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

Information networks are widely applied to represent objects and their interactions in real-world systems in different academic fields. Examples include gene regulatory networks, semantic networks and social networks. As a result, network analysis draws plenty of attention from the research communities. Clustering and ranking are the most widely applied network analysis techniques. These techniques have been successfully applied independently to homogeneous information networks, i.e. networks that contain only one type of objects and links. However, real-world information networks are oftentimes heterogeneous, containing multiple types of objects and links. In addition, recent research has shown that clustering and ranking can mutually enhance each other. In this talk, I will introduce a probabilistic generative model that simultaneously achieves clustering and ranking in heterogeneous information networks, where edges from different types are modeled as samples from Poisson distributions with parameters determined by the “ranking scores” of the vertices in each cluster. The model is applied to two real-world networks extracted from DBLP and YELP data to illustrate its effectiveness.

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