  Overlapped community detection in social networks has become an important
research area with the increasing popularity and complexity of the networks.
Most of the existing solutions are either centralized or parallel algorithms,
which are computationally intensive - require complete knowledge of the entire
networks. But it isn't easy to collect entire network data because the size of
the actual networks may be prohibitively large. This may be a result of either
privacy concerns or technological impediments. Performing in-network
computation solves both problems utilizing the computational capability of the
individual nodes of the network. Simultaneously, nodes communicate and share
data with their neighbors via message passing, which may go a long way toward
mitigating individual nodes' privacy concerns in the network. All the
aforementioned concerns motivated us to design a decentralized or distributed
technique to detect overlapped communities in a large-scale network. It is
desirable because this technique does not offer a single point of failure, and
the system as a whole can continue to function even when many of the nodes
fail. In this paper, we address the overlapped community detection problem for
large-scale networks. We present an efficient distributed algorithm, named
DOCD, to identify the overlapped communities in the network. The DOCD
algorithm's efficiency is verified with extensive simulation study on real
network data such as Dolphin, Zachary karate club, Football club, and Facebook
ego networks. We show that DOCD algorithm is capable of keeping the
asymptotically same results with the existing classical centralized algorithms
in terms of community modularity and the number of identified communities. The
DOCD algorithm can also efficiently identify the overlapped nodes and
overlapped communities with a small number of rounds of communication and
computation.
