Community detection in complex networks using edge-deleting with restrictions

Abstract

Community detection is an important task with great practical significance for understanding the structure and function of complex networks in various fields. As the real world networks become larger and more complex, it is a challenge to achieve high quality of community partitioning. In order to identify community structure more effectively in complex networks, a new algorithm, which iteratively deletes edges with restrictions is proposed in this paper. The algorithm first makes use of the connection strength between vertices to divide the original network into some strongly connected communities with optimal modularity by the improved edge-deleting process, and finally reconnects the isolated vertices to initial communities for optimizing community structure. Experiments on the real-world and synthetic networks prove that the proposed algorithm achieves a competitive performance compared with other reference algorithms.