A Multi-label Propagation Algorithm for Community Detection Based on Average Mutual Information

Abstract

Community structure is one of the vital characteristics of complex networks. How to effectively detect communities is a hot issue. From the perspective of information theory, the community structure of complex networks can be detected and revealed more accurately. This research introduces the average mutual information (AMI) into the detection process of the multi-label propagation algorithm (MLPA) and proposes a new community detection algorithm AMI-MLPA. The algorithm initially determines the propagation order according to the influence of nodes in the network. By selecting the label with stronger propagation intensity and smaller conditional entropy in the process of label propagation, a more reasonable community partition can be obtained. Experiments on real-world datasets and synthetic datasets show that the algorithm is better than FastGN, GN, and other LPA-based algorithms in general, with high accuracy of results on large-scale synthetic networks, which verifies the effectiveness of the algorithm.