An Empirical Study Of Community And Sub-Community Detection In Social Networks Applying Newman-Girvan Algorithm

A social network can be represented by a set of human beings in which one member is connected to one or more members from the same set. We can obtain visual and mathematical models of human relationship by analysing a social network. There are several inherent properties of social networks such as power law distribution, centrality, small world network, modularity etc. Community structure is another important property of social network and it has gained tremendous popularity in terms of current research trends. With the increasing popularity, community structure is also getting equally complex within online social network services like Facebook, Google+, MySpace and Twitter. Newman-Girvan algorithm is the widely used community detection algorithm in social networks. This paper reflects the structure of communities as well as sub-communities occurring in a social network by applying Newman-Girvan algorithm. We have implemented this community detection algorithm on real world networks. We have given a new concept to detect sub-communities in real world networks in this paper. This paper is mainly focused on an empirical study of the Newman-Girvan algorithm.