Title: Overlapping community detection using superior seed set selection in social networks,
Abstract: Community discovery in the social network is one of the tremendously
expanding areas which earn interest among researchers for the past one decade.
There are many already existing algorithms. However, new seed-based algorithms
establish an emerging drift in this area. The basic idea behind these
strategies is to identify exceptional nodes in the given network, called seeds,
around which communities can be located. This paper proposes a blended strategy
for locating suitable superior seed set by applying various centrality measures
and using them to find overlapping communities. The examination of the
algorithm has been performed regarding the goodness of the identified
communities with the help of intra-cluster density and inter-cluster density.
Finally, the runtime of the proposed algorithm has been compared with the
existing community detection algorithms showing remarkable improvement.