Community Detection in Networks Based on Modified PageRank and Stochastic Block Model

Community detection plays a vital role in network analysis, simplification, and compression, which reveals the network structure by dividing a network into several internally dense modules. Among plenty of methods, those based on statistical inference are widely used because they are theoretically sound and consistent. However, in many of them, the number of communities needs to be provided in advance or computed in a time-consuming way and parameters are usually initialized randomly, resulting in unstable accuracy and low convergence rate. In this paper, we present a community detection method based on modified PageRank and stochastic block model, which is able to compute the number of communities by finding community centers and initialize community assignments according to the centers and distance. Experiments on both synthetic and real-world networks prove that our method can intuitively give the number of communities, steadily get results of high NMI and modularity and efficiently speed up the convergence of optimizing likelihood probability.