Contextual Information Based Community Detection in Attributed Heterogeneous Networks

Abstract

Community detection is an important network analysis task that has been studied by academy and industry for the last years. Community detection algorithms try to maximize the number of connections in each community and minimize the number of connections between different communities. Some of them consider not only the topological aspects of the networks but also try to explore existing information about the context of the application available in attributes of nodes and/or connections in order to find cohesive content communities. Those algorithmswere designed to run exclusively over homogeneous networks and cannot deal with heterogeneous structures. Nevertheless, typical real-world networks are heterogeneous. Thus, this article proposes ComDet, a community detection approach that fills this gap by taking into account topological and contextual information to detect communities in heterogeneous networks. The proposed approach uses data clustering as a pre-processing step for the community detection process in order to identify similar nodes that are directly or indirectly linked and organize them in cohesive and possibly overlapping communities. Experiments in three attributed heterogeneous networks show that ComDet leads to interesting partitions with cohesive content communities.