Topic Modeling with Network Regularization
In this paper, we formally define the problem of topic modeling with network structure (TMN). We propose a novel solution to this problem, which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data. The proposed method combines topic modeling and social network analysis, and leverages the power of both statistical topic models and discrete regularization. The output of this model can summarize well topics in text, map a topic onto the network, and discover topical communities. With appropriate instantiations of the topic model and the graph-based regularizer, our model can be applied to a wide range of text mining problems such as authortopic analysis, community discovery, and spatial text mining. Empirical experiments on two data sets with different genres show that our approach is effective and outperforms both text-oriented methods and network-oriented methods alone. The proposed model is general; it can be applied to any text collections with a mixture of topics and an associated network structure.
statistical topic models social networks graphbased regularization
Qiaozhu Mei Deng Cai Duo Zhang ChengXiang Zhai
Department of Computer Science University of Illinois at Urbana-Champaign
国际会议
第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)
北京
英文
2008-04-21（万方平台首次上网日期，不代表论文的发表时间）