English
Related papers

Related papers: Moderate deviations in random graphs and Bernoulli…

200 papers

In this work, we study some statistical properties of the extreme eigenstates of the randomly-weighted adjacency matrices of random graphs. We focus on two random graph models: Erd\H{o}s-R\'{e}nyi (ER) graphs and random geometric graphs…

Disordered Systems and Neural Networks · Physics 2025-06-17 C. T Martínez Martínez , J. A. Méndez Bermúdez

Correcting for skewness can result in more accurate tail probability approximations in the central limit theorem for sums of independent random variables. In this paper, we extend the theory to sums of local statistics of independent random…

Probability · Mathematics 2019-04-05 Xiao Fang , Li Luo , Qi-Man Shao

We consider the upper tail large deviations of subgraph counts for irregular graphs $\mathrm{H}$ in $\mathbb{G}(n,p)$, the sparse Erd\H{o}s-R\'enyi graph on $n$ vertices with edge connectivity probability $p \in (0,1)$. For $n^{-1/\Delta}…

Probability · Mathematics 2025-04-10 Anirban Basak , Shaibal Karmakar

Suppose you can color $n$ \emph{biased} coins with $n$ colors, all coins having the same bias. It is forbidden to color both sides of a coin with the same color, but all other colors are allowed. Let $X$ be the number of different colors…

Probability · Mathematics 2013-12-24 Christos Pelekis

The pseudo-marginal algorithm is a variant of the Metropolis--Hastings algorithm which samples asymptotically from a probability distribution when it is only possible to estimate unbiasedly an unnormalized version of its density.…

Computation · Statistics 2019-12-04 Sebastian M. Schmon , George Deligiannidis , Arnaud Doucet , Michael K. Pitt

A wide variety of complex networks (social, biological, information etc.) exhibit local clustering with substantial variation in the clustering coefficient (the probability of neighbors being connected). Existing models of large graphs…

Discrete Mathematics · Computer Science 2017-09-28 Samantha Petti , Santosh Vempala

We consider a multitype epidemic model which is a natural extension of the randomized Reed--Frost epidemic model. The main result is the derivation of an asymptotic Gaussian limit theorem for the final size of the epidemic. The method of…

Probability · Mathematics 2007-05-23 Peter Neal

We study variational principles for metric mean dimension. First we prove that in the variational principle of Lindenstrauss and Tsukamoto it suffices to take supremum over ergodic measures. Second we derive a variational principle for…

Dynamical Systems · Mathematics 2022-02-04 Yonatan Gutman , Adam Śpiewak

We obtain bounds to quantify the distributional approximation in the delta method for vector statistics (the sample mean of $n$ independent random vectors) for normal and non-normal limits, measured using smooth test functions. For normal…

Statistics Theory · Mathematics 2023-05-11 Robert E. Gaunt , Heather Sutcliffe

A great deal of effort has been spent measuring topological features of the Internet. However, it was recently argued that sampling based on taking paths or traceroutes through the network from a small number of sources introduces a…

Disordered Systems and Neural Networks · Physics 2008-04-12 Aaron Clauset , Cristopher Moore

Given a super-critical branching random walk on $\mathbb R$ started from the origin, let $M_n$ be the maximal position of individuals at the $n$-th generation. Under some mild conditions, it is known from \cite{A13} that as…

Probability · Mathematics 2018-07-24 Xinxin Chen , Hui He

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

Despite the recently exhibited importance of higher-order interactions for various processes, few flexible (null) models are available. In particular, most studies on hypergraphs focus on a small set of theoretical models. Here, we…

Statistical Mechanics · Physics 2022-12-28 Marc Barthelemy

The maximum entropy principle from statistical mechanics states that a closed system attains an equilibrium distribution that maximizes its entropy. We first show that for graphs with fixed number of edges one can define a stochastic edge…

Disordered Systems and Neural Networks · Physics 2007-05-23 Jesse S. A. Bridgewater , P. Oscar Boykin , Vwani P. Roychowdhury

Given a finite typed rooted tree $T$ with $n$ vertices, the {\em empirical subtree measure} is the uniform measure on the $n$ typed subtrees of $T$ formed by taking all descendants of a single vertex. We prove a large deviation principle in…

Probability · Mathematics 2007-05-23 Amir Dembo , Peter Morters , Scott Sheffield

We consider the Norros-Reittu random graph $NR_n(\textbf{w})$, where edges are present independently but edge probabilities are moderated by vertex weights, and use probabilistic arguments based on martingales to analyse the component sizes…

Probability · Mathematics 2023-08-02 Umberto De Ambroggio , Angelica Pachon

We derive a simple formula characterizing the distribution of the size of the connected component of a fixed vertex in the Erd\H{o}s-R\'enyi random graph which allows us to give elementary proofs of some results of Federico, van der…

Probability · Mathematics 2018-04-27 Balazs Rath

The Graph Reconstruction Conjecture famously posits that any undirected graph on at least three vertices is determined up to isomorphism by its family of (unlabeled) induced subgraphs. At present, the conjecture admits partial resolutions…

Discrete Mathematics · Computer Science 2025-12-03 Julian Asilis , Xi Chen , Dutch Hansen , Shang-Hua Teng

The topology of the Internet has typically been measured by sampling traceroutes, which are roughly shortest paths from sources to destinations. The resulting measurements have been used to infer that the Internet's degree distribution is…

Disordered Systems and Neural Networks · Physics 2013-05-29 Aaron Clauset , Cristopher Moore

This paper studies parametric bootstrap methods for network data, with the goal of quantifying the uncertainty of network statistics of interest. While existing network resampling methods primarily focus on count statistics under…

Methodology · Statistics 2026-05-29 Zhixuan Shao , Can M. Le