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
More filters
Proceedings ArticleDOI
SLPA: Uncovering Overlapping Communities in Social Networks via a Speaker-Listener Interaction Dynamic Process
TL;DR: A novel, general framework to detect and analyze both individual overlapping nodes and entire communities, in which nodes exchange labels according to dynamic interaction rules is presented.
Journal ArticleDOI
Extending the definition of modularity to directed graphs with overlapping communities
TL;DR: This paper starts from the definition of a modularity function, given by Newman to evaluate the goodness of network community decompositions, and extends it to the more general case of directed graphs with overlapping community structures.
Journal ArticleDOI
Robust detection of dynamic community structure in networks.
Danielle S. Bassett,Mason A. Porter,Nicholas F. Wymbs,Scott T. Grafton,Jean M. Carlson,Peter J. Mucha +5 more
TL;DR: In this paper, the authors consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems and propose a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions.
Journal ArticleDOI
CitNetExplorer: A new software tool for analyzing and visualizing citation networks
Nees Jan van Eck,Ludo Waltman +1 more
TL;DR: CitNetExplorer as discussed by the authors is a software tool for analyzing and visualizing citation networks of scientific publications, which can be used to study the development of a research field, delineate the literature on a research topic, and to support literature reviewing.
Journal ArticleDOI
Graph theory and networks in Biology
Oliver Mason,Mark Verwoerd +1 more
TL;DR: A survey of the use of graph theoretical techniques in biology is presented in this article, with an emphasis on synchronisation and disease propagation, as well as the link between structural network properties and dynamics.
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.