A novel algorithm for community detection and influence ranking in social networks

Community detection and influence analysis are significant notions in social networks. We exploit the implicit knowledge of influence-based connectivity and proximity encoded in the network topology, and propose a novel algorithm for both community detection and influence ranking. Using a new influence cascade model, the algorithm generates an influence vector for each node, which captures in detail how the node's influence is distributed through the network. Similarity in this influence space defines a new, meaningful and refined connectivity measure for the closeness of any pair of nodes. Our approach not only differentiates the influence ranking but also effectively finds communities in both undirected and directed networks, and incorporates these two important tasks into one integrated framework. We demonstrate its superior performance with extensive tests on a set of real-world networks and synthetic benchmarks.