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Open AccessJournal ArticleDOI

Fast algorithm for detecting community structure in networks.

Mark Newman
- 18 Jun 2004 - 
- Vol. 69, Iss: 6, pp 066133-066133
TLDR
An algorithm is described which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms.
Abstract
Many networks display community structure--groups of vertices within which connections are dense but between which they are sparser--and sensitive computer algorithms have in recent years been developed for detecting this structure. These algorithms, however, are computationally demanding, which limits their application to small networks. Here we describe an algorithm which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms. We give several example applications, including one to a collaboration network of more than 50,000 physicists.

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Journal ArticleDOI

Extracting the commercialization gap between science and technology — Case study of a solar cell

TL;DR: This paper compared structures of the citation network of scientific publications with those of patents, and discussed the differences between them, and a case study was performed in a solar cell to develop a method of detecting gaps between science and technology.
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Sustainable development, intellectual capital and technology policies: A structured literature review and future research agenda

TL;DR: In this paper, a Structured Literature Review (SLR) about the strategic role of Intellectual Capital (IC) for achieving sustainable development goals (SDGs) is provided, which offers an outline of past and present literature and frames a future research agenda.
Journal ArticleDOI

Evolutionary Computation for Community Detection in Networks: A Review

TL;DR: The aim of this paper is to present the approaches based on evolutionary computation to uncover community structure, and the representation schemes with the genetic operators apt for them are described and the most popular fitness functions employed by the methods are discussed.
Journal ArticleDOI

Detecting overlapping communities of weighted networks via a local algorithm

TL;DR: An overlapping communities detecting algorithm is proposed whose main strategies are finding an initial partial community from a node with maximal node strength and adding tight nodes to expand the partial community.
Journal ArticleDOI

Phylogenetic signal in module composition and species connectivity in compartmentalized host-parasite networks.

TL;DR: It is suggested that the establishment of host-parasite networks results from the interplay between phylogenetic influences acting mostly on hosts and local factors acting on parasites, to create an asymmetrically constrained pattern of geographic variation in modular structure.
References
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疟原虫var基因转换速率变化导致抗原变异[英]/Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

宁北芳, +1 more
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Journal ArticleDOI

Statistical mechanics of complex networks

TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Journal ArticleDOI

The Structure and Function of Complex Networks

Mark Newman
- 01 Jan 2003 - 
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Journal ArticleDOI

Community structure in social and biological networks

TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Journal ArticleDOI

Finding and evaluating community structure in networks.

TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
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