A Hybrid Spectral Method For Network Community Detection

Community detection has been paid much attention, and a large number of community-detection methods have been proposed in the last decade. Spectral methods are widely used in many applications due to their solid mathematical foundations. In this paper, we propose a hybrid spectral method to effectively identify communities from networks. This method begins with a network-sparsification operation, which is expected to remove some between-community edges from the network to make the community boundaries clearer and sharper, then it utilizes an iterative spectral bisection algorithm to partition the network into small communities, and finally some of the small communities are merged to obtain the resulting community structure. We conducted extensive experiments on five real-world networks and two artificial networks, the experimental results show that our proposed method can extract high-quality community structures from networks effectively.