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We give asymptotics for the cumulative distribution function (CDF) for degrees of large dense random graphs sampled from a graphon. The proof is based on precise asymptotics for binomial random variables. Replacing the indicator function in…

Probability · Mathematics 2018-07-27 Jean-François Delmas , Jean-Stéphane Dhersin , Marion Sciauveau

We consider a sequence of processes defined on half-line for all non negative t. We give sufficient conditions for Large Deviation Principle (LDP) to hold in the space of continuous functions with a new metric that is more sensitive to…

Probability · Mathematics 2015-11-30 F. C. Klebaner , A. V. Logachov , A. A. Mogulski

In the sufficiently sparse case, we find the probability that a uniformly random bipartite graph with given degree sequence contains no edge from a specified set of edges. This enables us to enumerate loop-free digraphs and oriented graphs…

Combinatorics · Mathematics 2026-01-09 Catherine Greenhill , Mahdieh Hasheminezhad , Isaiah Iliffe , Brendan D. McKay

We initiate a study of large deviations for block model random graphs in the dense regime. Following Chatterjee-Varadhan(2011), we establish an LDP for dense block models, viewed as random graphons. As an application of our result, we study…

Probability · Mathematics 2025-09-17 Christian Borgs , Jennifer Chayes , Julia Gaudio , Samantha Petti , Subhabrata Sen

This paper studies the Laplacian spectrum and the average effective resistance of (large) graphs that are sampled from graphons. Broadly speaking, our main finding is that the Laplacian eigenvalues of a large dense graph can be effectively…

Probability · Mathematics 2020-12-03 Renato Vizuete , Federica Garin , Paolo Frasca

In this paper we derive a Large Deviation Principle (LDP) for inhomogeneous U/V-statistics of a general order. Using this, we derive a LDP for two types of statistics: random multilinear forms, and number of monochromatic copies of a…

Probability · Mathematics 2026-04-01 Sohom Bhattacharya , Nabarun Deb , Sumit Mukherjee

Establishing a Large Deviation Principle (LDP) proves to be a powerful result for a vast number of stochastic models in many application areas of probability theory. The key object of an LDP is the large deviations rate function, from which…

Probability · Mathematics 2017-06-23 Ken R. Duffy , Brendan D. Williamson

We consider the set of all graphs on n labeled vertices with prescribed degrees D=(d_1, ..., d_n). For a wide class of tame degree sequences D we prove a computationally efficient asymptotic formula approximating the number of graphs within…

Combinatorics · Mathematics 2011-12-05 Alexander Barvinok , J. A. Hartigan

We consider random graphs with a given degree sequence and show, under weak technical conditions, asymptotic normality of the number of components isomorphic to a given tree, first for the random multigraph given by the configuration model…

Probability · Mathematics 2019-02-01 Svante Janson

We consider uniform random cographs (either labeled or unlabeled) of large size. Our first main result is the convergence towards a Brownian limiting object in the space of graphons. We then show that the degree of a uniform random vertex…

This paper presents a linear prioritized local algorithm that computes large independent sets on a random $d$-regular graph with small and fixed degree $d$. We studied experimentally the independence ratio obtained by the algorithm when $ d…

Data Structures and Algorithms · Computer Science 2021-08-18 Raffaele Marino , Scott Kirkpatrick

We consider temporal models of rapidly changing Markovian networks modulated by time-evolving spatially dependent kernels that define rates for edge formation and dissolution. Alternatively, these can be viewed as Markovian networks with…

Probability · Mathematics 2025-06-11 Shankar Bhamidi , Amarjit Budhiraja , Souvik Ray

We give a Large Deviation Principle (LDP) with explicit rate function for the distribution of vertex degrees in plane trees, a combinatorial model of RNA secondary structures. We calculate the typical degree distributions based on nearest…

Biomolecules · Quantitative Biology 2008-03-28 Yuri Bakhtin , Christine E. Heitsch

In this paper, we prove the large deviation principle (LDP) for stochastic differential equations driven by stochastic integrals in one dimension. The result can be proved with a minimal use of rough path theory, and this implies the LDP…

Probability · Mathematics 2025-01-03 Ryoji Takano

In this article, we study random graphs with a given degree sequence $d_1, d_2, \cdots, d_n$ from the configuration model. We show that under mild assumptions of the degree sequence, the spectral distribution of the normalized Laplacian…

Probability · Mathematics 2024-12-04 Shuyi Wang , Kevin Li , Jiaoyang Huang

For any fixed simple graph $H=(V,E)$ and any fixed $u>0$, we establish the leading order of the exponential rate function for the probability that the number of copies of $H$ in the Erd\H{o}s--R\'enyi graph $G(n,p)$ exceeds its expectation…

Probability · Mathematics 2020-04-28 Nicholas A. Cook , Amir Dembo

We deal with a random graph model where at each step, a vertex is chosen uniformly at random, and it is either duplicated or its edges are deleted. Duplication has a given probability. We analyse the limit distribution of the degree of a…

Probability · Mathematics 2014-09-19 Ágnes Backhausz , Tamás F. Móri

The aim of this note is to announce some results about the probabilistic and deterministic asymptotic properties of linear groups. The first one is the analogue, for norms of random matrix products, of the classical theorem of Cramer on…

Probability · Mathematics 2017-02-23 Cagri Sert

Given a set D of nonnegative integers, we derive the asymptotic number of graphs with a givenvnumber of vertices, edges, and such that the degree of every vertex is in D. This generalizes existing results, such as the enumeration of graphs…

Combinatorics · Mathematics 2015-07-22 Élie de Panafieu , Lander Ramos

In this article, we prove a local large deviation principle (LLDP) for the empirical locality measure of typed random networks on $n$ nodes conditioned to have a given \emph{ empirical type measure} and \emph{ empirical link measure.} From…

Information Theory · Computer Science 2018-02-27 Kwabena Doku-Amponsah