Mapreduce model for finding closely knit communities in large scale networks

In this paper, we propose a scalable method for finding the evolving communities in complex networks. The various network properties that are prominent in real-world networks are studied. The proposed algorithm computes the edge betweenness based on the transitive closure property combined with the greedy approach applied in Dijkstra's single source shortest path method. The major contribution is an improvement to GN algorithm in linear time for weighted undirected networks. The proposed algorithm is applied on Mapreduce to prove its scalability and enhance the performance in managing and analyzing the large scale networks. The experimentation of the distributed algorithm is tested on the private cluster and the results are as expected in the theoretical analysis.