# Stochastic blockmodels for exchangeable collections of networks

arXiv: https://arxiv.org/abs/1606.05277 · HTML: https://mlsanity.com/paper/1606.05277/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2016.

## Built on by (most cited first, 2 of 2)

- [A Latent Space Model for Multilayer Network Data](https://mlsanity.com/paper/2102.09560/) (2021)
- [Nested stochastic block model for simultaneously clustering networks and nodes](https://mlsanity.com/paper/2307.09210/) (2023)

## Abstract (arXiv)

We construct a novel class of stochastic blockmodels using Bayesian nonparametric mixtures. These model allows us to jointly estimate the structure of multiple networks and explicitly compare the community structures underlying them, while allowing us to capture realistic properties of the underlying networks. Inference is carried out using MCMC algorithms that incorporates sequentially allocated split-merge steps to improve mixing. The models are illustrated using a simulation study and a variety of real-life examples.