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Local Community Detection Using Social Relations and Topic Features in Social Networks

  • Chengcheng XuEmail author
  • Huaping Zhang
  • Bingbing Lu
  • Songze Wu
Conference paper
  • 1.4k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10565)

Abstract

Local community detection is an important research focus in social network analysis. Most existing methods share the intrinsic limitation of utilizing undirected and unweighted networks. In this paper, we propose a novel local community detection algorithm that fuses social relations and topic features in social networks. By defining a new social similarity, the proposed algorithm can effectively reveal the dynamic characteristics in social networks. In addition, the topic similarity is measured by Jensen–Shannon divergence, in which the topics are extracted from the user-generated content by topic models. Extensive experiments conducted on a real social network dataset demonstrate that our proposed algorithm outperforms methods based on social relations or topic features alone.

Keywords

Social networks Local community detection Topic model 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Chengcheng Xu
    • 1
    Email author
  • Huaping Zhang
    • 1
  • Bingbing Lu
    • 1
  • Songze Wu
    • 1
  1. 1.School of Computer ScienceBeijing Institute of TechnologyBeijingChina

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