Optimal community detection method based on average mutual information

Abstract

This study proposes an optimal community detection method based on average mutual information (AMI). We calculate the optimal community partition using AMI. This method is applied to the non-overlapping community detection algorithm (GN) and the overlapping community detection algorithm (COPRA), and the AMI-GN algorithm and the AMI-COPRA algorithm are generated respectively. Compared with the performance of the original GN, FN, and IE algorithm, experimental results show that AMI-GN algorithm improves the quality of community detection. Furthermore, compared with the performance of the original COPRA algorithm and the LPPB algorithm, experimental results show that the AMI-COPRA algorithm improves the stability of the original COPRA algorithm, reduces the average number of iterations, and accelerates the convergence speed of the algorithm. Moreover, compared with the LPPB algorithm, the AMI-COPRA algorithm reveals that the quality of the community shows a little difference, but is more stable than the LPPB algorithm. Our study shows that the AMI-based methods can improve the performance of typical non-overlapping community discovery algorithms and overlapping community discovery algorithms.