Central limit theorems for the eigenvalues of graph Laplacians on data clouds

TL;DR

This paper establishes central limit theorems for the eigenvalues of graph Laplacians constructed from data points on a manifold, providing a Gaussian fluctuation description and statistical efficiency insights.

Contribution

It introduces the first CLTs for eigenvalues of graph Laplacians on data clouds, with explicit asymptotic variance and geometric interpretation, linking to Fisher-Rao and Cramer-Rao bounds.

Findings

Eigenvalues are asymptotically Gaussian with explicit variance.

Results hold under suitable assumptions on data and graph parameters.

Numerical experiments support theoretical findings.

Abstract

Given i.i.d.\ samples $X_n =\{ x_1, \dots, x_n \}$ from a distribution supported on a low dimensional manifold ${M}$ embedded in Eucliden space, we consider the graph Laplacian operator $\Delta_n$ associated to an $\varepsilon$-proximity graph over $X_n$ and study the asymptotic fluctuations of its eigenvalues around their means. In particular, letting $\hat{\lambda}_l^\varepsilon$ denote the $l$-th eigenvalue of $\Delta_n$, and under suitable assumptions on the data generating model and on the rate of decay of $\varepsilon$, we prove that $\sqrt{n } (\hat{\lambda}_{l}^\varepsilon - \mathbb{E}[\hat{\lambda}_{l}^\varepsilon] )$ is asymptotically Gaussian with a variance that we can explicitly characterize. A formal argument allows us to interpret this asymptotic variance as the dissipation of a gradient flow of a suitable energy with respect to the Fisher-Rao geometry. This geometric interpretation allows us to give, in turn, a statistical interpretation of the asymptotic variance in terms of a Cramer-Rao lower bound for the estimation of the eigenvalues of certain weighted Laplace-Beltrami operator. The latter interpretation suggests a form of asymptotic statistical efficiency for the eigenvalues of the graph Laplacian. We also present CLTs for multiple eigenvalues and through several numerical experiments explore the validity of our results when some of the assumptions that we make in our theoretical analysis are relaxed.