Evolutionary Community Discovery from Dynamic Multi-relational CQA Networks

Zhongfeng Zhang, Qiudan Li, Daniel Zeng

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.

External IDs:dblp:conf/iat/ZhangLZ10