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A Burling graph is an induced subgraph of some graph in Burling's construction of triangle-free high-chromatic graphs. Equivalently, a Burling graph is a graph that admits a so-called strict frame representation. We provide a…

Combinatorics · Mathematics 2026-04-28 Paweł Rzążewski , Bartosz Walczak

In this paper, we study the problem of sampling random supersingular elliptic curves with unknown endomorphism rings. This problem has recently gained considerable attention as many isogeny-based cryptographic protocols require such…

Quantum Physics · Physics 2026-03-24 Maher Mamah , Jake Doliskani , David Jao

We show that if $p=O(1/n)$, then the Erd\H{o}s-R\'{e}nyi random graph $G(n,p)$ with high probability admits a canonical labeling computable in time $O(n\log n)$. Combined with the previous results on the canonization of random graphs, this…

Discrete Mathematics · Computer Science 2024-10-02 Oleg Verbitsky , Maksim Zhukovskii

We give an algorithm to morph planar graph drawings that achieves small grid size at the expense of allowing a constant number of bends on each edge. The input is an $n$-vertex planar graph and two planar straight-line drawings of the graph…

Computational Geometry · Computer Science 2024-09-09 Therese Biedl , Anna Lubiw , Jack Spalding-Jamieson

The correlated Erd\"os-R\'enyi random graph ensemble is a probability law on pairs of graphs with $n$ vertices, parametrized by their average degree $\lambda$ and their correlation coefficient $s$. It can be used as a benchmark for the…

Disordered Systems and Neural Networks · Physics 2024-11-21 Andrea Muratori , Guilhem Semerjian

We propose a new type of approximate counting algorithms for the problems of enumerating the number of independent sets and proper colorings in low degree graphs with large girth. Our algorithms are not based on a commonly used Markov chain…

Probability · Mathematics 2007-05-23 Antar Bandyopadhyay , David Gamarnik

Decomposable dependency models and their graphical counterparts, i.e., chordal graphs, possess a number of interesting and useful properties. On the basis of two characterizations of decomposable models in terms of independence…

Artificial Intelligence · Computer Science 2013-02-08 Luis M. de Campos , Juan F. Huete

We develop an experimental algorithm for the exact solving of the maximum independent set problem. The algorithm consecutively finds the maximal independent sets of vertices in an arbitrary undirected graph such that the next such set…

Data Structures and Algorithms · Computer Science 2016-03-02 Anatoly D. Plotnikov

We study the two inference problems of detecting and recovering an isolated community of \emph{general} structure planted in a random graph. The detection problem is formalized as a hypothesis testing problem, where under the null…

Data Structures and Algorithms · Computer Science 2022-01-25 Wasim Huleihel

In this work, Transition Probability Matrix (TPM) is proposed as a new method for extracting the features of nodes in the graph. The proposed method uses random walks to capture the connectivity structure of a node's close neighborhood. The…

Machine Learning · Computer Science 2023-03-07 Sarmad N. Mohammed , Semra Gündüç

Let P_{n,m} denote the graph taken uniformly at random from the set of all planar graphs on {1,2,..., n} with exactly m(n) edges. We use counting arguments to investigate the probability that P_{n,m} will contain given components and…

Combinatorics · Mathematics 2011-01-27 Chris Dowden

We give polynomial-time algorithms for obtaining hamilton circuits in random graphs, G, and random directed graphs, D. If n is finite, we assume that G or D contains a hamilton circuit. If G is an arbitrary graph containing a hamilton…

Combinatorics · Mathematics 2007-05-23 Howard Kleiman

We study how we can accelerate the spreading of information in temporal graphs via shifting operations; a problem that captures real-world applications varying from information flows to distribution schedules. In a temporal graph there is a…

Data Structures and Algorithms · Computer Science 2025-10-09 Argyrios Deligkas , Eduard Eiben , George Skretas

This paper investigates the active sampling for estimation of approximately bandlimited graph signals. With the assistance of a graph filter, an approximately bandlimited graph signal can be formulated by a Gaussian random field over the…

Signal Processing · Electrical Eng. & Systems 2019-02-19 Sijie Lin , Xuan Xie , Hui Feng , Bo Hu

We prove a pointwise ergodic theorem for quasi-probability-measure-preserving (quasi-pmp) locally countable measurable graphs, equivalently, Schreier graphs of quasi-pmp actions of countable groups. For ergodic graphs, the theorem gives an…

Dynamical Systems · Mathematics 2023-08-29 Anush Tserunyan

An efficient dominating set (or perfect code) in a graph is a set of vertices the closed neighborhoods of which partition the vertex set of the graph. The minimum weight efficient domination problem is the problem of finding an efficient…

Discrete Mathematics · Computer Science 2014-11-26 Andreas Brandstädt , Pavel Fičur , Arne Leitert , Martin Milanič

The positive zero forcing number of a graph is a graph parameter that arises from a non-traditional type of graph colouring, and is related to a more conventional version of zero forcing. We establish a relation between the zero forcing and…

Combinatorics · Mathematics 2014-07-28 Shaun Fallat , Karen Meagher , Boting Yang

Our work studies the enumeration and random generation of unlabeled combinatorial classes of unrooted graphs. While the technique of vertex pointing provides a straightforward procedure for analyzing a labeled class of unrooted graphs by…

Discrete Mathematics · Computer Science 2015-11-20 Alexander Iriza

We propose a novel random walk-based algorithm for unbiased estimation of arbitrary functions of a weighted adjacency matrix, coined universal graph random features (u-GRFs). This includes many of the most popular examples of kernels…

Machine Learning · Statistics 2024-05-27 Isaac Reid , Krzysztof Choromanski , Eli Berger , Adrian Weller

In a random intersection graph $G_{n,m,p}$, each of $n$ vertices selects a random subset of a set of $m$ labels by including each label independently with probability $p$ and edges are drawn between vertices that have at least one label in…

Discrete Mathematics · Computer Science 2022-10-06 Filippos Christodoulou , Sotiris Nikoletseas , Christoforos Raptopoulos , Paul Spirakis