Multi-typed Community Discovery in Dynamic Heterogeneous Information Networks through Tensor Method

Abstract

Community discovery in a dynamic heterogeneous information network is a challenging topic and quite more difficult than that in a traditional static homogeneous information network. Community in heterogeneous information network, named multi-typed community, contains multiple types of dynamic objects and links, which brings three challenges. Firstly, the multi-typed communities are heterogeneous. Secondly, The communities are constantly changing along time. Finally, the network schemas for different heterogeneous information networks are various. To overcome these challenges, we propose a multi-typed community discovery method for dynamic heterogeneous information networks through tensor method without the restriction of network schema. A tensor decomposition framework is designed to model the multi-typed community and address the community evolution. Experimental result on a real-world dataset demonstrates the efficiency of our framework.