  Community detection in online social networks has been a hot research topic
in recent years. Meanwhile, to enjoy more social network services, users
nowadays are usually involved in multiple online social networks
simultaneously, some of which can share common information and structures.
Networks that involve some common users are named as multiple "partially
aligned networks". In this paper, we want to detect communities of multiple
partially aligned networks simultaneously, which is formally defined as the
"Mutual Clustering" problem. The "Mutual Clustering" problem is very
challenging as it has two important issues to address: (1) how to preserve the
network characteristics in mutual community detection? and (2) how to utilize
the information in other aligned networks to refine and disambiguate the
community structures of the shared users? To solve these two challenges, a
novel community detection method, MCD (Mutual Community Detector), is proposed
in this paper. MCD can detect social community structures of users in multiple
partially aligned networks at the same time with full considerations of (1)
characteristics of each network, and (2) information of the shared users across
aligned networks. Extensive experiments conducted on two real-world partially
aligned heterogeneous social networks demonstrate that MCD can solve the
"Mutual Clustering" problem very well.
