Latent Community Topic Analysis

Abstract

This article studies the problem of latent community topic analysis in text-associated graphs. With the development of social media, a lot of user-generated content is available with user networks. Along with rich information in networks, user graphs can be extended with text information associated with nodes. Topic modeling is a classic problem in text mining and it is interesting to discover the latent topics in text-associated graphs. Different from traditional topic modeling methods considering links, we incorporate community discovery into topic analysis in text-associated graphs to guarantee the topical coherence in the communities so that users in the same community are closely linked to each other and share common latent topics. We handle topic modeling and community discovery in the same framework. In our model we separate the concepts of community and topic, so one community can correspond to multiple topics and multiple communities can share the same topic. We compare different methods and perform extensive experiments on two real datasets. The results confirm our hypothesis that topics could help understand community structure, while community structure could help model topics.