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Overlapping Community Detection in Directed Heterogeneous Social Network

  • Changhe Qiu
  • Wei Chen
  • Tengjiao Wang
  • Kai Lei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)

Abstract

In social networks, users and artifacts (documents, discussions or videos) can be modelled as directed bi-type heterogeneous networks. Most existing works for community detection is either with undirected links or in homogeneous networks. In this paper, we propose an efficient algorithm OcdRank (Overlapping Community Detection and Ranking), which combines overlapping community detection and community-member ranking together in directed heterogeneous social network. The algorithm has low time complexity and supports incremental update. Experiments show that our method can detect better community structures as compared to other existing community detection methods.

Keywords

Community detection Directed heterogeneous social network Ranking 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Changhe Qiu
    • 1
    • 2
    • 3
  • Wei Chen
    • 1
    • 2
  • Tengjiao Wang
    • 1
    • 2
  • Kai Lei
    • 3
  1. 1.Key Laboratory of High Confidence Software TechnologiesPeking University, Ministry of EducationBeijingChina
  2. 2.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina
  3. 3.Shenzhen Key Lab for Cloud Computing Technology and Applications (SPCCTA), School of Electronics and Computer EngineeringPeking UniversityBeijingChina

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