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
Breaking the speed and scalability barriers for graph exploration on distributed-memory machines
Fabio Checconi,Fabrizio Petrini,Jeremiah Willcock,Andrew Lumsdaine,Anamitra R. Choudhury,Yogish Sabharwal +5 more
TL;DR: The algorithmic design of a family of highly-efficient Breadth-First Search algorithms and the main classes of optimizations that are used to achieve these results are described.
Journal ArticleDOI
Identification of network modules by optimization of ratio association.
Leonardo Angelini,Stefano Boccaletti,Daniele Marinazzo,Mario Pellicoro,Sebastiano Stramaglia +4 more
TL;DR: This work introduces a novel method for identifying the modular structures of a network based on the maximization of an objective function: the ratio association, and develops an efficient optimization algorithm,based on the deterministic annealing scheme.
Posted Content
A New Metric for Quality of Network Community Structure
TL;DR: This work proposes to modify modularity by subtracting from it the fraction of edges connecting nodes of different communities and by including community density into modularity, and describes the motivation for introducing this metric and proves that this new metric solves the resolution limit problem.
Journal ArticleDOI
A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks
Yadong Li,Jing Liu,Chenlong Liu +2 more
TL;DR: The experimental results indicate that the two MAs outperform the two EAs in terms of the solution quality and the computational cost, and by tuning the parameter in D-value, the four algorithms have the multi-resolution ability.
Proceedings ArticleDOI
AlViz - A Tool for Visual Ontology Alignment
TL;DR: A multiple-view tool called AlViz is introduced, which supports the alignment of ontologies visually and proposes the use of visualization techniques to facilitate user understanding of the ontology alignment results.
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.