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Complex networks: Structure and dynamics

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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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Stochastic sampled-data control for synchronization of complex dynamical networks with control packet loss and additive time-varying delays

TL;DR: This study examines the exponential synchronization of complex dynamical networks with control packet loss and additive time-varying delays with novel Lyapunov-Krasovskii functional with triple integral terms and an effective method is introduced by extending the lower bound lemma.
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Structure and dynamical behavior of non-normal networks.

TL;DR: A collection of empirical networks in a wide spectrum of disciplines are analyzed and it is shown that strong non-normality is ubiquitous in network science.
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Integrating stochasticity and network structure into an epidemic model

TL;DR: A more rigorous analytical understanding is developed based on pairwise approximations to incorporate localized spatial structure and diffusion approximation to capture the impact of stochasticity in the context of an epidemic.
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The Statistical Physics of Real-World Networks

TL;DR: In this article, a survey of statistical physics models that reproduce more complex, semi-local network features using Markov chain Monte Carlo sampling, as well as the models of generalised network structures such as multiplex networks, interacting networks and simplicial complexes is presented.
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Pseudoinverse of the Laplacian and best spreader node in a network.

TL;DR: A new graph metric is proposed that complements the effective graph resistance R_{G} and that specifies the heterogeneity of the nodal spreading capacity in a graph and the pseudoinverse matrix of the Laplacian of star, path, and cycle graphs.
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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.