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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Do Brain Networks Evolve by Maximizing Their Information Flow Capacity
TL;DR: It is found that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks.
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A novel community detection method in bipartite networks
TL;DR: A two-stage method for detecting community structure in bipartite networks is proposed, developing the Bi-Louvain algorithm that iteratively groups the nodes in each part by turns and demonstrating that the calculation of the gain of modularity of each aggregation, and the operation of joining two communities can be compactly calculated by matrix operations for all pairs of communities simultaneously.
Generalised measures for the evaluation of community detection methods
TL;DR: This article proposes a modification to solve the problem of relevance of community detection measures, and applies it to the three most widespread measures: purity, Rand index and normalised mutual information (NMI).
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Healthcare Worker Contact Networks and the Prevention of Hospital-Acquired Infections
Donald E. Curtis,Christopher S. Hlady,Gaurav Kanade,Sriram V. Pemmaraju,Philip M. Polgreen,Alberto M. Segre +5 more
TL;DR: Using the generated contact networks, a simple policy that vaccinates the most mobile healthcare workers first, is robust and quite effective relative to a random vaccination policy, and several alternate vaccination policies are evaluated.
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Edge-count probabilities for the identification of local protein communities and their organization.
Victor Farutin,Keith E. Robison,Eric S. Lightcap,Vlado Dančík,Alan Ruttenberg,Stanley Letovsky,Joel Pradines +6 more
TL;DR: A computational approach based on a local search strategy that discovers sets of proteins that preferentially interact with each other are discovered and are referred to as protein communities and are likely to represent functional modules.
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.
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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.