A Scalable Algorithm for Discovering Topologies in Social Networks

Abstract

Discovering topologies in a social network targets various business applications such as finding key influencers in a network, recommending music movies in virtual communities, finding active groups in network and promoting a new product. Since social networks are large in size, discovering topologies from such networks is challenging. In this paper, we present a scalable topology discovery approach using Giraph platform and perform (i) graph structural analysis and (ii) graph mining. For graph structural analysis, we consider various centrality measures. First, we find top-K centrality vertices for a specific topology (e.g. star, ring and mesh). Next, we find other vertices which are in the neighborhood of top centrality vertices and then create the cluster based on structural density. We compare our clustering approach with DBSCAN algorithm on the basis of modularity parameter. The results show that clusters generated through structural density parameter are better in quality than generated through neighborhood density parameter.