Community Detection using Unsupervised Learning Approach

Abstract

A community is referred to as a set of nodes in a network that has a high degree of connectivity with each other and a low degree of connectivity with other nodes in the same network. Community Detection is a renowned research problem for the past many years. The applications of Community Detection is spread across several domains like social networks, transportation networks, genetic networks, citation networks, web networks etc. In this work, several unsupervised learning techniques namely Louvain Algorithm, K-means clustering Algorithm and Gaussian Mixture Model have been examined to identify communities in social networks. The results demonstrated that the Louvain Algorithm outperforms the other two unsupervised learning techniques.