highly optimized tolerance perspective, which show where and why metabolic networks have power laws.
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Section 2(a)

TL;DR: When designing an engine it is necessary to decide in what type of vehicle it is to be used and the amount of power required.
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Efficient local strategies for vaccination and network attack

TL;DR: This work studies how a fraction of a population should be vaccinated to most efficiently stop epidemics and concludes that only local information-about the neighborhood of specific vertices-is relevant for vaccination.
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Statistical properties of randomly broken objects and of multivalley structures in disordered systems

TL;DR: The statistical properties of the multivalley structure of disordered systems and of randomly broken objects have many features in common as mentioned in this paper, and it is shown that the probability distributions of the largest piece W, P2(W) of the second largest piece, and n(Y) of Y = 1, Wi always have singularities at W, = l/n, W2 = l n, W 2 = l /n and Y = Ijn, n = 1.
Book ChapterDOI

The social forces.

TL;DR: This paper present an attractive little volume of two hundred and twenty-six pages, neatly bound and printed upon excellent paper with wide margins and clear type, made up of twenty-five editorials appearing in The Survey in 1907 and 1908.
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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.