  We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm
for scalable inference in mixed-membership stochastic blockmodels (MMSB). Our
algorithm is based on the stochastic gradient Riemannian Langevin sampler and
achieves both faster speed and higher accuracy at every iteration than the
current state-of-the-art algorithm based on stochastic variational inference.
In addition we develop an approximation that can handle models that entertain a
very large number of communities. The experimental results show that SG-MCMC
strictly dominates competing algorithms in all cases.
