  Heterogeneous networks are networks consisting of different types of nodes
and multiple types of edges linking such nodes. While community detection has
been extensively developed as a useful technique for analyzing networks that
contain only one type of nodes, very few community detection techniques have
been developed for heterogeneous networks. In this paper, we propose a
modularity based community detection framework for heterogeneous networks.
Unlike existing methods, the proposed approach has the flexibility to treat the
number of communities as an unknown quantity. We describe a Louvain type
maximization method for finding the community structure that maximizes the
modularity function. Our simulation results show the advantages of the proposed
method over existing methods. Moreover, the proposed modularity function is
shown to be consistent under a heterogeneous stochastic blockmodel framework.
Analyses of the DBLP four-area dataset and a MovieLens dataset demonstrate the
usefulness of the proposed method.
