A novel probabilistic clustering model for heterogeneous networks

Heterogeneous networks, consisting of multi-type objects coupled with various relations, are ubiquitous in the real world. Most previous work on clustering heterogeneous networks either converts them into homogeneous networks or simplifies the modeling of the heterogeneity in terms of specific objects, structures or assumptions. However, few studies consider all relevant objects and relations, and trade-off between integrating relevant objects and reducing the noises caused by relations across objects. In this paper, we propose a general probabilistic graphical model for clustering heterogeneous networks. First, we present a novel graphical representation based on our basic assumptions: different relation types produce different weight distributions to specify intra-cluster probability between two objects, and clusters are formed around cluster cores. Then, we derive an efficient algorithm called PROCESS, standing for PRObabilistic Clustering modEl for heterogeneouS networkS. PROCESS employs a balance-controlled message passing algorithm and mathematical programming for inference and estimation. Experimental results show that our approach is effective and significantly outperforms the state-of-the-art algorithms on both synthetic and real data from heterogeneous networks.