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Related papers: Constructing heat kernels on infinite graphs

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Let $G=(V, E)$ be a locally finite connected graph satisfying curvature-dimension conditions ($CDE(n, 0)$ or its strengthened version $CDE'(n, 0))$) and polynomial volume growth conditions of degree $m$. We systematically establish sharp…

Analysis of PDEs · Mathematics 2025-05-13 Yuanyang Hu

We introduce the first graph kernels for metric graphs via tropical algebraic geometry. In contrast to conventional graph kernels based on graph combinatorics such as nodes, edges, and subgraphs, our metric graph kernels are purely based on…

Machine Learning · Computer Science 2026-01-30 Yueqi Cao , Anthea Monod

We consider graphs associated to Delone sets in Euclidean space. Such graphs arise in various ways from tilings. Here, we provide a unified framework. In this context, we study the associated Laplace operators and show Gaussian heat kernel…

Spectral Theory · Mathematics 2017-04-26 Sebastian Haeseler , Xueping Huang , Daniel Lenz , Felix Pogorzelski

We prove a variant of the Davies-Gaffney-Grigor'yan Lemma for the continuous time heat kernel on graphs. We use it together with the Li-Yau inequality to obtain strong heat kernel estimates for graphs satisfying the exponential curvature…

Differential Geometry · Mathematics 2015-11-30 Frank Bauer , Bobo Hua , Shing-Tung Yau

In this paper, we prove two-sided heat kernel estimates on what we call "book-like" graphs. These are graphs consisting of pieces that satisfy the parabolic Harnack inequality that are glued together in a sufficiently nice way over a…

Probability · Mathematics 2026-03-06 Emily Dautenhahn , Laurent Saloff-Coste

In nonparametric regression, it is common for the inputs to fall in a restricted subset of Euclidean space. Typical kernel-based methods that do not take into account the intrinsic geometry of the domain across which observations are…

Methodology · Statistics 2021-11-04 David B Dunson , Hau-Tieng Wu , Nan Wu

This is first of series papers on new two-side Gaussian bounds for the heat kernel $H(x,y,t)$ on a complete manifold $(M,g)$. In this paper, on a complete manifold $M$ with $Ric(M)\geq 0$, we obtain new two-side Gaussian bounds for the heat…

Differential Geometry · Mathematics 2020-01-01 Xiangjin Xu

In this paper we study heat kernels associated to a Carnot group $G$, endowed with a family of collapsing left-invariant Riemannian metrics $\sigma_\e$ which converge in the Gromov-Hausdorff sense to a sub-Riemannian structure on $G$ as…

Analysis of PDEs · Mathematics 2013-07-22 Luca Capogna , Giovanna Citti , Maria Manfredini

In the sub-Riemannian manifolds, on the one hand, following Baudoin-Garofalo \cite{BaudoinGarofalo}, the upper bound for heat kernels associated to a class of locally subelliptic operators are given under the generalized curvature-dimension…

Mathematical Physics · Physics 2013-08-29 Huai Qian LI

Given i.i.d. observations uniformly distributed on a closed manifold $\mathcal{M}\subseteq \mathbb{R}^p$, we study the spectral properties of the associated empirical graph Laplacian based on a Gaussian kernel. Our main results are…

Statistics Theory · Mathematics 2024-02-27 Martin Wahl

By a discrete torus we mean the Cayley graph associated to a finite product of finite cycle groups with generating set given by choosing a generator for each cyclic factor. In this article we study the spectral theory of the combinatorial…

Combinatorics · Mathematics 2009-11-02 G. Chinta , J. Jorgenson , A. Karlsson

We use heat kernels or eigenfunctions of the Laplacian to construct local coordinates on large classes of Euclidean domains and Riemannian manifolds (not necessarily smooth, e.g. with $\mathcal{C}^\alpha$ metric). These coordinates are…

Analysis of PDEs · Mathematics 2008-10-09 Peter W. Jones , Mauro Maggioni , Raanan Schul

We consider compact metric graphs with an arbitrary self adjoint realisation of the differential Laplacian. After discussing spectral properties of Laplacians, we prove several versions of trace formulae, relating Laplace spectra to sums…

Mathematical Physics · Physics 2015-05-13 Jens Bolte , Sebastian Endres

We obtain matching two sided estimates of the heat kernel on a connected sum of parabolic manifolds, each of them satisfying the Li-Yau estimate. The key result is the on-diagonal upper bound of the heat kernel at a central point. Contrary…

Probability · Mathematics 2016-08-05 Alexander Grigor'yan , Satoshi Ishiwata , Laurent Saloff-Coste

We study the heat kernel for a Laplace type partial differential operator acting on smooth sections of a complex vector bundle with the structure group $G\times U(1)$ over a Riemannian manifold $M$ without boundary. The total connection on…

Mathematical Physics · Physics 2011-02-17 Ivan G. Avramidi , Guglielmo Fucci

This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals. In our framework, graph-based modeling is formulated as a graph system identification problem, where the goal…

Machine Learning · Computer Science 2018-03-08 Hilmi E. Egilmez , Eduardo Pavez , Antonio Ortega

Let $(X,g)$ be a product cone with the metric $g=dr^2+r^2h$, where $X=C(Y)=(0,\infty)_r\times Y$ and the cross section $Y$ is a $(n-1)$-dimensional closed Riemannian manifold $(Y,h)$. We study the upper boundedness of heat kernel associated…

Analysis of PDEs · Mathematics 2022-05-16 Xiaoqi Huang , Junyong Zhang

We prove the Li-Yau gradient estimate for the heat kernel on graphs. The only assumption is a variant of the curvature-dimension inequality, which is purely local, and can be considered as a new notion of curvature for graphs. We compute…

Analysis of PDEs · Mathematics 2015-12-02 Frank Bauer , Paul Horn , Yong Lin , Gabor Lippner , Dan Mangoubi , Shing-Tung Yau

We build upon a recently introduced class of quasi-graph random features (q-GRFs), which have demonstrated the ability to yield lower variance estimators of the 2-regularized Laplacian kernel (Choromanski 2023). Our research investigates…

Machine Learning · Computer Science 2024-10-14 Brooke Feinberg , Aiwen Li

Most state-of-the-art graph kernels only take local graph properties into account, i.e., the kernel is computed with regard to properties of the neighborhood of vertices or other small substructures. On the other hand, kernels that do take…

Machine Learning · Computer Science 2017-09-25 Christopher Morris , Kristian Kersting , Petra Mutzel