Discovering temporal communities from social network documents

Ding Zhou, Isaac Councill, Hongyuan Zha, C. Lee Giles

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

56 Scopus citations

Abstract

This paper studies the discovery of communities from social network documents produced over time, addressing the discovery of temporal trends in community memberships. We first formulate static community discovery at a single time period as a tripartite graph partitioning problem. Then we propose to discover the temporal communities by threading the statically derived communities in different time periods using a new constrained partitioning algorithm, which partitions graphs based on topology as well as prior information regarding vertex membership. We evaluate the proposed approach on synthetic datasets and a real-world dataset prepared from the CiteSeer.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages745-750
Number of pages6
ISBN (Print)0769530184, 9780769530185
DOIs
StatePublished - Jan 1 2007
Event7th IEEE International Conference on Data Mining, ICDM 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

Other7th IEEE International Conference on Data Mining, ICDM 2007
CountryUnited States
CityOmaha, NE
Period10/28/0710/31/07

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Zhou, D., Councill, I., Zha, H., & Giles, C. L. (2007). Discovering temporal communities from social network documents. In Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007 (pp. 745-750). [4470321] (Proceedings - IEEE International Conference on Data Mining, ICDM). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2007.56