Community mining from signed social networks

Bo Yang*, Kwok Wai CHEUNG, Jiming LIU

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

305 Citations (Scopus)

Abstract

Many complex systems in the real world can be modeled as signed social networks that contain both positive and negative relations. Algorithms for mining social networks have been developed in the past; however, most of them were designed primarily for networks containing only positive relations and, thus, are not suitable for signed networks. In this work, we propose a new algorithm, called FEC, to mine signed social networks where both positive within-group relations and negative between-group relations are dense. FEC considers both the sign and the density of relations as the clustering attributes, making it effective for not only signed networks but also conventional social networks including only positive relations. Also, FEC adopts an agent-based heuristic that makes the algorithm efficient (in linear time with respect to the size of a network) and capable of giving nearly optimal solutions. FEC depends on only one parameter whose value can easily be set and requires no prior knowledge on hidden community structures. The effectiveness and efficacy of FEC have been demonstrated through a set of rigorous experiments Involving both benchmark and randomly generated signed networks.

Original languageEnglish
Pages (from-to)1333-1348
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume19
Issue number10
DOIs
Publication statusPublished - Oct 2007

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • Agent-based approach
  • Community mining
  • Random walk
  • Signed social networks

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