Cross Multi-Type Objects Clustering in Attributed Heterogeneous Information Network

Abstract Real-world networks usually consist of a large number of interacting, multi-typed components which are usually referred as heterogeneous information networks (HIN). HIN that associated with various attributes on nodes is defined as attributed HIN (or AHIN). Clustering is a fundamental task for HIN and AHIN. However, most of the current existing methods focus on single type nodes and there is very limited existing work that groups objects of different types into the same cluster. This is largely due to the reasons that object similarities can either be attribute-based or link-based between same type of nodes and it is challenging to incorporate both in a unified framework. To bridge this gap, in this paper, we propose a framework, namely Cross Multi-Type Objects Clustering in Attributed Heterogeneous Information Network, or CMOC-AHIN, to integrate both the attribute information and multi-type node clustering in a principled way. We empirically show superior performances of CMOC-AHINon three large scale challenging data sets and also summarize insights on the performances compared to other state-of-the-arts methodologies.