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We develop a quantitative large deviations theory for random Bernoulli tensors. The large deviation principles rest on a decomposition theorem for arbitrary tensors outside a set of tiny measure, in terms of a novel family of norms generalizing the cut norm. Combined with associated counting lemmas, these yield sharp asymptotics for upper tails of homomorphism counts in the $r$-uniform ErdH{o}s--Renyi hypergraph for any fixed $rge 2$, generalizing and improving on previous results for the ErdH{o}s--Renyi graph ($r=2$). The theory is sufficiently quantitative to allow the density of the hypergraph to vanish at a polynomial rate, and additionally yields (joint) upper and lower tail asymptotics for other nonlinear functionals of interest.
We consider a dynamic ErdH{o}s-Renyi random graph (ERRG) on $n$ vertices in which each edge switches on at rate $lambda$ and switches off at rate $mu$, independently of other edges. The focus is on the analysis of the evolution of the associated empi
We consider an inhomogeneous ErdH{o}s-Renyi random graph $G_N$ with vertex set $[N] = {1,dots,N}$ for which the pair of vertices $i,j in [N]$, $i eq j$, is connected by an edge with probability $r(tfrac{i}{N},tfrac{j}{N})$, independently of other pai
In this article, we develop a framework to study the large deviation principle for matrix models and their quantiz
A wide array of random graph models have been postulated to understand properties of observed networks. Typically these models have a parameter $t$ and a critical time $t_c$ when a giant component emerges. It is conjectured that for a large class of
Borgs, Chayes, Gaudio, Petti and Sen [arXiv:2007.14508] proved a large deviation principle for block model random graphs with rational block ratios. We strengthen their result by allowing any block ratios (and also establish a simpler formula for the