Detecting Probabilistic Community with Topic Modeling on Sampling SubGraphs

Detecting communities plays a great important role in sociology, biology and computer science, disciplines where systems are often modeled as graphs. Such inherent community structures make us deeply understand about the networks and therefore have drawn significant interests among researchers. This paper describes a probabilistic community detection algorithm by modeling topic on sampling sub graphs. In this algorithm, the communities are modeled as latent topic variables of an LDA topic model and the vertices of sampling sub graphs are drawn from these topics with different probabilities. This paper also proposes a sub graph sampling algorithm and explores its impact on community detection performance. Our algorithm is evaluated by extensive experiments using many computer-generated artificial graphs and real-world networks. The results show that our algorithm is effective in detecting probabilistic community.