Scalable MCMC for Mixed Membership Stochastic Blockmodels
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.