BAYESIAN DEGREE-CORRECTED STOCHASTIC BLOCK MODELS FOR COMMUNITY DETECTION

Community detection in networks has drawn much attention in diverse fields, especially social sciences. Given its significance, there has been a large body of literature among which many are not statisti-cally based. In this paper, we propose a novel stochastic blockmodel based on a logistic regression setup with node correction terms to better address this problem. We follow a Bayesian approach that ex-plicitly captures the community behavior via prior specification. We then adopt a data augmentation strategy with latent Pólya-Gamma variables to obtain posterior samples. We conduct inference based on a canonically mapped centroid estimator that formally addresses label non-identifiability. We demonstrate the novel proposed model and es-timation on real-world as well as simulated benchmark networks and show that the proposed model and estimator are more flexible, repre-sentative, and yield smaller error rates when compared to the MAP estimator from classical degree-corrected stochastic blockmodels. 1. Introduction. Network