  As research into community finding in social networks progresses, there is a
need for algorithms capable of detecting overlapping community structure. Many
algorithms have been proposed in recent years that are capable of assigning
each node to more than a single community. The performance of these algorithms
tends to degrade when the ground-truth contains a more highly overlapping
community structure, with nodes assigned to more than two communities. Such
highly overlapping structure is likely to exist in many social networks, such
as Facebook friendship networks. In this paper we present a scalable algorithm,
MOSES, based on a statistical model of community structure, which is capable of
detecting highly overlapping community structure, especially when there is
variance in the number of communities each node is in. In evaluation on
synthetic data MOSES is found to be superior to existing algorithms, especially
at high levels of overlap. We demonstrate MOSES on real social network data by
analyzing the networks of friendship links between students of five US
universities.
