Combining link and content for community detection: a discriminative approach

In this paper, we consider the problem of combining link and content analysis for community detection from networked data, such as paper citation networks and Word Wide Web. Most existing approaches combine link and content information by a generative model that generates both links and contents via a shared set of community memberships. These generative models have some shortcomings in that they failed to consider additional factors that could affect the community memberships and isolate the contents that are irrelevant to community memberships. To explicitly address these shortcomings, we propose a discriminative model for combining the link and content analysis for community detection. First, we propose a conditional model for link analysis and in the model, we introduce hidden variables to explicitly model the popularity of nodes. Second, to alleviate the impact of irrelevant content attributes, we develop a discriminative model for content analysis. These two models are unified s...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu
• Keywords: Community Memberships, Content Analysis, Data Mining, KDD 2009, Keywords Discriminative Model