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Threshold graphs are graphs that can be characterized in a number of different ways. For example, they are graphs that are $P_4,\ C_4,\ 2K_2$--free. They may also be characterized by a finite sequence of positive integers $a_1, \ldots,…

Combinatorics · Mathematics 2026-05-07 James L. Borg , Irene Sciriha , Zoia Sherman

In this paper we generalise the results on eigenvalues and eigenvectors of unnormalized (combinatorial) Laplacian of two-dimensional grid presented by Edwards:2013 first to a grid graph of any dimension, and second also to other types of…

Classical Analysis and ODEs · Mathematics 2019-09-02 Mieczysław A. Kłopotek

The hierarchical product of two graphs represents a natural way to build a larger graph out of two smaller graphs with less regular and therefore more heterogeneous structure than the Cartesian product. Here we study the eigenvalue spectrum…

Adaptation and Self-Organizing Systems · Physics 2016-11-28 Per Sebastian Skardal , Kirsti Wash

A complex unit hypergraph is a hypergraph where each vertex-edge incidence is given a complex unit label. We define the adjacency, incidence, Kirchoff Laplacian and normalized Laplacian of a complex unit hypergraph and study each of them.…

Combinatorics · Mathematics 2024-05-17 Raffaella Mulas , Nathan Reff

In this paper we consider higher isoperimetric numbers of a (finite directed) graph. In this regard we focus on the $n$th mean isoperimetric constant of a directed graph as the minimum of the mean outgoing normalized flows from a given set…

Combinatorics · Mathematics 2015-02-03 Amir Daneshgar , Hossein Hajiabolhassan , Ramin Javadi

The spectral graph theory provides an algebraical approach to investigate the characteristics of weighted networks using the eigenvalues and eigenvectors of a matrix (e.g., normalized Laplacian matrix) that represents the structure of the…

Physics and Society · Physics 2021-04-19 Yusuke Sakumoto , Masaki Aida

We review the properties of eigenvectors for the graph Laplacian matrix, aiming at predicting a specific eigenvalue/vector from the geometry of the graph. After considering classical graphs for which the spectrum is known, we focus on…

Spectral Theory · Mathematics 2023-01-23 J. -G. Caputo , A. Knippel

Motivated by discrete Laplacian differential operators with various accuracy orders in numerical analysis, we introduce new matrices attached to a simple graph that can be considered graph Laplacians with higher accuracy. In particular, we…

Combinatorics · Mathematics 2025-04-09 Mary Yoon

In spectral graph theory, the Cheeger's inequality gives upper and lower bounds of edge expansion in normal graphs in terms of the second eigenvalue of the graph's Laplacian operator. Recently this inequality has been extended to undirected…

Discrete Mathematics · Computer Science 2017-11-07 T-H. Hubert Chan , Zhihao Gavin Tang , Xiaowei Wu , Chenzi Zhang

Spectral methods that are based on eigenvectors and eigenvalues of discrete graph Laplacians, such as Diffusion Maps and Laplacian Eigenmaps are often used for manifold learning and non-linear dimensionality reduction. It was previously…

Numerical Analysis · Mathematics 2015-06-02 Amit Singer , Hau-tieng Wu

We derive Cheeger inequalities for directed graphs and hypergraphs using the reweighted eigenvalue approach that was recently developed for vertex expansion in undirected graphs [OZ22,KLT22,JPV22]. The goal is to develop a new spectral…

Data Structures and Algorithms · Computer Science 2022-11-18 Lap Chi Lau , Kam Chuen Tung , Robert Wang

We use the concept of intrinsic metrics to give a new definition for an isoperimetric constant of a graph. We use this novel isoperimetric constant to prove a Cheeger-type estimate for the bottom of the spectrum which is nontrivial even if…

Spectral Theory · Mathematics 2012-09-25 Frank Bauer , Matthias Keller , Radosław K. Wojciechowski

Graphs and hypergraphs are foundational structures in discrete mathematics. They have many practical applications, including the rapidly developing field of bioinformatics, and more generally, biomathematics. They are also a source of…

Combinatorics · Mathematics 2019-01-16 Mark Budden , Josh Hiller , Andrew Penland

We use two variational techniques to prove upper bounds for sums of the lowest several eigenvalues of matrices associated with finite, simple, combinatorial graphs. These include estimates for the adjacency matrix of a graph and for both…

Spectral Theory · Mathematics 2013-08-27 Evans M. Harell , Joachim Stubbe

The work in this thesis concerns the investigation of eigenvalues of the Laplacian matrix, normalized Laplacian matrix, signless Laplacian matrix and distance signless Laplacian matrix of graphs. In Chapter 1, we present a brief…

Combinatorics · Mathematics 2021-07-21 Bilal A. Rather

Because of the significant increase in size and complexity of the networks, the distributed computation of eigenvalues and eigenvectors of graph matrices has become very challenging and yet it remains as important as before. In this paper…

Numerical Analysis · Mathematics 2017-11-27 Konstantin Avrachenkov , Philippe Jacquet , Jithin Sreedharan

A generalized Fourier analysis on arbitrary graphs calls for a detailed knowledge of the eigenvectors of the graph Laplacian. Using the symmetries of the Cayley tree, we recursively construct the family of eigenvectors with exponentially…

Statistical Mechanics · Physics 2019-10-31 Ayşe Erzan , Aslı Tuncer

Let $G$ be a connected uniform hypergraphs with maximum degree $\Delta$, spectral radius $\lambda$ and minimum H-eigenvalue $\mu$. In this paper, we give some lower bounds for $\Delta-\lambda$, which extend the result of [S.M. Cioab\u{a},…

Combinatorics · Mathematics 2015-12-01 Jiang Zhou , Lizhu Sun , Changjiang Bu

In this article we introduce a definition of k-uniform thresholds hypergraphs through a binary sequence, a natural extension of the classical definition for thresholds graphs. We characterize some of its eigenvalues and multiplicities by…

Combinatorics · Mathematics 2026-02-26 Miriam Abdón , Lucas Portugal , Renata Del-Vecchio , Renata de Freitas

We introduce an unsupervised graph embedding that trades off local node similarity and connectivity, and global structure. The embedding is based on a generalized graph Laplacian, whose eigenvectors compactly capture both network structure…

Machine Learning · Computer Science 2020-10-01 Shay Deutsch , Stefano Soatto
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