Overlap Community Detection Based on Node Convergence Degree

Community structure is a common feature in real-world network. Overlap community detection is an important method to analyze topology structure and function of the network. Most algorithms are based on the network structure, without considering the node attributes. In this paper, we propose an overlapping community detection algorithm based on node convergence degree which combines the network topology with the node attributes. In our method, PageRank algorithm is used to get the importance of each node in the global network and utilize the local network (local neighbors) to measure the structure convergence degree. Then, node convergence degree combining node attributes and structure convergence degree is designed. Finally, the overlap communities can be identified by the Spectral Cluster based on node convergence degree. Experiments results demonstrate effectiveness and better performance of our method.