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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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Bridgeness: a local index on edge significance in maintaining global connectivity

TL;DR: Experimental results on document networks show that the Bridgeness outperforms content similarity in characterizing the edge significance, and extensive numerical results on disparate networks indicate that the bridgeness is also better than some well-known indices onedge significance, including the Jaccard coefficient, degree product and betweenness centrality.
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Node-to-node pinning control of complex networks.

TL;DR: This paper proposes the node-to-node pinning-control strategy to optimize the control performance while avoiding to simultaneously control all the network sites, and defines a performance metric based on the location of pinned sites, the pining-control gains, and the network topology.
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Perspective: Memcomputing: Leveraging memory and physics to compute efficiently

TL;DR: In this article, the authors discuss how to employ one such property, memory (time non-locality), in a novel physics-based approach to computation, and focus on digital memcomputing machines (DMMs) that are scalable.
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Understanding emotion with brain networks.

TL;DR: How to think of causation in complex systems like the brain is considered, so as to inform the relationship between emotion and other mental aspects, such as cognition.
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The complexity of Chinese syntactic dependency networks

TL;DR: The finding that the two syntactic networks have the same diameter and different average degrees, path lengths, clustering coefficients and power exponents can be seen as an indicator that complexity theory can work as a means of stylistic study.
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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.