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We deal with a random graph model evolving in discrete time steps by duplicating and deleting the edges of randomly chosen vertices. We prove the existence of an a.s. asymptotic degree distribution, with streched exponential decay; more…

Probability · Mathematics 2014-11-10 Ágnes Backhausz , Tamás F. Móri

We study the behavior of exponential random graphs in both the sparse and the dense regime. We show that exponential random graphs are approximate mixtures of graphs with independent edges whose probability matrices are critical points of…

Probability · Mathematics 2018-04-20 Ronen Eldan , Renan Gross

We introduce and develop a theory of limits for sequences of sparse graphs based on $L^p$ graphons, which generalizes both the existing $L^\infty$ theory of dense graph limits and its extension by Bollob\'as and Riordan to sparse graphs…

Combinatorics · Mathematics 2019-08-19 Christian Borgs , Jennifer T. Chayes , Henry Cohn , Yufei Zhao

Recently, Bollob\'as, Janson and Riordan introduced a very general family of random graph models, producing inhomogeneous random graphs with $\Theta(n)$ edges. Roughly speaking, there is one model for each {\em kernel}, i.e., each symmetric…

Combinatorics · Mathematics 2010-06-29 B. Bollobas , O. Riordan

We propose a novel statistical model for sparse networks with overlapping community structure. The model is based on representing the graph as an exchangeable point process, and naturally generalizes existing probabilistic models with…

Methodology · Statistics 2025-02-06 Adrien Todeschini , Xenia Miscouridou , François Caron

We conjecture that the distribution of the edge-disjoint union of two random regular graphs on the same vertex set is asymptotically equivalent to a random regular graph of the combined degree, provided it grows as the number of vertices…

Combinatorics · Mathematics 2024-07-29 Mikhail Isaev , Brendan D. McKay , Angus Southwell , Maksim Zhukovskii

We consider a variant of so called power-law random graph. A sequence of expected degrees corresponds to a power-law degree distribution with finite mean and infinite variance. In previous works the asymptotic picture with number of nodes…

Probability · Mathematics 2007-12-12 Hannu Reittu , Ilkka Norros

Network data appear in a number of applications, such as online social networks and biological networks, and there is growing interest in both developing models for networks as well as studying the properties of such data. Since individual…

Machine Learning · Statistics 2016-03-23 Diana Cai , Tamara Broderick

In this paper we consider the Erd\H{o}s-R\'enyi random graph in the sparse regime in the limit as the number of vertices $n$ tends to infinity. We are interested in what this graph looks like when it contains many triangles, in two…

Probability · Mathematics 2026-01-27 Suman Chakraborty , Remco van der Hofstad , Frank den Hollander

We consider the number of common edges in two independent random spanning trees of a graph $G$. For complete graphs $K_n$, we give a new proof of the fact, originally obtained by Moon, that the distribution converges to a Poisson…

Combinatorics · Mathematics 2025-06-09 Miklos Bona , Fabian Burghart , Stephan Wagner

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

In 1990 Bender, Canfield and McKay gave an asymptotic formula for the number of connected graphs on $[n]=\{1,2,\ldots,n\}$ with $m$ edges, whenever $n\to\infty$ and $n-1\le m=m(n)\le \binom{n}{2}$. We give an asymptotic formula for the…

Combinatorics · Mathematics 2018-11-05 Béla Bollobás , Oliver Riordan

We introduce a method for the theoretical analysis of exponential random graph models. The method is based on a large-deviations approximation to the normalizing constant shown to be consistent using theory developed by Chatterjee and…

Probability · Mathematics 2013-11-21 Sourav Chatterjee , Persi Diaconis

We propose the following model of a random graph on n vertices. Let F be a distribution in R_+^{n(n-1)/2} with a coordinate for every pair i$ with 1 \le i,j \le n. Then G_{F,p} is the distribution on graphs with n vertices obtained by…

Combinatorics · Mathematics 2011-08-09 Alan Frieze , Santosh Vempala , Juan Vera

This paper proves limit theorems for the number of monochromatic edges in uniform random colorings of general random graphs. These can be seen as generalizations of the birthday problem (what is the chance that there are two friends with…

Probability · Mathematics 2018-02-13 Bhaswar B. Bhattacharya , Persi Diaconis , Sumit Mukherjee

We propose a simple random process inducing various types of random graphs and the scale free random graphs among others. The model is of a threshold nature and differs from the preferential attachment approach discussed in the literature…

Disordered Systems and Neural Networks · Physics 2007-05-23 D. Volchenkov , Ph. Blanchard

The unconstrained exponential family of random graphs assumes no prior knowledge of the graph before sampling, but it is natural to consider situations where partial information about the graph is known, for example the total number of…

Probability · Mathematics 2017-04-19 Richard Kenyon , Mei Yin

In this paper we describe a triple correspondence between graph limits, information theory and group theory. We put forward a new graph limit concept called log-convergence that is closely connected to dense graph limits but its main…

Combinatorics · Mathematics 2015-04-06 Balazs Szegedy

This paper concerns the large deviations of a system of interacting particles on a random graph. There is no stochasticity, and the only sources of disorder are the random graph connections, and the initial condition. The average number of…

Probability · Mathematics 2021-03-08 James MacLaurin

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a…

Data Analysis, Statistics and Probability · Physics 2015-05-27 Bruce A. Desmarais , Skyler J. Cranmer