Clustering and Ranking in Heterogeneous Information Networks via Gamma-Poisson Model

Clustering and ranking have been successfully applied independently to homogeneous information networks, containing only one type of objects. However, real-world information networks are oftentimes heterogeneous, containing multiple types of objects and links. Recent research has shown that clustering and ranking can actually mutually enhance each other, and several techniques have been developed to integrate clustering and ranking together on a heterogeneous information network. To the best of our knowledge, however, all of such techniques assume the network follows a certain schema. In this paper, we propose a probabilistic generative model that simultaneously achieves clustering and ranking on a heterogeneous network that can follow arbitrary schema, where the edges from different types are sampled from a Poisson distribution with the parameters determined by the ranking scores of the nodes in each cluster. A variational Bayesian inference method is proposed to learn these parameters, which can be used to output ranking and clusters simultaneously. Our method is evaluated on both synthetic and real-world networks extracted from the DBLP and YELP data. Experimental results show that our method outperforms the state-of-the-art baselines.