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This paper presents a new look at the neural network (NN) robustness problem, from the point of view of graph theory analysis, specifically graph curvature. Graph curvature (e.g., Ricci curvature) has been used to analyze system dynamics…

Machine Learning · Computer Science 2024-12-17 Shuhang Tan , Jayson Sia , Paul Bogdan , Radoslav Ivanov

In [Evans, Francis 2022; Hendel] the authors investigated resistance distance in triangular grid graphs and observed several types of asymptotic behavior. This paper extends their work by studying the initial, non-asymptotic, behavior found…

Combinatorics · Mathematics 2024-06-25 Emily J. Evans , Russell J. Hendel

We define a new notion of total curvature, called net total curvature, for finite graphs embedded in Rn, and investigate its properties. Two guiding principles are given by Milnor's way of measuring the local crookedness of a Jordan curve…

Differential Geometry · Mathematics 2011-01-13 Robert Gulliver , Sumio Yamada

Many works show that node-level predictions of Graph Neural Networks (GNNs) are unrobust to small, often termed adversarial, changes to the graph structure. However, because manual inspection of a graph is difficult, it is unclear if the…

Machine Learning · Computer Science 2023-05-03 Lukas Gosch , Daniel Sturm , Simon Geisler , Stephan Günnemann

Let $\{D_M\}_{M\geq 0}$ be the $n$-vertex random directed graph process, where $D_0$ is the empty directed graph on $n$ vertices, and subsequent directed graphs in the sequence are obtained by the addition of a new directed edge uniformly…

Combinatorics · Mathematics 2020-11-18 Richard Montgomery

In a recent work of Brendle-Hirsch-Johne, a notion of intermediate curvature was introduced to extend the classical non-existence theorem of positive scalar curvature on torus to product manifolds. In this work, we study the rigidity when…

Differential Geometry · Mathematics 2022-08-26 Jianchun Chu , Kwok-Kun Kwong , Man-Chun Lee

Neural diffusion on graphs is a novel class of graph neural networks that has attracted increasing attention recently. The capability of graph neural partial differential equations (PDEs) in addressing common hurdles of graph neural…

Machine Learning · Computer Science 2023-05-12 Yang Song , Qiyu Kang , Sijie Wang , Zhao Kai , Wee Peng Tay

In this paper, we consider Wang's $CD_p(m,K)$ condition on graphs, which depends on the $p$-Laplacian $\Delta_p$ for $p>1$ and is an extension of the classical Bakry-\'Emery $CD(m,K)$ curvature dimension condition. We calculate several…

Combinatorics · Mathematics 2026-01-23 Chunyang Hu

We introduce and study a generalization of conformal rigidity for graphs. A graph is $k$-conformally rigid if the uniform edge weights simultaneously maximize the sum of the $k$ smallest nontrivial Laplacian eigenvalues and minimize the sum…

Combinatorics · Mathematics 2026-05-12 Henrique Assumpção , Gabriel Coutinho , Chris Godsil

The importance of studying properties of networks is manifest in diverse fields ranging from biology, engineering, physics, chemistry, neuroscience, and medicine. The functionality of networks with regard to performance, throughput,…

Molecular Networks · Quantitative Biology 2015-03-27 Allen Tannenbaum , Chris Sander , Liangjia Zhu , Romeil Sandhu , Ivan Kolesov , Eduard Reznik , Yasin Senbabaoglu , Tryphon Georgiou

Given a finite, simple, connected graph $G=(V,E)$ with $|V|=n$, we consider the associated graph Laplacian matrix $L = D - A$ with eigenvalues $0 = \lambda_1 < \lambda_2 \leq \dots \leq \lambda_n$. One can also consider the same graph…

Combinatorics · Mathematics 2025-04-08 Stefan Steinerberger , Rekha R. Thomas

Discrete curvatures are quantities associated to the nodes and edges of a graph that reflect the local geometry around them. These curvatures have a rich mathematical theory and they have recently found success as a tool to analyze networks…

Physics and Society · Physics 2024-08-02 Michelle Roost , Karel Devriendt , Giulio Zucal , Jürgen Jost

The preference of thin flat sheets to bend rather than stretch, combined with results from Geometry, mean that changes in a thin sheet's Gaussian curvature are prohibitively expensive. As a result, an imposed curvature in one principal…

Soft Condensed Matter · Physics 2019-08-19 Matteo Taffetani , Finn Box , Arthur Neveu , Dominic Vella

Graph embedding methods embed the nodes in a graph in low dimensional vector space while preserving graph topology to carry out the downstream tasks such as link prediction, node recommendation and clustering. These tasks depend on a…

Machine Learning · Computer Science 2020-10-22 Ramanujam Madhavan , Mohit Wadhwa

This work explores the definiteness of the weighted graph Laplacian matrix with negative edge weights. The definiteness of the weighted Laplacian is studied in terms of certain matrices that are related via congruent and similarity…

Optimization and Control · Mathematics 2015-03-03 Daniel Zelazo , Mathias Bürger

Neural Ordinary Differential Equations (NODEs) probed the usage of numerical solvers to solve the differential equation characterized by a Neural Network (NN), therefore initiating a new paradigm of deep learning models with infinite depth.…

Machine Learning · Computer Science 2023-05-17 Vishal Purohit

The main goal of this article is to understand the trace properties of nonlocal minimal graphs in~$\R^3$, i.e. nonlocal minimal surfaces with a graphical structure. We establish that at any boundary points at which the trace from inside…

Analysis of PDEs · Mathematics 2019-07-03 Serena Dipierro , Ovidiu Savin , Enrico Valdinoci

In this paper, we study the large-scale structure of dense regular graphs. This involves the notion of robust expansion, a recent concept which has already been used successfully to settle several longstanding problems. Roughly speaking, a…

Combinatorics · Mathematics 2017-05-17 Daniela Kühn , Allan Lo , Deryk Osthus , Katherine Staden

In this paper arithmetic progressions on the integers and the integers modulo n are extended to graphs. This allows for the definition of the anti-van der Waerden number of a graph. Much of the focus of this paper is on 3-term arithmetic…

The \emph{resistance matrix} of a simple connected graph $G$ is denoted by $R$, and is defined by $R =(r_{ij})$, where $r_{ij}$ is the resistance distance between the vertices $i$ and $j$ of $G$. In this paper, we consider the resistance…

Combinatorics · Mathematics 2018-04-05 Fouzul Atik , Ravindra B Bapat , M. Rajesh Kannan