  Community discovery is the general process of attaining assortative
communities from a network: collections of nodes that are densely connected
within yet sparsely connected to the rest of the network. While community
discovery has been well studied, few such techniques exist for heterogeneous
networks, which contain different types of nodes and possibly different
connectivity patterns between the node types. In this paper, we introduce a
framework 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, ECoHeN
identifies communities that are significantly more densely connected than
expected given the node types and connectivity of its membership. Specifically,
the ECoHeN algorithm extracts communities one at a time through a dynamic set
of iterative updating rules, is guaranteed to converge, and imposes no
constraints on the type composition of extracted communities. To our knowledge
this is the first discovery method that distinguishes and identifies both
homogeneous and heterogeneous, possibly overlapping, community structure in a
network. We demonstrate the performance of ECoHeN through simulation and in
application to a political blogs network to identify collections of blogs which
reference one another more than expected considering the ideology of its'
members.
