Efficient Overlapping Community Detection Using MapReduce-based Fuzzy C-Means Clustering on Seed Nodes

In social and real-time network applications, community detection is the very popular and rapidly expanding field of study. Recently, many community detection approaches have been developed. In instance, community detection has proved to be effective and successful in local development strategies. Nevertheless, there are few basic problems to expose the overlapping communities. Although certain techniques are not sensitive enough to demonstrate widespread overlaps, the maximal approaches allow the seeds to be initialized and parameters to be created. A new unsupervised Map Reduce dependent local expanding technique to overlap community dependent seed node finding is presented in this study. The proposed method finds the leader or seed nodes of communities by using simple graph metrics, including closeness, centrality, degree, and betweenness. It then finds the communities that follow the leader nodes. Map-Reduce-oriented is proposed to utilize the Fuzzy C-Means Clustering approach to find which communities overlap depending on the leader nodes. The experimental outcomes illustrate the proposed method (LBCD), which assess network graph allowed the overlapping community structures, is more effectual and confident when used to entire 11 actual-world data sets.