Multi-way Clustering for Heterogeneous Information Networks with General Network Schema

Abstract

Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks. However, most studies assume that the heterogeneous information networks usually follow some simple schemas, such as bi-typed network and star network schema. In this paper, we propose a multi-way clustering framework for heterogeneous information networks with general network schema, which can cluster multiple types of objects simultaneously. The types of objects and relations in the heterogeneous information networks are modeled as a multi-way array, i.e., tensor. Based on the nonnegative tensor decomposition, we partition different types of objects into different clusters simultaneously. The experimental results on both synthetic datasets and real-world dataset show that our proposed clustering framework can deal with the heterogeneous information networks well, and outperforms the state-of-the-art clustering algorithms.