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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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Multi-objective community detection in complex networks

TL;DR: It is demonstrated that a combination of two negatively correlated objectives under the multi-objective framework usually leads to remarkably better performance compared with either of the orignal single objectives, including even many popular algorithms.
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Some bibliometric procedures for analyzing and evaluating research fields

TL;DR: Four bibliometric indices, the h-classics approach to identify the classic papers of a research field and three free science mapping software tools are shown.
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Investigating the topology of interacting networks - Theory and application to coupled climate subnetworks

TL;DR: In this article, a graph-theoretical framework for studying the interaction structure between subnetworks embedded within a complex network of networks is proposed, which can quantify the structural role of single vertices or whole sub-networks with respect to the interaction of a pair of sub-nets on local, mesoscopic and global topological scales.
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Collective behavior and evolutionary games - An introduction

TL;DR: This article is an introduction to a special issue in Chaos, Solitons & Fractals with the goal of attracting submissions that identify unifying principles that describe the essential aspects of collective behavior, and which thus allow for a better interpretation and foster the understanding of the complexity arising in such systems.
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Characterization of anatomical and functional connectivity in the brain: a complex networks perspective.

TL;DR: Strong evidence is presented that brain networks, ranging from simple nets of interconnected neurons up to macroscopic networks of brain areas display the typical features of complex systems: high clustering, short path lengths, skewed degree distributions, presence of hubs, assortative mixing and the presence of modules.
References
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Journal ArticleDOI

Optimization by Simulated Annealing

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book

Computers and Intractability: A Guide to the Theory of NP-Completeness

TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
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Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Book

Matrix computations

Gene H. Golub
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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.