Scalable MCMC for mixed membership stochastic blockmodels

Wenzhe Li, Sungjin Ahn, Max Welling

Research output: Contribution to conferencePaperpeer-review

27 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages723-731
Number of pages9
StatePublished - Jan 1 2016
Event19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016 - Cadiz, Spain
Duration: May 9 2016May 11 2016

Conference

Conference19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016
Country/TerritorySpain
CityCadiz
Period5/9/165/11/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Statistics and Probability

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