On discovering community trends in social networks

Jian Li*, Kwok Wai CHEUNG, Jiming LIU, C. H. Li

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

Real-world social networks (e.g., blogosphere) often evolve over time and thus poses challenges on conventional social network analysis techniques which model the underlying networks as static graphs. In this paper, we are interested in detecting dynamic communities and their trend of evolution in a social network by examining the structural and dynamic patterns of interactions. In doing so, we propose an iterative mining algorithm for computing the intensities and bursts of some hidden communities over time. Our method is probabilistic in nature and can be applied to both undirected graphs and directed graphs. Quantitative and qualitative performance comparisons between the proposed method and some representative methods for social network analysis are provided. Evaluation results based on three benchmark datasets, including Reuters terror news network, political blogosphere, and Enron emails, show that the proposed method is both effective and efficient.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009
Pages230-237
Number of pages8
DOIs
Publication statusPublished - 2009
Event2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009 - Milano, Italy
Duration: 15 Sep 200918 Sep 2009

Publication series

NameProceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009
Volume1

Conference

Conference2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009
Country/TerritoryItaly
CityMilano
Period15/09/0918/09/09

Scopus Subject Areas

  • Software
  • Computer Networks and Communications

User-Defined Keywords

  • Data mining
  • Dynamic communities
  • Graph clustering
  • Social networks

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