Evolutionary community discovery from dynamic multi-relational CQA networks

Zhongfeng Zhang, Qiudan Li, Daniel Zeng

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

2 Scopus citations

Abstract

As a knowledge sharing platform, Community Question Answering (CQA) services have attracted much attention from both academic and industry. This paper studies the problem of mining evolutionary community structures in CQA, through analysis of time-varying, multi-relational data among users and contents. We propose a unified framework for this problem, which makes the following contributions: 1) We propose an AT-LDA model, which combines author-topic model with topological structure analysis, to discover densely connected communities and the community topics in a unified process; 2) Our framework captures community structures and their evolution with temporal smoothing given by historic community structures. Empirical evaluation on real-world dataset shows that interesting communities and their evolution patterns can be detected.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2010
Pages83-86
Number of pages4
DOIs
StatePublished - Dec 13 2010
Event2010 3rd IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2010 - Toronto, ON, Canada
Duration: Aug 31 2010Sep 3 2010

Publication series

NameProceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2010

Other

Other2010 3rd IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2010
CountryCanada
CityToronto, ON
Period8/31/109/3/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

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  • Cite this

    Zhang, Z., Li, Q., & Zeng, D. (2010). Evolutionary community discovery from dynamic multi-relational CQA networks. In Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2010 (pp. 83-86). [5614070] (Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2010). https://doi.org/10.1109/WI-IAT.2010.189