Community detection in social networks using consensus clustering

Abstract

Community detection is one of the most appealing research fields in computer science. Although many different methods have been proposed to cluster the nodes of a graph, none of these methods is complete. Each method has strengths and weaknesses in extracting highly coherent groups of nodes (i.e. communities or clusters). The differences that various methods show are typically due to two main factors: 1) structure of the network they operate on, and 2) the strategy they use to find clusters. Since none of these methods is optimal, it seems a good idea to combine them to take advantage of their strengths while minimizing their weaknesses. In this paper, we present a new approach for the community detection problem by considering an ensemble of community detection methods. We refer to our approach as "Mitra". The base methods employed in Mitra, use different techniques and strategies to find communities for different applications of network data analysis. Mitra employs some known base community detection methods, receives their results on a network and builds a bipartite network to combine the communities found by the base methods. Then the fast projection technique compresses and summarizes the bipartite network to a new unipartite one. Then we find the communities of the unipartite network in the final step. We evaluate our approach against real and artificial datasets and compare our method with each one of the base methods. Artificial datasets include a diverse collection of large scale benchmark graphs. In this work, the main experimental evaluation function is Normalized Mutual Information (NMI). We also use several measures to compare the quality and properties of final community structures of the partitions found by all methods.