Comparative Analysis of Graph Clustering Algorithms for Detecting Communities in Social Networks

Abstract

Community detection in social networks is often thought of a challenged domain that has not been explored completely. In today’s digital world, it is forever laborious to make a relationship between people or objects. Community detection helps us to find such relationships or build such relationships. It also can facilitate bound organizations to induce the opinion of their product from certain people. Many algorithms have emerged over the years which detect communities in the social networks. We performed a comparative analysis between six completely different bunch of algorithms for detecting communities in social network by taking into account parameters like run-time, cluster size, normalized mutual data, adjusted random score and average score.