Community structure exploration considering latent link patterns in complex networks

Community detection using statistical models is a promising research area in network analysis. Most existing statistical models for this task cannot be fitted well for various community structures. In this paper, we propose a model incorporating the latent link patterns for detecting effectively various community structures. It can automatically discover the number and the sizes of communities by grouping the vertices owning the same latent link pattern, which is meaningful and explainable. An inference approach based on collapsed Gibbs sampling is proposed to estimate the parameters of our model. Experiments on 13 real-world networks demonstrate our model outperforms four state-of-the-art approaches on most of the datasets and is competent to explore various community structures. In addition, our model can detect some hidden link patterns that offer extra information for network analysis.