Scalable MCMC for Mixed Membership Stochastic Blockmodels

16 Oct 2015Wenzhe LiSungjin AhnMax Welling

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... (read more)

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