  Detecting community structures in social networks has gained considerable
attention in recent years. However, lack of prior knowledge about the number of
communities, and their overlapping nature have made community detection a
challenging problem. Moreover, many of the existing methods only consider
static networks, while most of real world networks are dynamic and evolve over
time. Hence, finding consistent overlapping communities in dynamic networks
without any prior knowledge about the number of communities is still an
interesting open research problem. In this paper, we present an overlapping
community detection method for dynamic networks called Dynamic Bayesian
Overlapping Community Detector (DBOCD). DBOCD assumes that in every snapshot of
network, overlapping parts of communities are dense areas and utilizes link
communities instead of common node communities. Using Recurrent Chinese
Restaurant Process and community structure of the network in the last snapshot,
DBOCD simultaneously extracts the number of communities and soft community
memberships of nodes while maintaining the consistency of communities over
time. We evaluated DBOCD on both synthetic and real dynamic data-sets to assess
its ability to find overlapping communities in different types of network
evolution. The results show that DBOCD outperforms the recent state of the art
dynamic community detection methods.
