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Community discovery is the general process of attaining assortativecommunities from a network: collections of nodes that are densely connectedwithin yet sparsely connected to the rest of the network. While communitydiscovery has been well studied, few such techniques exist for heterogeneousnetworks, which contain different types of nodes and possibly differentconnectivity patterns between the node types. In this paper, we introduce aframework called ECoHeN, which \textbf{e}xtracts \textbf{co}mmunities from a\textbf{he}terogeneous \textbf{n}etwork in a statistically meaningful way.Using a heterogeneous configuration model as a reference distribution, ECoHeNidentifies communities that are significantly more densely connected thanexpected given the node types and connectivity of its membership. Specifically,the ECoHeN algorithm extracts communities one at a time through a dynamic setof iterative updating rules, is guaranteed to converge, and imposes noconstraints on the type composition of extracted communities. To our knowledgethis is the first discovery method that distinguishes and identifies bothhomogeneous and heterogeneous, possibly overlapping, community structure in anetwork. We demonstrate the performance of ECoHeN through simulation and inapplication to a political blogs network to identify collections of blogs whichreference one another more than expected considering the ideology of its'members.

ECoHeN: A Hypothesis Testing Framework for Extracting Communities from Heterogeneous Networks