Consensus Clustering Approach for Discovering Overlapping Nodes in Social Networks

Community discovery is an important problem that has been addressed in social networks through multiple perspectives. Most of these algorithms discover disjoint communities and yield widely varying results with regard to number of communities as well as community membership. We utilize this information positively by interpreting the results as opinions of different algorithms regarding membership of a node in a community. A novel approach to discovering overlapping nodes is proposed based on Consensus Clustering and we design two algorithms, namely core-consensus and periphery-consensus. The algorithms are implemented on LFR networks which are synthetic bench mark data sets created for community discovery and comparative performance is presented. It is shown that overlapping nodes are detected with a high Recall of above 96 % with an average F-measure of nearly 75% for dense networks and 65% for sparse networks which are on par with high-performing algorithms in the literature.