Overlapping Community Detection in Directed Heterogeneous Social Network

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)


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.


Community detection Directed heterogeneous social network Ranking 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., Wu, T.: Rankclus: integrating clustering with ranking for heterogeneous information network analysis. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, pp. 565–576. ACM (2009)Google Scholar
  2. 2.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30(1), 107–117 (1998)CrossRefGoogle Scholar
  3. 3.
    Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11(3), 033015 (2009)CrossRefGoogle Scholar
  4. 4.
    Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 587–596. ACM (2013)Google Scholar
  5. 5.
    Yang, J., McAuley, J., Leskovec, J.: Detecting cohesive and 2-mode communities indirected and undirected networks. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 323–332. ACM (2014)Google Scholar

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

Personalised recommendations