Common Neighborhood Sub-graph Density as a Similarity Measure for Community Detection

Abstract

Community detection in networks involves grouping nodes on a graph into clusters such that connections between groups are sparse while nodes within groups are densely connected. Despite the success of clustering based community detection methods, there have been few efforts to devise similarity metrics between nodes for clustering algorithms that measures the likeliness of two nodes belonging to the same community. In this paper we present a new similarity measure based on the density of a sub-graph constructed by common neighbors of two nodes in question. The proposed metric is referred to as common neighborhood sub-graph density (CND) and is combined with affinity propagation to detect communities from network data. We apply community detection algorithms with CND to real-world benchmark data sets to demonstrate its useful behavior in the task of community detection in networks.