A Gamma-Poisson Block Model for Community Detection in Directed Network

Abstract

Detecting communities in networks is to find subgroups of nodes with similar characteristics, which is commonly defined as finding groups of nodes with dense connection in undirected networks. However, communities in directed networks can represent connectivity patterns because of asymmetric relations' which is difficult to capture using traditional algorithms. In this paper, a Gamma-Poisson block model is proposed for community detection in directed networks, which can model not only assortative communities but also communities with various connectivity patterns due to a block matrix. The model can also be extended to undirected networks if we set the block matrix symmetric, and for assortative community detection task if we set the block matrix diagonal. We develop an efficient Gibbs sampling algorithm for the inference work, which can scale to large sparse networks since links other than node pairs are considered during each iteration. We compare our model with several previous ones on a variety of real-world networks and the results demonstrate the advantages in our model.