# Random Geometric Graph: Some recent developments and perspectives

arXiv: https://arxiv.org/abs/2203.15351 · HTML: https://mlsanity.com/paper/2203.15351/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2022.

## Built on by (most cited first, 4 of 4)

- [Detection-Recovery Gap for Planted Dense Cycles](https://mlsanity.com/paper/2302.06737/) (2023)
- [Information-Theoretic Thresholds for Planted Dense Cycles](https://mlsanity.com/paper/2402.00305/) (2024)
- [Optimal community detection in dense bipartite graphs](https://mlsanity.com/paper/2505.18372/) (2025)
- [Aligning the Unseen in Attributed Graphs: Interplay between Graph Geometry and Node Attributes Manifold](https://mlsanity.com/paper/2601.22806/) (2026)

## Abstract (arXiv)

The Random Geometric Graph (RGG) is a random graph model for network data with an underlying spatial representation. Geometry endows RGGs with a rich dependence structure and often leads to desirable properties of real-world networks such as the small-world phenomenon and clustering. Originally introduced to model wireless communication networks, RGGs are now very popular with applications ranging from network user profiling to protein-protein interactions in biology. RGGs are also of purely theoretical interest since the underlying geometry gives rise to challenging mathematical questions. Their resolutions involve results from probability, statistics, combinatorics or information theory, placing RGGs at the intersection of a large span of research communities. This paper surveys the recent developments in RGGs from the lens of high dimensional settings and non-parametric inference. We also explain how this model differs from classical community based random graph models and we review recent works that try to take the best of both worlds. As a by-product, we expose the scope of the mathematical tools used in the proofs.