Estimating the Number of Communities in Complex Network

Community structure is one of the important characteristics of complex networks. Community discovery has important theoretical significance and practical value. At present, accurate estimation of the number of communities is the key and difficult issue in community discovery. Inspired by the idea of Bayesian inference, this paper deduces the relationship between maximum possible partition and average mutual information and information entropy, and proposes a framework algorithm Number_Accurate_Estimation (short for NAE) and a concrete implementation to estimate the community number. Several typical algorithms for community number estimation are compared on several typical data sets, and experiments show the effectiveness of our method.