Sharp detection and triangle likelihood limits for quadratic spherical kernel graphs

Source abstract

We study whether quadratic spherical geometry can be detected from a single observed graph. Latent positions are independent and uniform on $\mathbb S^{d-1}$; conditional on them, edges are independent with connection probability $K_{d,β}(t)=[1+β(dt^2-1)/(d-1)]/2$, where $0<|β|\le1$. We test this model against $G(n,1/2)$. Writing $N=(d-1)(d+2)/2$ for the degree-two harmonic multiplicity, we prove that the total variation distance tends to one when $N\ll n^{3/4}|β|^{3/2}$ and to zero when $N\gg n^{3/4}|β|^{3/2}$. At criticality, if $N\to\infty$ and $Δ_n=\sqrt{\binom n3}\,|β|^3/N^2\toΔ\in(0,\infty)$, the signed triangle captures all asymptotic testing information and the total variation distance tends to $2Φ(Δ/2)-1$, where $Φ$ is the standard normal distribution function. Polynomial finite-sample error bounds hold under polynomial signal scaling. The proof controls the likelihood of the dependent quadratic feature law through spectral recursions, covariance truncation and interpolation from a symmetric prior.