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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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Small worlds and semantic network growth in typical and late talkers.

TL;DR: It is shown that, while typically developing children's early networks show small world structure as early as 15 months and with as few as 55 words, children with language delay (late talkers) have this structure to a smaller degree, which implicates a maladaptive bias in word acquisition for late talkers, potentially indicating a preference for “oddball” words.
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Ranking in evolving complex networks

TL;DR: The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks and emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of real network traffic, prediction of future links, and identification of highly-significant nodes.
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Eigenvector centrality of nodes in multiplex networks

TL;DR: This work extends the concept of eigenvector centrality to multiplex networks, and introduces several alternative parameters that quantify the importance of nodes in a multi-layered networked system, including the definition of vectorial-type centralities.
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Percolation in multiplex networks with overlap

TL;DR: It is shown that the overlap among layers can improve the robustness of interdependent multiplex systems and change the critical behavior of the percolation phase transition in a complex way.
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Graph-based term weighting for information retrieval

TL;DR: This work proposes a principled graph-theoretic approach of computing term weights and integrating discourse aspects into retrieval, and experimentally shows that this type of ranking performs comparably to BM25, and can even outperform it, across different TREC datasets and evaluation measures.
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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.