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We study a new algorithmic process of graph growth which starts from a single initial vertex and operates in discrete time-steps, called \emph{slots}. In every slot, the graph grows via two operations (i) vertex generation and (ii) edge…

Data Structures and Algorithms · Computer Science 2022-12-20 George B. Mertzios , Othon Michail , George Skretas , Paul G. Spirakis , Michail Theofilatos

The number of spanning trees in the giant component of the random graph $\G(n, c/n)$ ($c>1$) grows like $\exp\big\{m\big(f(c)+o(1)\big)\big\}$ as $n\to\infty$, where $m$ is the number of vertices in the giant component. The function $f$ is…

Probability · Mathematics 2010-04-27 Russell Lyons , Ron Peled , Oded Schramm

In this note, we analyze two random greedy processes on sparse random graphs and hypergraphs with a given degree sequence. First we analyze the matching process, which builds a set of disjoint edges one edge at a time; then we analyze the…

Combinatorics · Mathematics 2021-09-24 Deepak Bal , Patrick Bennett

We consider a random geometric graph $G(\chi_n, r_n)$, given by connecting two vertices of a Poisson point process $\chi_n$ of intensity $n$ on the unit torus whenever their distance is smaller than the parameter $r_n$. The model is…

Probability · Mathematics 2019-07-04 Sourav Chatterjee , Matan Harel

We study the $k$-core of a random (multi)graph on $n$ vertices with a given degree sequence. In our previous paper [Random Structures Algorithms 30 (2007) 50--62] we used properties of empirical distributions of independent random variables…

Probability · Mathematics 2009-09-29 Svante Janson , Malwina J. Luczak

Local convergence techniques have become a key methodology to study sparse random graphs. However, convergence of many random graph properties does not directly follow from local convergence. A notable, and important, such random graph…

Probability · Mathematics 2025-10-07 Remco van der Hofstad

In a coalescing random walk, a set of particles make independent random walks on a graph. Whenever one or more particles meet at a vertex, they unite to form a single particle, which then continues the random walk through the graph.…

Data Structures and Algorithms · Computer Science 2016-12-28 Colin Cooper , Robert Elsasser , Hirotaka Ono , Tomasz Radzik

We determine the size of $k$-core in a large class of dense graph sequences. Let $G_n$ be a sequence of undirected, $n$-vertex graphs with edge weights $\{a^n_{i,j}\}_{i,j \in [n]}$ that converges to a kernel $W:[0,1]^2\to [0,+\infty)$ in…

Probability · Mathematics 2022-05-11 Erhan Bayraktar , Suman Chakraborty , Xin Zhang

In this work we give precise asymptotic expressions on the probability of the existence of fixed-size components at the threshold of connectivity for random geometric graphs.

Discrete Mathematics · Computer Science 2008-07-23 J. Diaz , D. Mitsche , X. Perez

We study the probabilistic properties of the Greatest Increase Grid (GIG) digraph. We compute the probability of a particular sequence of directed edges connecting two random vertices. We compute the joint probability that a set of vertices…

Combinatorics · Mathematics 2019-11-21 Chuhan Guo , Laurie J. Heyer , Jeffrey L. Poet

Given a Poisson process on a bounded interval, its random geometric graph is the graph whose vertices are the points of the Poisson process and edges exist between two points if and only if their distance is less than a fixed given…

Probability · Mathematics 2010-08-31 Laurent Decreusefond , Eduardo Ferraz

We study the "rank 1 case" of the inhomogeneous random graph model. In the subcritical case we derive an exact formula for the asymptotic size of the largest connected component scaled to log n. This result is new, it completes the…

Probability · Mathematics 2007-06-15 T. S. Turova

We study the phase transition of the random degree constrained process (RDCP), a time-evolving random graph model introduced by Ruci\'nski and Wormald that generalizes the random $d$-process to the non-regular setting: each vertex of the…

Probability · Mathematics 2026-01-16 Balázs Ráth , Márton Szőke

This Letter introduces a generalization of known duplication-divergence models for growing random graphs. This general duplication-divergence model includes a new coupled divergence asymmetry rate, which allows to obtain the structure of…

Statistical Mechanics · Physics 2024-12-04 Dario Borrelli

Given b>0, integers n, m=bn and a probability measure Q on {0, 1,..., m}, consider the random intersection graph on the vertex set [n]={1, ..., n}, where i and j are declared adjacent whenever S(i) and S(j) intersect. Here S(1), ..., S(n)…

Probability · Mathematics 2010-02-26 Mindaugas Bloznelis

We consider bootstrap percolation on uncorrelated complex networks. We obtain the phase diagram for this process with respect to two parameters: $f$, the fraction of vertices initially activated, and $p$, the fraction of undamaged vertices…

Statistical Mechanics · Physics 2015-03-13 G J Baxter , S N Dorogovtsev , A V Goltsev , J F F Mendes

For a finite graph $G=(V,E)$ let $G^*$ be obtained by considering a random perfect matching of $V$ and adding the corresponding edges to $G$ with weight $\varepsilon$, while assigning weight 1 to the original edges of $G$. We consider…

Probability · Mathematics 2023-10-17 Zsuzsanna Baran , Jonathan Hermon , Anđela Šarković , Perla Sousi

In the binomial random graph $\mathcal{G}(n,p)$, when $p$ changes from $(1-\varepsilon)/n$ (subcritical case) to $1/n$ and then to $(1+\varepsilon)/n$ (supercritical case) for $\varepsilon>0$, with high probability the order of the largest…

Combinatorics · Mathematics 2018-10-19 Oliver Cooley , Wenjie Fang , Nicola Del Giudice , Mihyun Kang

Inspired by a concept in comparative genomics, we investigate properties of randomly chosen members of G_1(m,n,t), the set of bipartite graphs with $m$ left vertices, n right vertices, t edges, and each vertex of degree at least one. We…

Probability · Mathematics 2007-05-23 Jonah Blasiak , Rick Durrett

We consider two classes of random graphs: $(a)$ Poissonian random graphs in which the $n$ vertices in the graph have i.i.d.\ weights distributed as $X$, where $\mathbb{E}(X) = \mu$. Edges are added according to a product measure and the…

Probability · Mathematics 2010-10-05 Tom Britton , Pieter Trapman