Algorithms and Applications for Community Detection in Weighted Networks

Zongqing Lu, Xiao Sun, Yonggang Wen, Guohong Cao, Thomas La Porta

Research output: Contribution to journalArticle

51 Scopus citations

Abstract

Community detection is an important issue due to its wide use in designing network protocols such as data forwarding in Delay Tolerant Networks (DTN) and worm containment in Online Social Networks (OSN). However, most of the existing community detection algorithms focus on binary networks. Since most networks are naturally weighted such as DTN or OSN, in this article, we address the problems of community detection in weighted networks, exploit community for data forwarding in DTN and worm containment in OSN, and demonstrate how community can facilitate these network designs. Specifically, we propose a novel community detection algorithm, and introduce two metrics: intra-centrality and inter-centrality, to characterize nodes in communities, based on which we propose an efficient data forwarding algorithm for DTN and a worm containment strategy for OSN. Extensive trace-driven simulation results show that the proposed community detection algorithm, the data forwarding algorithm, and the worm containment strategy significantly outperform existing works.

Original languageEnglish (US)
Article number6954543
Pages (from-to)2916-2926
Number of pages11
JournalIEEE Transactions on Parallel and Distributed Systems
Volume26
Issue number11
DOIs
Publication statusPublished - Nov 1 2015

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All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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