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
Journal ArticleDOI
Identifying influential nodes in Social Networks: Neighborhood Coreness based voting approach
TL;DR: Experiments and simulations using Susceptible–Infected–Recovered (SIR) stochastic model on many real datasets show that the proposed method, NCVoteRank, outperforms some of the existing popular methods such as PageRank, K-shell, Extended Coreness, VoteRank, and WVoteRank.
Journal ArticleDOI
LICOD: A Leader-driven algorithm for community detection in complex networks
Zied Yakoubi,Rushed Kanawati +1 more
TL;DR: Results show that the proposed framework for implementing LdCD algorithms outperforms top state of the art algorithms for community detection in complex networks and proposes a new way for evaluating performances of community detection algorithms.
Journal ArticleDOI
A multilayered analysis of energy security research and the energy supply process
Eriko Kiriyama,Yuya Kajikawa +1 more
TL;DR: The bibliometrics analysis indicates that research has shifted from promoting strategies for ensuring the self-sufficiency of the primary energy to diversification of the secondary energy supply chain by introducing energy networks consisting of an infrastructure established through international coordination.
Posted Content
A Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks
TL;DR: The experimental results show that the proposed modularity based algorithm can keep track of community structure in time and outperform the well known CNM algorithm in terms of modularity.
Book ChapterDOI
Community Detection in Multi-relational Social Networks
TL;DR: This paper introduces a novel co-ranking framework, named MutuRank, that makes full use of the mutual influence between relations and actors to transform the multi-relational network to the single- Relational network.
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.