Dynamic Topical Community Detection in Social Network: A Generative Model Approach

Abstract

Social networks that are dynamic contain rich network structure and content information. In dynamic networks, it is necessary to discover communities and their topical meanings. However, existing methods either only discover communities with ignoring their topical meaning in dynamic networks, or they discover communities and their topics in static networks. In this paper, we identify the problem of dynamic topical community detection and propose a dynamic topical community detection (DTCD) method to detect communities and their topical meanings in dynamic networks. The DTCD is a generative model integrating network structure, text, and time. The DTCD considers a community as a mixture of topics and generates the neighbors and documents of the node and their time stamps at the same time via the community. The latent variables are learned by collapsed Gibbs sampling. The DTCD not only can find communities and their topics, but also capture the temporal variations of communities and topics. The experimental results on two real-world datasets demonstrate the effectiveness of DTCD.