A topical link model for community discovery in textual interaction graph

Abstract

This paper is concerned with community discovery in textual interaction graph, where the links between entities are indicated by textual documents. Specifically, we propose a Topical Link Model(TLM), which leverages Hierarchical Dirichlet Process(HDP) to introduce hidden topical variable of the links. Other than the use of links, TLM can look into the documents on the links in detail to recover sound communities. Moreover, TLM is a nonparametric model, which is able to learn the number of communities from the data. Extensive experiments on two real world corpora show TLM outperforms two state-of-the-art baseline models, which verify the effectiveness of TLM in determining the proper number of communities and generating sound communities.