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We study how the spectral gap of the normalized Laplacian of a random graph changes when an edge is added to or removed from the graph. There are known examples of graphs where, perhaps counterintuitively, adding an edge can decrease the…

Combinatorics · Mathematics 2015-06-23 Ronen Eldan , Miklós Rácz , Tselil Schramm

We introduce and study Laplacians on a finite metric graph endowed with generalized densities, that is, measures of finite mass. One important motivation is that this setting provides a common framework for several interesting classes of…

Spectral Theory · Mathematics 2025-12-24 Kiyan Naderi , Noema Nicolussi

Laplace operators on finite compact metric graphs are considered under the assumption that matching conditions at graph vertices are of $\delta$ and $\delta'$ types. Assuming rational independence of edge lengths, necessary and sufficient…

Spectral Theory · Mathematics 2015-12-09 Yulia Ershova , Irina I. Karpenko , Alexander V. Kiselev

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

We consider the Laplacian in a domain squeezed between two parallel curves in the plane, subject to Dirichlet boundary conditions on one of the curves and Neumann boundary conditions on the other. We derive two-term asymptotics for…

Spectral Theory · Mathematics 2011-02-21 David Krejcirik

The study of complex systems benefits from graph models and their analysis. In particular, the eigendecomposition of the graph Laplacian lets emerge properties of global organization from local interactions; e.g., the Fiedler vector has the…

Machine Learning · Computer Science 2017-06-28 Dimitri Van De Ville , Robin Demesmaeker , Maria Giulia Preti

We consider the Laplacian in a domain squeezed between two parallel hypersurfaces in Euclidean spaces of any dimension, subject to Dirichlet boundary conditions on one of the hypersurfaces and Neumann boundary conditions on the other. We…

Spectral Theory · Mathematics 2014-07-29 David Krejcirik

In this paper, we consider a certain convolutional Laplacian for metric measure spaces and investigate its potential for the statistical analysis of complex objects. The spectrum of that Laplacian serves as a signature of the space under…

Statistics Theory · Mathematics 2022-04-14 Gilles Mordant , Axel Munk

In graph signal processing, the graph adjacency matrix or the graph Laplacian commonly define the shift operator. The spectral decomposition of the shift operator plays an important role in that the eigenvalues represent frequencies and the…

Numerical Analysis · Computer Science 2016-11-09 Stephen Kruzick , Jose M. F. Moura

We introduce a set of novel multiscale basis transforms for signals on graphs that utilize their "dual" domains by incorporating the "natural" distances between graph Laplacian eigenvectors, rather than simply using the eigenvalue ordering.…

Signal Processing · Electrical Eng. & Systems 2021-04-26 Alexander Cloninger , Haotian Li , Naoki Saito

In this paper, we prove a variant of the Burger-Brooks transfer principle which, combined with recent eigenvalue bounds for surfaces, allows to obtain upper bounds on the eigenvalues of graphs as a function of their genus. More precisely,…

Metric Geometry · Mathematics 2016-09-02 Omid Amini , David Cohen-Steiner

We develop a nonlinear spectral graph theory, in which the Laplace operator is replaced by the 1-Laplacian ?$\Delta_1$. The eigenvalue problem is to solve a nonlinear system involving a set valued function. In the study, we investigate the…

Spectral Theory · Mathematics 2016-10-31 Kung Ching Chang

We develop a theory of ultrametric graphons as limiting objects for random networks with nested hierarchical community structure. A graphon $W:[0,1]^2\to[0,1]$ is called ultrametric if $W(x,y)=w(d(x,y))$, where $d$ is an ultrametric on…

Spectral Theory · Mathematics 2026-05-14 Ángel Alfredo Morán Ledezma

In some previous works, the analytic structure of the spectrum of a quantum graph operator as a function of the vertex conditions and other parameters of the graph was established. However, a specific local coordinate chart on the…

Mathematical Physics · Physics 2019-10-02 Peter Kuchment , Jia Zhao

We develop a Hellmann--Feynman type perturbation theory for the discrete signed $p$-Laplacian and apply it to a parametrized perturbation by edge cuts. We show that the eigenvalues of the signed $p$-Laplacian can be characterized as citical…

Analysis of PDEs · Mathematics 2025-09-09 Gregory Berkolaiko , Matthias Hofmann

Graph embedding seeks to build a low-dimensional representation of a graph G. This low-dimensional representation is then used for various downstream tasks. One popular approach is Laplacian Eigenmaps, which constructs a graph embedding…

Machine Learning · Computer Science 2020-03-10 Leo Torres , Kevin S Chan , Tina Eliassi-Rad

We address the Laplacian on a perturbed periodic graph which might not be a periodic graph. We present a class of perturbed graphs for which the essential spectra of the Laplacians are stable even when the graphs are perturbed by adding and…

Mathematical Physics · Physics 2015-10-01 Itaru Sasaki , Akito Suzuki

In this paper, structural properties of chordal graphs are analysed, in order to establish a relationship between these structures and integer Laplacian eigenvalues. We present the characterization of chordal graphs with equal vertex and…

Discrete Mathematics · Computer Science 2019-07-12 Nair Maria Maia de Abreu , Claudia Marcela Justel , Lilian Markenzon

This paper surveys some recent results and progress on the extremal prob- lems in a given set consisting of all simple connected graphs with the same graphic degree sequence. In particular, we study and characterize the extremal graphs…

Combinatorics · Mathematics 2015-10-08 Xiao-Dong Zhang

Graphs possess exotic features like variable size and absence of natural ordering of the nodes that make them difficult to analyze and compare. To circumvent this problem and learn on graphs, graph feature representation is required. A good…

Machine Learning · Computer Science 2019-12-03 Edouard Pineau