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This work will appear as a chapter in a forthcoming volume titled "Topics in Probabilistic Graph Theory". A theory of scaling limits for random graphs has been developed in recent years. This theory gives access to the large-scale geometric…

Probability · Mathematics 2024-10-18 Louigi Addario-Berry , Christina Goldschmidt

In the classical preferential attachment model, links form instantly to newly arriving nodes and do not change over time. We propose a hierarchical random graph model in a spatial setting, where such a time-variability arises from an…

Probability · Mathematics 2019-04-04 Markus Heydenreich , Christian Hirsch

Consider the setting of \emph{randomly weighted graphs}, namely, graphs whose edge weights are chosen independently according to probability distributions with finite support over the non-negative reals. Under this setting, properties of…

Data Structures and Algorithms · Computer Science 2010-03-30 Yuval Emek , Amos Korman , Yuval Shavitt

We study the limiting behavior of interacting particle systems indexed by large sparse graphs, which evolve either according to a discrete time Markov chain or a diffusion, in which particles interact directly only with their nearest…

Probability · Mathematics 2022-05-18 Daniel Lacker , Kavita Ramanan , Ruoyu Wu

We present a new method for learning Soft Random Geometric Graphs (SRGGs), drawn in probabilistic metric spaces, with the connection function of the graph defined as the marginal posterior probability of an edge random variable, given the…

Methodology · Statistics 2020-02-05 Kangrui Wang , Dalia Chakrabarty

Consider a `dense' Erd\H{o}s--R\'enyi random graph model $G=G_{n,M}$ with $n$ vertices and $M$ edges, where we assume the edge density $M/\binom{n}{2}$ is bounded away from 0 and 1. Fix $k=k(n)$ with $k/n$ bounded away from 0 and~1, and let…

Combinatorics · Mathematics 2025-04-01 Paul Balister , Emil Powierski , Alex Scott , Jane Tan

We couple projective limits of probability measures to direct limits of their symmetry groups. We show that the direct limit group is the group of symmetries of the projective limit probability measure. If projective systems of probability…

Probability · Mathematics 2026-03-05 Pim van der Hoorn , Huck Stepanyants , Dmitri Krioukov

We study the effect of the choice of embedding geometry on the entropy of random geometric graph ensembles with soft connection functions. First we show that when the connection range is small, the entropy is dependent only on the dimension…

Probability · Mathematics 2026-01-22 Oliver Baker , Carl P. Dettmann

In this paper, we give the relationship between spectral radius and local structures of graphs and hypergraphs. Our work shows that certain local subgraphs (subhypergraphs) must occur when the spectral radius ratio is large. We also give…

Combinatorics · Mathematics 2025-12-02 Jiang Zhou , Changjiang Bu

A temporal random geometric graph is a random geometric graph in which all edges are endowed with a uniformly random time-stamp, representing the time of interaction between vertices. In such graphs, paths with increasing time stamps…

Probability · Mathematics 2025-02-24 Anna Brandenberger , Serte Donderwinkel , Céline Kerriou , Gábor Lugosi , Rivka Mitchell

We present a new notion of limits of weighted directed graphs of growing size based on convergence of their random quotients. These limits are specified in terms of random exchangeable measures on the unit square. We call our limits…

Combinatorics · Mathematics 2026-03-24 Eitan Levin , Venkat Chandrasekaran

Hybrid Gibbs samplers represent a prominent class of approximated Gibbs algorithms that utilize Markov chains to approximate conditional distributions, with the Metropolis-within-Gibbs algorithm standing out as a well-known example. Despite…

Statistics Theory · Mathematics 2025-03-24 Qian Qin , Nianqiao Ju , Guanyang Wang

Gibbs sampling on factor graphs is a widely used inference technique, which often produces good empirical results. Theoretical guarantees for its performance are weak: even for tree structured graphs, the mixing time of Gibbs may be…

Machine Learning · Computer Science 2015-10-06 Christopher De Sa , Ce Zhang , Kunle Olukotun , Christopher Ré

This study extends a prior investigation of limit shapes for partitions of integers, which was based on analysis of sums of geometric random variables. Here we compute limit shapes for grand canonical Gibbs ensembles of partitions of sets,…

Mathematical Physics · Physics 2021-05-12 Ibrahim Fatkullin , Jianfei Xue

We consider a countable system of interacting (possibly non-Markovian) stochastic differential equations driven by independent Brownian motions and indexed by the vertices of a locally finite graph $G = (V,E)$. The drift of the process at…

Probability · Mathematics 2020-09-28 Daniel Lacker , Kavita Ramanan , Ruoyu Wu

Exponential family Random Graph Models (ERGMs) can be viewed as expressing a probability distribution on graphs arising from the action of competing social forces that make ties more or less likely, depending on the state of the rest of the…

Discrete Mathematics · Computer Science 2019-08-27 Yue Yu , Gianmarc Grazioli , Nolan E. Phillips , Carter T. Butts

Local sets, a graph structure invariant under local complementation, have been originally introduced in the context of quantum computing for the study of quantum entanglement within the so-called graph state formalism. A local set in a…

Quantum Physics · Physics 2025-09-09 Nathan Claudet , Simon Perdrix

Let $G=(V,E)$ be a finite graph. For $v\in V$ we denote by $G_v$ the subgraph of $G$ that is induced by $v$'s neighbor set. We say that $G$ is $(a,b)$-regular for $a>b>0$ integers, if $G$ is $a$-regular and $G_v$ is $b$-regular for every…

Combinatorics · Mathematics 2019-08-29 Michael Chapman , Nati Linial , Yuval Peled

We present a method for balancing between the Local and Global Structures (LGS) in graph embedding, via a tunable parameter. Some embedding methods aim to capture global structures, while others attempt to preserve local neighborhoods. Few…

Human-Computer Interaction · Computer Science 2023-09-06 Jacob Miller , Vahan Huroyan , Stephen Kobourov

Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several settings. These architectures take advantage of a graph…

Machine Learning · Computer Science 2023-11-13 Andrea Cini , Ivan Marisca , Daniele Zambon , Cesare Alippi