An Agglomerative Method to Construct Discrepant Cohesive Subgroups

This paper introduces an agglomerative method for detecting cohesive subgroups in networks based on geodesic distance. The algorithm starts with a set of nodes as "seed". Beginning with the seed nodes as initial clusters, the clusters grow by incorporating more nodes successively based on minimal average distance to the current members of the cluster as a criterion for cluster extension. This approach is combined with an optimization step to achieve high quality performance on subgroup detection. The resulting method for detecting discrepant cohesive subgroups has been tested on artificial benchmark graphs as well as real-world networks.