Community Detection in Social Networks using Local Topology and Information Exchange

Abstract

The community detection problem has attracted a huge number of researchers from the scientific community because it helps to understand the relationship between structural and functional properties of social networks. In this paper, we propose a novel community detection approach namely, Closeness Similarity driven Information Diffusion based community detection (CSID) which makes full utilization of local topology for similarity computation followed by information exchange and modularity maximization. Most of the existing algorithms so far are not suitable to quantify both the local topology information and exchange of information between the nodes. Extensive experiments are carried out to perform a comparative evaluation on several real-world datasets. The experimental results show that the proposed approach gives better results in comparison to most of the baseline algorithms.