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Optimal detection of geometry in random graphs and kernel matrices at critical thresholds
Detection of geometry in random graphs and matrices has been extensively studied in recent years. For testing a dense random geometric graph on $n$ vertices with latent points in dimension $d$ against an Erdős--Rényi graph, the detection threshold $d\asymp n^3$ is well known since Bubeck, Ding, Elda
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