Sharp detection thresholds for random geometric graphs with polynomial average degrees

Abstract

We study the random geometric graph formed by connecting $n$ independent uniform points on ${\mathbb{S}}^{d-1}$ whenever their inner product exceeds a threshold chosen to give edge density $p$. We establish the conjectured dimensionality threshold for indistinguishability from the Erdös-Rényi graph $G(n,p)$ throughout the sparse regime with polynomially growing average degree. Specifically, for every fixed $\delta >0$, if $p\to 0$, $p\ge n^{-1+\delta }$, and $d\gg (np\log (1/p){)}^3$, then the Kullback-Leibler (KL) divergence between the two distributions vanishes. Our proof builds on the posterior analysis framework introduced in previous work of the authors, combining a truncated second moment analysis with Fourier and spherical harmonic expansions. As in prior works, we reduce the KL bound to controlling a truncated ${\chi }^2$-divergence $\Delta$ for the neighborhood of the last vertex conditional on the graph induced by the preceding vertices. We then bound $\Delta$ using a Fourier expansion of the conditional likelihood ratio for this neighborhood, where the high-frequency contribution is made negligible by truncation to a typical-degree event, while a spherical harmonic expansion approximates the low-frequency contribution in terms of posterior moments of bounded-degree harmonic polynomials. The key observation is that these moments and $\Delta$ satisfy a coupled system of cavity recurrences, yielding a self-bounding inequality that implies the desired KL bound.

arXiv comment43 pages, 1 figure