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Related papers: The Interval function, Ptolemaic, distance heredit…

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We consider undirected simple finite graphs. The sets of vertices and edges of a graph $G$ are denoted by $V(G)$ and $E(G)$, respectively. For a graph $G$, we denote by $\delta(G)$ and $\eta(G)$ the least degree of a vertex of $G$ and the…

Combinatorics · Mathematics 2013-07-05 N. N. Davtyan , R. R. Kamalian

A graph G is {\xi}-nearly planar if it can be embedded in the sphere so that each of its edges is crossed at most {\xi} times. The family of {\xi}-nearly planar graphs is widely extending the notion of planarity. We introduce an alternative…

Combinatorics · Mathematics 2013-11-04 Alexander Grigoriev , Athanassios Koutsonas , Dimitrios M. Thilikos

A family of graphs $\mathcal{F}$ is hereditary if $\mathcal{F}$ is closed under isomorphism and taking induced subgraphs. The speed of $\mathcal{F}$ is the sequence $\{|\mathcal{F}^n|\}_{n \in \mathbb{N}}$, where $\mathcal{F}^n$ denotes the…

Combinatorics · Mathematics 2020-07-03 Sergey Norin , Yelena Yuditsky

In static graphs, the betweenness centrality of a graph vertex measures how many times this vertex is part of a shortest path between any two graph vertices. Betweenness centrality is efficiently computable and it is a fundamental tool in…

Data Structures and Algorithms · Computer Science 2021-05-28 Maciej Rymar , Hendrik Molter , André Nichterlein , Rolf Niedermeier

The GG-width of a class of graphs GG is defined as follows. A graph G has GG-width k if there are k independent sets N1,...,Nk in G such that G can be embedded into a graph H in GG such that for every edge e in H which is not an edge in G,…

Combinatorics · Mathematics 2012-11-01 M. Chang , L. Hung , T. Kloks , S. Peng

An \emph{interval $t$-coloring} of a graph $G$ is a proper edge-coloring with colors $1,\dots,t$ such that the colors on the edges incident to every vertex of $G$ are colored by consecutive colors. A graph $G$ is called \emph{interval…

Combinatorics · Mathematics 2024-09-27 Petros A. Petrosyan , Hrant H. Khachatrian , Hovhannes G. Tananyan

Let G be a vertex transitive graph. A study of the range of simple random walk on G and of its bridge is proposed. While it is expected that on a graph of polynomial growth the sizes of the range of the unrestricted random walk and of its…

Probability · Mathematics 2007-05-23 Itai Benjamini , Roey Izkovsky , Harry Kesten

The edit distance between two graphs on the same vertex set is defined to be size of the symmetric difference of their edge sets. The edit distance function of a hereditary property, $\mathcal{H}$, is a function of $p$ and measures,…

Combinatorics · Mathematics 2011-02-22 Ryan Martin , Tracy McKay

Temporal graphs are a class of graphs defined by a constant set of vertices and a changing set of edges, each of which is known as a timestep. These graphs are well motivated in modelling real-world networks, where connections may change…

Data Structures and Algorithms · Computer Science 2025-05-21 Duncan Adamson

The edge betweenness centrality of an edge is loosely defined as the fraction of shortest paths between all pairs of vertices passing through that edge. In this paper, we investigate graphs where the edge betweenness centrality of edges is…

Combinatorics · Mathematics 2017-09-15 Heather A. Newman , Hector Miranda , Rigoberto Florez , Darren A. Narayan

Graphs are nowadays ubiquitous in the fields of signal processing and machine learning. As a tool used to express relationships between objects, graphs can be deployed to various ends: I) clustering of vertices, II) semi-supervised…

Machine Learning · Computer Science 2020-07-17 Carlos Lassance , Vincent Gripon , Gonzalo Mateos

We prove a characterization of all polynomial-time computable queries on the class of interval graphs by sentences of fixed-point logic with counting. More precisely, it is shown that on the class of unordered interval graphs, any query is…

Logic in Computer Science · Computer Science 2011-01-14 Bastian Laubner

Graph Neural Network (GNN) is a powerful tool to perform standard machine learning on graphs. To have a Euclidean representation of every node in the Non-Euclidean graph-like data, GNN follows neighbourhood aggregation and combination of…

Machine Learning · Computer Science 2021-11-18 Sucheta Dawn , Sanghamitra Bandyopadhyay

We focus on strongly connected, strong for short, digraphs since in this setting distance is defined for every pair of vertices. Distance ideals generalize the spectrum and Smith normal form of several distance matrices associated with…

The $t$-e.c. and pseudo-random property are typical properties of random graphs. In this note, we study the gap between them which has not been studied well. As a main result, we give the first explicit construction of infinite families of…

Combinatorics · Mathematics 2019-07-23 Shohei Satake

These lecture notes are on automorphism groups of Cayley graphs and their applications to optimal fault-tolerance of some interconnection networks. We first give an introduction to automorphisms of graphs and an introduction to Cayley…

Combinatorics · Mathematics 2017-04-04 Ashwin Ganesan

Persistent homology, a technique from computational topology, has recently shown strong empirical performance in the context of graph classification. Being able to capture long range graph properties via higher-order topological features,…

Machine Learning · Computer Science 2024-12-20 Rubén Ballester , Bastian Rieck

For a given homogeneous Poisson point process in $\mathbb{R}^d$ two points are connected by an edge if their distance is bounded by a prescribed distance parameter. The behaviour of the resulting random graph, the Gilbert graph or random…

Probability · Mathematics 2017-11-06 Matthias Reitzner , Matthias Schulte , Christoph Thaele

For an undirected, simple, finite, connected graph $G$, we denote by $V(G)$ and $E(G)$ the sets of its vertices and edges, respectively. A function $\varphi:E(G)\rightarrow\{1,2,\ldots,t\}$ is called a proper edge $t$-coloring of a graph…

Combinatorics · Mathematics 2013-05-30 R. R. Kamalian

We introduce a taxonomy of interaction types and show that graphs are focal hypergraphs: every graph is canonically a focal hypergraph via its closed neighbourhood structure, and every graph dynamical model is a special case of the general…

Physics and Society · Physics 2026-03-05 Elkaïoum M. Moutuou
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