Mining overlapping and hierarchical communities in complex networks

• Introduce the coupling strength of two neighboring communities.
• A common architecture for detecting both overlapping and hierarchical organization.
• Propose the extended partition density and MOHCC algorithm.
• Conduct satisfactory experiments.

Community detection in the social networks is one of the most important tasks of social computing. Highly relevant researches indicate that the social network generally contains both an overlapping and hierarchical structure. This paper introduces an efficient and functional community detection algorithm MOHCC, which can concurrently discover overlapping and hierarchical organization in complex networks. This algorithm first extracts all maximal cliques from the original complex network. Merges all extracted maximal cliques into a dendrogram by using the aggregative framework presented in MOHCC. Finally, it cuts through the dendrogram and obtains a network partition with maximum extended partition density. Experimental results utilizing computer-generated artificial networks and real-world social benchmark networks give satisfactory correspondence.