# Bayesian estimation of the latent dimension and communities in stochastic blockmodels

## TL;DR

This paper introduces a Bayesian model that automatically determines the latent space dimension and number of communities in stochastic blockmodels, improving community detection in networks.

## Contribution

A novel Bayesian approach for simultaneous, automatic estimation of latent dimension and community count in stochastic blockmodels, extending to directed and bipartite graphs.

## Key findings

- Effective in recovering community structure from simulated data
- Shows promising results on real-world network data
- Outperforms traditional methods requiring pre-specified parameters

## Abstract

Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theoretical guarantees. The embedding can be used to estimate the community structure of the network, with strong consistency results in the stochastic blockmodel framework. One of the main practical limitations of standard algorithms for community detection from spectral embeddings is that the number of communities and the latent dimension of the embedding must be specified in advance. In this article, a novel Bayesian model for simultaneous and automatic selection of the appropriate dimension of the latent space and the number of blocks is proposed. Extensions to directed and bipartite graphs are discussed. The model is tested on simulated and real world network data, showing promising performance for recovering latent community structure.