# Modeling heterogeneity in random graphs through latent space models: a selective review

arXiv: https://arxiv.org/abs/1402.4296 · HTML: https://mlsanity.com/paper/1402.4296/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2014.

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

- [A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations](https://mlsanity.com/paper/2204.05885/) (2022)
- [Conformal link prediction for false discovery rate control](https://mlsanity.com/paper/2306.14693/) (2023)
- [Error rate control for classification rules in multiclass mixture models](https://mlsanity.com/paper/2109.14235/) (2021)
- [A Generative Node-attribute Network Model for Detecting Generalized Structure](https://mlsanity.com/paper/2106.02878/) (2021)
- [Private Synthetic Graph Generation and Fused Gromov-Wasserstein Distance](https://mlsanity.com/paper/2502.11778/) (2025)
- [Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals](https://mlsanity.com/paper/2509.15989/) (2025)
- [Low-Complexity and Consistent Graphon Estimation from Multiple Networks](https://mlsanity.com/paper/2603.15578/) (2026)

## Abstract (arXiv)

We present a selective review on probabilistic modeling of heterogeneity in random graphs. We focus on latent space models and more particularly on stochastic block models and their extensions that have undergone major developments in the last five years.