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The \emph{distance-number} of a graph $G$ is the minimum number of distinct edge-lengths over all straight-line drawings of $G$ in the plane. This definition generalises many well-known concepts in combinatorial geometry. We consider the…

Combinatorics · Mathematics 2008-09-09 Paz Carmi , Vida Dujmović , Pat Morin , David R. Wood

We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph model. We adopt a graphon as our random graph model, which is…

Machine Learning · Statistics 2022-12-21 Madeline Navarro , Santiago Segarra

To each graph on $n$ vertices there is an associated subspace of the $n \times n$ matrices called the operator system of the graph. We prove that two graphs are isomorphic if and only if their corresponding operator systems are unitally…

Operator Algebras · Mathematics 2014-12-23 Carlos M. Ortiz , Vern I. Paulsen

We study the automorphism group of graphons (graph limits). We prove that after an appropriate "standardization" of the graphon, the automorphism group is compact. Furthermore, we characterize the orbits of the automorphism group on…

Combinatorics · Mathematics 2021-02-17 László Lovász , Balázs Szegedy

For discrete weighted graphs there is sufficient literature about the Cheeger cut and the Cheeger problem, but for metric graphs there are few results about these problems. Our aim is to study the Cheeger cut and the Cheeger problem in…

Analysis of PDEs · Mathematics 2022-03-16 José M. Mazón

By considering graphs as discrete analogues of Riemann surfaces, Baker and Norine (Adv. Math. 2007) developed a concept of linear systems of divisors for graphs. Building on this idea, a concept of gonality for graphs has been defined and…

Combinatorics · Mathematics 2016-07-12 Kevin Hendrey

Leighton's graph covering theorem states that a pair of finite graphs with isomorphic universal covers have a common finite cover. We provide a new proof of Leighton's theorem that allows generalizations; we prove the corresponding result…

Group Theory · Mathematics 2018-07-31 Daniel J. Woodhouse

We prove the cut-off phenomenon in total variation distance for the Brownian motions traced on the classical symmetric spaces of compact type, that is to say: (1) the classical simple compact Lie groups: special orthogonal groups, special…

Probability · Mathematics 2013-02-06 Pierre-Loïc Méliot

K\"orner introduced the notion of graph entropy in 1973 as the minimal code rate of a natural coding problem where not all pairs of letters can be distinguished in the alphabet. Later it turned out that it can be expressed as the solution…

Combinatorics · Mathematics 2023-08-29 Viktor Harangi , Xueyan Niu , Bo Bai

We consider the problem of graph matchability in non-identically distributed networks. In a general class of edge-independent networks, we demonstrate that graph matchability can be lost with high probability when matching the networks…

Statistics Theory · Mathematics 2019-03-22 Vince Lyzinski , Daniel L. Sussman

We prove that the sequence of cones of metric measure spaces converges if the sequence of base spaces converges in Gromov's box, concentration, and weak topologies. As an application, we show that the generalized Cauchy distribution with…

Metric Geometry · Mathematics 2024-02-23 Syota Esaki , Daisuke Kazukawa , Ayato Mitsuishi

Metric graphs are ubiquitous in science and engineering. For example, many data are drawn from hidden spaces that are graph-like, such as the cosmic web. A metric graph offers one of the simplest yet still meaningful ways to represent the…

Computational Geometry · Computer Science 2017-12-05 Tamal K. Dey , Dayu Shi , Yusu Wang

Subgraph densities have been defined, and served as basic tools, both in the case of graphons (limits of dense graph sequences) and graphings (limits of bounded-degree graph sequences). While limit objects have been described for the…

Combinatorics · Mathematics 2021-05-11 Dávid Kunszenti-Kovács , László Lovász , Balázs Szegedy

Let $G$ be a finite, simple connected graph. The average distance of a vertex $v$ of $G$ is the arithmetic mean of the distances from $v$ to all other vertices of $G$. The remoteness $\rho(G)$ of $G$ is the maximum of the average distances…

Combinatorics · Mathematics 2024-05-27 Peter Dankelmann , Sonwabile Mafunda , Sufiyan Mallu

Random planar graphs have been the subject of much recent work. Many basic properties of the standard uniform random planar graph P_{n}, by which we mean a graph chosen uniformly at random from the set of all planar graphs with vertex set…

Combinatorics · Mathematics 2013-07-23 Chris Dowden

We introduce new distance measures for comparing straight-line embedded graphs based on the Fr\'echet distance and the weak Fr\'echet distance. These graph distances are defined using continuous mappings and thus take the combinatorial…

Computational Geometry · Computer Science 2019-09-12 Hugo A. Akitaya , Maike Buchin , Bernhard Kilgus , Stef Sijben , Carola Wenk

Continuing the analysis in a unified scheme for treating generalized superselection sectors based upon the notion of selection criteria for states of relevance in quantum physics, we extend the Doplicher-Roberts superselection theory for…

Mathematical Physics · Physics 2007-05-23 I. Ojima

In the Matching Cut problem we ask whether a graph $G$ has a matching cut, that is, a matching which is also an edge cut of $G$. We consider the variants Perfect Matching Cut and Disconnected Perfect Matching where we ask whether there…

Combinatorics · Mathematics 2025-01-16 Felicia Lucke

Quasi-isometries are mappings on graphs, with distance-distortions parameterized by a multiplicative factor and an additive constant. The distance-distortions of quasi-isometries are in a general form that captures a wide range of…

Data Structures and Algorithms · Computer Science 2022-08-22 Khí-Uí Soo , Bakhadyr Khoussainov , Simone Linz

Graph neural networks (GNNs) rely on graph convolutions to extract local features from network data. These graph convolutions combine information from adjacent nodes using coefficients that are shared across all nodes. Since these…

Machine Learning · Computer Science 2020-10-22 Luana Ruiz , Luiz F. O. Chamon , Alejandro Ribeiro