# Community Detection on Euclidean Random Graphs

arXiv: https://arxiv.org/abs/1706.09942 · HTML: https://mlsanity.com/paper/1706.09942/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2017.

## Built on by (most cited first, 5 of 5)

- [Spectral clustering in the Gaussian mixture block model](https://mlsanity.com/paper/2305.00979/) (2023)
- [Community Recovery in the Geometric Block Model](https://mlsanity.com/paper/2206.11303/) (2022)
- [On the Robustness of Spectral Algorithms for Semirandom Stochastic Block Models](https://mlsanity.com/paper/2412.14315/) (2024)
- [Multi-Community Spectral Clustering for Geometric Graphs](https://mlsanity.com/paper/2508.00893/) (2025)
- [Spectral graph clustering with inhomogeneous latent geometry](https://mlsanity.com/paper/2608.11321/) (2026)

## Abstract (arXiv)

We study the problem of community detection (CD) on Euclidean random geometric graphs where each vertex has two latent variables: a binary community label and a ℝd valued location label which forms the support of a Poisson point process of intensity λ. A random graph is then drawn with edge probabilities dependent on both the community and location labels. In contrast to the stochastic block model (SBM) that has no location labels, the resulting random graph contains many more short loops due to the geometric embedding. We consider the recovery of the community labels, partial and exact, using the random graph and the location labels. We establish phase transitions for both sparse and logarithmic degree regimes, and provide bounds on the location of the thresholds, conjectured to be tight in the case of exact recovery. We also show that the threshold of the distinguishability problem, i.e., the testing between our model and the null model without community labels exhibits no phase-transition and in particular, does not match the weak recovery threshold (in contrast to the SBM).