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arXiv · 2206.14896

Threshold for Detecting High Dimensional Geometry in Anisotropic Random Geometric Graphs

Matthew Brennan·Guy Bresler·Brice Huang
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Abstract

In the anisotropic random geometric graph model, vertices correspond to points drawn from a high-dimensional Gaussian distribution and two vertices are connected if their distance is smaller than a specified threshold. We study when it is possible to hypothesis test between such a graph and an Erd\H{o}s-R\'enyi graph with the same edge probability. If $n$ is the number of vertices and $\alpha$ is the vector of eigenvalues, Eldan and Mikulincer show that detection is possible when $n^3 \gg (\|\alpha\|_2/\|\alpha\|_3)^6$ and impossible when $n^3 \ll (\|\alpha\|_2/\|\alpha\|_4)^4$. We show detection is impossible when $n^3 \ll (\|\alpha\|_2/\|\alpha\|_3)^6$, closing this gap and affirmatively resolving the conjecture of Eldan and Mikulincer.

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Matthew Brennan, Guy Bresler, Brice Huang. 2022-06-29. Threshold for Detecting High Dimensional Geometry in Anisotropic Random Geometric Graphs. https://arxiv.org/abs/2206.14896

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