CALF-SBM: A Covariate-Assisted Latent Factor Stochastic Block Model

Music City Center 

We propose a novel network generative model extended from the standard stochastic block model by concurrently utilizing observed node-level information and accounting for network enabled nodal heterogeneity. The proposed model is so called covariate-assisted latent factor stochastic block model (CALF-SBM), inference of which is done in a fully Bayesian framework. The primary application of CALF-SBM in the present research is focused on community detection, where a model-selection-based method is employed to estimate the number of communities if it is unknown. To assess the performance of CALF-SBM, an extensive simulation study is carried out, including comparisons with multiple classical and modern network clustering algorithms. Lastly, the paper presents two real data applications respectively based on an extremely new network data demonstrating collaborative relationships of the otolaryngologists in the United States and a traditional aviation network data collecting information about direct flights among major airports in the United States and Canada.