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In this paper we study absence of embedded eigenvalues for Schr\"odinger operators on non-compact connected Riemannian manifolds. A principal example is given by a manifold with an end (possibly more than one) in which geodesic coordinates…

Mathematical Physics · Physics 2011-09-12 K. Ito , E. Skibsted

Eigenvectors and eigenvalues of discrete graph Laplacians are often used for manifold learning and nonlinear dimensionality reduction. It was previously proved by Belkin and Niyogi that the eigenvectors and eigenvalues of the graph…

Information Theory · Computer Science 2015-07-02 Zuoqiang Shi

The standard notion of the Laplacian of a graph is generalized to the setting of a graph with the extra structure of a ``transmission`` system. A transmission system is a mathematical representation of a means of transmitting…

Combinatorics · Mathematics 2009-12-22 Sylvain E. Cappell , Edward Y. Miller

This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model a directed graph as a finite set of observations from a diffusion on a manifold endowed with a vector field.…

Machine Learning · Statistics 2014-06-03 Dominique Perrault-Joncas , Marina Meila

As a generalization of anti-invariant Riemannian submersions and Lagrangian Riemannian submersions, we introduce the notions of h-anti-invariant submersions and h-Lagrangian submersions from almost quaternionic Hermitian manifolds onto…

Differential Geometry · Mathematics 2015-07-17 Kwang-Soon Park

We extend the spectral theory of generalized Laplacians to integrable metrics on compact Riemann surfaces. As a consequence, we attach in a direct way, a holomorphic analytic torsion to any integrable metrics. We also provide a different…

Spectral Theory · Mathematics 2013-01-17 Mounir Hajli

A new class of isospectral graphs is presented. These graphs are isospectral with respect to both the normalised Laplacian on the discrete graph and the standard differential Laplacian on the corresponding metric graph. The new class of…

Spectral Theory · Mathematics 2023-02-20 Pavel Kurasov , Jacob Muller

The graph Laplacian operator is widely studied in spectral graph theory largely due to its importance in modern data analysis. Recently, the Fourier transform and other time-frequency operators have been defined on graphs using Laplacian…

Functional Analysis · Mathematics 2017-03-20 Matthew Begué , Kasso A. Okoudjou

The Laplacian matrix of a simple graph is the difference of the diagonal matrix of vertex degree and the (0,1) adjacency matrix. In the past decades, the Laplacian spectrum has received much more and more attention, since it has been…

Combinatorics · Mathematics 2013-10-31 Xiao-Dong Zhang

Main mathematical applications of Frobenius manifolds are in the theory of Gromov - Witten invariants, in singularity theory, in differential geometry of the orbit spaces of reflection groups and of their extensions, in the hamiltonian…

Algebraic Geometry · Mathematics 2007-05-23 Boris Dubrovin

Pseudodifferential parabolic equations with an operator square root arise in wave propagation problems as a one-way counterpart of the Helmholtz equation. The expression under the square root usually involves a differential operator and a…

Atmospheric and Oceanic Physics · Physics 2025-11-21 Matthias Ehrhardt , Jochen Glück , Pavel Petrov , Stefan Tappe

We consider a J-self-adjoint 2x2 block operator matrix L in the Feshbach spectral case, that is, in the case where the spectrum of one main-diagonal entry is embedded into the absolutely continuous spectrum of the other main-diagonal entry.…

Spectral Theory · Mathematics 2017-01-10 Sergio Albeverio , Alexander K. Motovilov

In this note we elaborate on some notions of surface area for discrete graphs which are closely related to the inverse degree. These notions then naturally lead to associated connectivity measures of graphs and to the definition of a…

Combinatorics · Mathematics 2026-03-09 Patrizio Bifulco , Joachim Kerner

Graph neural networks (GNNs) are fundamental tools in graph machine learning. The performance of GNNs relies crucially on the availability of informative node features, which can be limited or absent in real-life datasets and applications.…

Machine Learning · Computer Science 2025-12-08 Ziyao Cui , Edric Tam

This paper aims to establish theoretical foundations of graph product multilayer networks (GPMNs), a family of multilayer networks that can be obtained as a graph product of two or more factor networks. Cartesian, direct (tensor), and…

Physics and Society · Physics 2017-09-05 Hiroki Sayama

In this paper we study the Riesz transform on complete and connected Riemannian manifolds $M$ with a certain spectral gap in the $L^2$ spectrum of the Laplacian. We show that on such manifolds the Riesz transform is $L^p$ bounded for all $p…

Spectral Theory · Mathematics 2010-05-18 Lizhen Ji , Peer Kunstmann , Andreas Weber

These are lecture notes for the Current Developments in Mathematics conference at Harvard, November, 2011. We discuss topological, probabilistic and combinatorial aspects of the Laplacian on a graph embedded on a surface. The three main…

Probability · Mathematics 2012-03-07 Richard Kenyon

In this survey we review positive inverse spectral and inverse resonant results for the following kinds of problems: Laplacians on bounded domains, Laplace-Beltrami operators on compact manifolds, Schr\"odinger operators, Laplacians on…

Spectral Theory · Mathematics 2013-08-28 Kiril Datchev , Hamid Hezari

We introduce the concept of completely positive roots of completely positive maps on operator algebras. We do this in different forms: as asymptotic roots, proper discrete roots and as continuous one-parameter semigroups of roots. We…

Operator Algebras · Mathematics 2020-04-21 B. V. Rajarama Bhat , Robin Hillier , Nirupama Mallick , Vijaya Kumar U

The diffusion maps embedding of data lying on a manifold has shown success in tasks such as dimensionality reduction, clustering, and data visualization. In this work, we consider embedding data sets that were sampled from a manifold which…

Machine Learning · Computer Science 2024-08-08 Eitan Rosen , Xiuyuan Cheng , Yoel Shkolnisky