Detection of communities in dynamic social networks

Abstract

In the social world the sharing of knowledge, data's and concepts within a group is done through the network of interactions and relationships. A community is formed by a group of individuals of same interest to share common values within themselves at a higher rate than outside the community. It can be a social unit of any size. The significant chore while studying the social network is to identify the communities. Communities facilitate to determine the cluster of intermingling objects denoted as nodes and the relations within themselves. In this paper, we propose a integrated framework for community detection in social networks. To find the communities in a social network our proposed framework follows a density based approach. We implement our proposed approach for different real-time dataset and got better results. Thus the proposed framework efficiently detects the communities exist in the social network.