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This article discusses random hypergraphs with varying hyperedge sizes, admitting large hyperedges with size tending to infinity, and heavy-tailed limiting hyperedge size distributions. The main result describes a threshold for the random…

Combinatorics · Mathematics 2024-06-11 Elmer Bergman , Lasse Leskelä

We investigate a class of line ensembles whose local structure is described by independent geometric random walk bridges, which have been conditioned to interlace with each other. The latter arise naturally in the context Schur processes,…

Probability · Mathematics 2025-09-16 Evgeni Dimitrov

We introduce a natural generalization of the Erd\H{o}s-R\'enyi random graph model in which random instances of a fixed motif are added independently. The binomial random motif graph $G(H,n,p)$ is the random (multi)graph obtained by adding…

Combinatorics · Mathematics 2019-07-30 Michael Anastos , Peleg Michaeli , Samantha Petti

We study the limit theory of large threshold graphs and apply this to a variety of models for random threshold graphs. The results give a nice set of examples for the emerging theory of graph limits.

Combinatorics · Mathematics 2009-08-19 Persi Diaconis , Susan Holmes , Svante Janson

This work addresses a modification of the random geometric graph (RGG) model by considering a set of points uniformly and independently distributed on the surface of a $(d-1)$-sphere with radius $r$ in a $d-$dimensional Euclidean space,…

Physics and Society · Physics 2018-10-03 Alfonso Allen-Perkins

Kim defined a very general combinatorial abstraction of the diameter of polytopes called subset partition graphs to study how certain combinatorial properties of such graphs may be achieved in lower bound constructions. Using Lov\'asz'…

Combinatorics · Mathematics 2012-03-08 Nicolai Hähnle

Finding patterns in graphs is a fundamental problem in databases and data mining. In many applications, graphs are temporal and evolve over time, so we are interested in finding durable patterns, such as triangles and paths, which persist…

Databases · Computer Science 2024-03-26 Pankaj K. Agarwal , Xiao Hu , Stavros Sintos , Jun Yang

We study the mixing time of random graphs in the $d$-dimensional toric unit cube $[0,1]^d$ generated by the geographical threshold graph (GTG) model, a generalization of random geometric graphs (RGG). In a GTG, nodes are distributed in a…

Probability · Mathematics 2011-09-21 Andrew Beveridge , Milan Bradonjić

The fractional versions of graph theoretic-invariants multiply the range of applications in scheduling, assignment and operational research problems. In this paper, we introduce the fractional version of local metric dimension of graphs.…

Combinatorics · Mathematics 2018-10-09 Hira Benish , Muhammad Murtaza , Imran Javaid

We prove that a strictly stable minimal $C^2_h$ intrinsic graph G is locally area-minimizing, i.e. given any $C^1_h$ graph $S$ with the same boundary, $\text{Area}(G)<\text{Area}(S)$ unless $G=S$. As a consequence we show the existence and…

Differential Geometry · Mathematics 2017-01-24 Giovanna Citti , Matteo Galli

A recent trend in the context of graph theory is to bring theoretical analyses closer to empirical observations, by focusing the studies on random graph models that are used to represent practical instances. There, it was observed that…

Discrete Mathematics · Computer Science 2024-07-11 Tobias Friedrich , Andreas Göbel , Maximilian Katzmann , Leon Schiller

This is a short note on how a particular graph construction on a subset of edges that lead to a subalgebra construction, provided a tool in proving some ring theoretical properties of Leavitt path algebras.

Rings and Algebras · Mathematics 2018-08-20 Songül Esin

Subgraph counts - in particular the number of occurrences of small shapes such as triangles - characterize properties of random networks, and as a result have seen wide use as network summary statistics. However, subgraphs are typically…

Statistics Theory · Mathematics 2020-06-30 P-A. Maugis

This paper considers a non-standard problem of generating samples from a low-temperature Gibbs distribution with \emph{constrained} support, when some of the coordinates of the mode lie on the boundary. These coordinates are referred to as…

Statistics Theory · Mathematics 2026-02-27 Ruixiao Wang , Xiaohong Chen , Sinho Chewi

Dependency networks (Heckerman et al., 2000) provide a flexible framework for modeling complex systems with many variables by combining independently learned local conditional distributions through pseudo-Gibbs sampling. Despite their…

Machine Learning · Computer Science 2026-04-02 Kazuya Takabatake , Shotaro Akaho

We study the local evolution of Prim's algorithm on large finite weighted graphs. When performed for $n$ steps, where $n$ is the size of the graph, Prim's algorithm will construct the minimal spanning tree (MST). We assume that our graphs…

Probability · Mathematics 2025-07-08 Benoît Corsini , Rowel Gündlach , Remco van der Hofstad

We study random digraphs on sequences of expanders with bounded average degree {which converge locally in probability}. We prove that the threshold for the existence of a giant strongly connected component, as well as the asymptotic…

Probability · Mathematics 2022-09-01 Yeganeh Alimohammadi , Christian Borgs , Amin Saberi

This paper studies higher index theory for a random sequence of bounded degree, finite graphs with diameter tending to infinity. We show that in a natural model for such random sequences the following hold almost surely: the coarse…

K-Theory and Homology · Mathematics 2014-04-28 Rufus Willett

Complex systems, ranging from soft materials to wireless communication, are often organised as random geometric networks in which nodes and edges evenly fill up the volume of some space. Studying such networks is difficult because they…

Probability · Mathematics 2022-07-19 Ivan Kryven , Rik Versendaal

Graph kernels are widely used for measuring the similarity between graphs. Many existing graph kernels, which focus on local patterns within graphs rather than their global properties, suffer from significant structure information loss when…

Machine Learning · Computer Science 2019-12-02 Lingfei Wu , Ian En-Hsu Yen , Zhen Zhang , Kun Xu , Liang Zhao , Xi Peng , Yinglong Xia , Charu Aggarwal