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 language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2010 |
| Pages | 83-86 |
| Number of pages | 4 |
| DOIs | |
| State | Published - 2010 |
| Event | 2010 3rd IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2010 - Toronto, ON, Canada Duration: Aug 31 2010 → Sep 3 2010 |
Other
| Other | 2010 3rd IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2010 |
|---|---|
| Country | Canada |
| City | Toronto, ON |
| Period | 8/31/10 → 9/3/10 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Software
Cite this
Evolutionary community discovery from dynamic multi-relational CQA networks. / Zhang, Zhongfeng; Li, Qiudan; Zeng, Dajun.
Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2010. 2010. p. 83-86 5614070.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Evolutionary community discovery from dynamic multi-relational CQA networks
AU - Zhang, Zhongfeng
AU - Li, Qiudan
AU - Zeng, Dajun
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78649887605&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649887605&partnerID=8YFLogxK
U2 - 10.1109/WI-IAT.2010.189
DO - 10.1109/WI-IAT.2010.189
M3 - Conference contribution
AN - SCOPUS:78649887605
SN - 9780769541914
SP - 83
EP - 86
BT - Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2010
ER -