Fast algorithm for detecting community structure in networks.
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.read more
Citations
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Age-related reorganizational changes in modularity and functional connectivity of human brain networks.
Jie Song,Rasmus M. Birn,Mélanie Boly,Timothy B. Meier,Veena A. Nair,Mary E. Meyerand,Vivek Prabhakaran +6 more
TL;DR: A brain network model is developed using graph theory methods applied to the resting-state functional magnetic resonance imaging data acquired from two groups of normal healthy adults classified by age that indicates that global reorganization of brain functional networks may reflect overall topological changes with aging and that aging likely alters individual brain networks differently depending on the functional properties.
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Genomic patterns of pleiotropy and the evolution of complexity
TL;DR: Analyzing phenotypes of large numbers of yeast, nematode, and mouse mutants, it is shown that the fraction of traits altered appreciably by the deletion of a gene is minute for most genes and the gene–trait relationship is highly modular and the observed scaling exponent falls in a narrow range that maximizes the optimal complexity.
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Modularity-Maximizing Network Communities via Mathematical Programming
Gaurav Agarwal,David Kempe +1 more
TL;DR: In this paper, the authors introduce the technique of rounding mathematical programs to the problem of modularity maximization, presenting two novel algorithms, namely, the linear programing algorithm comes with an a posteriori approximation guarantee: by comparing the solution quality to the fractional solution, a bound on the available "room for improvement" can be obtained.
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Local resolution-limit-free Potts model for community detection.
Peter Ronhovde,Zohar Nussinov +1 more
TL;DR: An exceptionally accurate spin-glass-type Potts model for community detection that is at least as accurate as the best currently available algorithms and robust to the effects of noise and competitive with the best current algorithms in terms of speed and size of solvable systems.
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Memetic algorithm for community detection in networks.
TL;DR: A memetic algorithm is proposed to optimize another quality function, modularity density, which includes a tunable parameter that allows one to explore the network at different resolutions, and the effectiveness and the multiresolution ability of the proposed method is shown.
References
More filters
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
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
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
Michelle Girvan,Mark Newman +1 more
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.