# Scalable MCMC for Mixed Membership Stochastic Blockmodels

arXiv: https://arxiv.org/abs/1510.04815 · HTML: https://mlsanity.com/paper/1510.04815/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2015.

## Built on by (most cited first, 7 of 7)

- [Learning a Latent Simplex in Input-Sparsity Time](https://mlsanity.com/paper/2105.08005/) (2021)
- [A sharp uniform-in-time error estimate for Stochastic Gradient Langevin Dynamics](https://mlsanity.com/paper/2207.09304/) (2022)
- [Uncertainty Quantification on Graph Learning: A Survey](https://mlsanity.com/paper/2404.14642/) (2024)
- [Estimating the number of clusters of a Block Markov Chain](https://mlsanity.com/paper/2407.18287/) (2024)
- [Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems](https://mlsanity.com/paper/2312.11486/) (2023)
- [Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC](https://mlsanity.com/paper/2403.00044/) (2024)
- [Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo](https://mlsanity.com/paper/2606.00309/) (2026)

## Abstract (arXiv)

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.