Scalable MCMC in Degree Corrected Stochastic Block Model

Abstract

Community detection from graphs has many applications in machine learning, biological and social sciences. While there is a broad spectrum of literature based on various approaches, recently there has been a significant focus on inference algorithms for statistical models of community structure. These algorithms strive to solve an inference problem based on a generative model of the network. Recent advances in stochastic gradient MCMC have played a crucial role in improving the scalability of these techniques. In this paper, we propose a version of a degree corrected stochastic block model and present an MCMC based inference algorithm. Experimental results on several real world networks demonstrate the effectiveness of the proposed approach.