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.