HINSCAN: Efficient Structural Graph Clustering Over Heterogeneous Information Networks

Structural graph clustering (SCAN) is one of the most popular graph clustering paradigms, and has attracted plenty of attention recently. Existing solutions assume that the input graphs is homogeneous, i.e., the vertices are of the same type. However, in many real applications, such as bibliographic networks and knowledge graphs, the input graphs is heterogeneous information networks which consist of multi-typed and interconnected objects, which makes SCAN cannot be applied to cluster. Therefore, in this paper, we study the SCAN problem over heterogeneous information networks. Based on the concept of meta-path, we propose two new structural graph clustering models first. Following these two new models, we design new algorithms to support the efficient clustering of a heterogeneous information network. We conduct extensive experiments on six real heterogeneous information networks, and the results demonstrate the effectiveness of our new models and the efficiency of our proposed clustering algorithms.