mlsanityData to 13 Oct 2026

Strong uniform convergence of Laplacians of random geometric and directed kNN graphs on compact manifolds

arXiv 2212.10287 ↗First posted 20 Dec 2022math.PR stat.MLcited by 1

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Abstract (arXiv)

Consider n points independently sampled from a density p of class C2 on a smooth compact d-dimensional sub-manifold M of ℝm, and consider the generator of a random walk visiting these points according to a transition kernel K. We study the almost sure uniform convergence of this operator to the diffusive Laplace-Beltrami operator when n tends to infinity. This work extends known results of the past 15 years. In particular, our result does not require the kernel K to be continuous, which covers the cases of walks exploring kNN-random and geometric graphs, and convergence rates are given. The distance between the random walk generator and the limiting operator is separated into several terms: a statistical term, related to the law of large numbers, is treated with concentration tools and an approximation term that we control with tools from differential geometry. The convergence of kNN Laplacians is detailed.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 2 of its 31 references that are in the map. First posted on arXiv 20 Dec 2022.

Builds on

2 of 31 references.

References · 2

  1. 2018Error estimates for spectral convergence of the graph Laplacian on random geometric graphs towards the Laplace--Beltrami operatorcited by 70
  2. 2020Convergence of Graph Laplacian with kNN Self-tuned Kernelscited by 17

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1 paper.