HSN-PAM: Finding hierarchical probabilistic groups from large-scale networks

Haizheng Zhang, Wei Li, Xuerui Wang, C. Lee Giles, Henry C. Foley, John Yen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Real-world social networks are often hierarchical, reflecting the fact that some communities are composed of a few smaller, sub-communities. This paper describes a hierarchical Bayesian model based scheme, namely HSNPAM (Hierarchical Social Network-Pachinko Allocation Model), for discovering probabilistic, hierarchical communities in social networks. This scheme is powered by a previously developed hierarchical Bayesian model. In this scheme, communities are classified into two categories: super-communities and regular-communities. Two different network encoding approaches are explored to evaluate this scheme on research collaborative networks, including CiteSeer and NanoSCI. The experimental results demonstrate that HSN-PAM is effective for discovering hierarchical community structures in large-scale social networks.

Original languageEnglish (US)
Title of host publicationICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
Pages27-32
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States
Duration: Oct 28 2007Oct 31 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
CountryUnited States
CityOmaha, NE
Period10/28/0710/31/07

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Zhang, H., Li, W., Wang, X., Giles, C. L., Foley, H. C., & Yen, J. (2007). HSN-PAM: Finding hierarchical probabilistic groups from large-scale networks. In ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops (pp. 27-32). [4476642] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDMW.2007.115