Evolutionary Community Discovery from Dynamic Multi-relational CQA Networks
Evolutionary Community Discovery from Dynamic Multi-relational CQA Networks
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DOI：
10.1109/wi-iat.2010.189
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发表时间：
2010-08
期刊：
影响因子：
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通讯作者：
Zhongfeng Zhang;Qiudan Li;D. Zeng
中科院分区：
文献类型：
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作者：
Zhongfeng Zhang;Qiudan Li;D. Zeng
社区问答服务作为一种知识共享平台，已经引起了学术界和产业界的广泛关注。本文研究了CQA中的进化社区结构挖掘问题，通过分析时变的、用户和内容之间的多关系数据。我们提出了一个统一的框架来解决这个问题，它的贡献在于：1）我们提出了一个AT-LDA模型，该模型结合了作者主题模型和拓扑结构分析，在一个统一的过程中发现密集连接的社区和社区主题; 2）我们的框架捕获社区结构和它们的演变与历史社区结构的时间平滑。在真实数据集上的实验结果表明，该算法能够检测出感兴趣的社区及其演化模式。
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.