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 algorithms
were 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