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We propose a random graph model with preferential attachment rule and \emph{edge-step functions} that govern the growth rate of the vertex set. We study the effect of these functions on the empirical degree distribution of these random…

Probability · Mathematics 2019-01-09 Caio Alves , Rodrigo Ribeiro , Remy Sanchis

We extend the classical preferential attachment random graph model to random simplicial complexes. At each stage of the model, we choose one of the existing $k$-simplices with probability proportional to its $k$-degree. The chosen…

Probability · Mathematics 2024-10-24 Takashi Owada , Gennady Samorodnitsky

We show that the local weak limit of a sequence of finite expander graphs with uniformly bounded degree is an ergodic (or extremal) unimodular random graph. In particular, the critical probability of percolation of the limiting random graph…

Probability · Mathematics 2021-05-12 Sourav Sarkar

We use an inequality of Sidorenko to show a general relation between local and global subgraph counts and degree moments for locally weakly convergent sequences of sparse random graphs. This yields an optimal criterion to check when the…

Probability · Mathematics 2023-02-07 Valentas Kurauskas

In several scale free graph models the asymptotic degree distribution and the characteristic exponent change when only a smaller set of vertices is considered. Looking at the common properties of these models, we present sufficient…

Probability · Mathematics 2010-07-27 Agnes Backhausz , Tamas F. Mori

We find new upper bounds on the size of a minimum totally dominating set for random regular graphs and for regular graphs with large girth. These bounds are obtained through the analysis of a local algorithm using a method due to Hoppen and…

Combinatorics · Mathematics 2020-01-07 Carlos Hoppen , Giovane Mansan

In this work we prove general bounds for the diameter of random graphs generated by a preferential attachment model whose parameter is a function $f:\mathbb{N}\to[0,1]$ that drives the asymptotic proportion between the numbers of vertices…

Probability · Mathematics 2023-07-04 Caio Alves , Rodrigo Ribeiro , Remy Sanchis

In this work we consider a growing random graph sequence where a new vertex is less likely to join to an existing vertex with high degree and more likely to join to a vertex with low degree. In contrast to the well studied…

Probability · Mathematics 2025-08-27 Antar Bandyopadhyay , Subhabrata Sen

We introduce a general class of algorithms and supply a number of general results useful for analysing these algorithms when applied to regular graphs of large girth. As a result, we can transfer a number of results proved for random…

Combinatorics · Mathematics 2017-03-06 Carlos Hoppen , Nicholas Wormald

This paper provides time-dependent expressions for the expected degree distribution of a given network that is subject to growth, as a function of time. We consider both uniform attachment, where incoming nodes form links to existing nodes…

Statistical Mechanics · Physics 2013-12-16 Babak Fotouhi , Michael G. Rabbat

We find assimpotics for the first $k$ highest degrees of the degree distribution in an evolving tree model combining the local choice and the preferential attachment. In the considered model, the random graph is constructd in the following…

Probability · Mathematics 2016-08-31 Yury Malyshkin

The article deals with two classes of growing random graphs following the preferential attachment rule with a linear weight function, L-graphs, and hybrid Pennock graphs. We determine the exact final vertex degree distribution and the exact…

Probability · Mathematics 2020-09-08 V. N. Zadorozhnyi , E. B. Yudin

We study a generalization of the affine preferential attachment model where triangles are randomly added to the graph. We show that the model exhibits an asymptotically power-law degree distribution with adjustable parameter $\gamma\in…

Probability · Mathematics 2025-04-04 Angelica Pachon , Robin Stephenson

We consider the degree distributions of preferential attachment random graph models with choice similar to those considered in recent work by Malyshkin and Paquette and Krapivsky and Redner. In these models a new vertex chooses $r$ vertices…

Probability · Mathematics 2020-08-12 John Haslegrave , Jonathan Jordan

We derive distributional approximations for the number of triangles in the linear preferential attachment model $\mathrm{PAM}(m,\delta)$, where $m\ge 2$ and $\delta>-m$, with explicit rates of convergence. The limiting distribution…

Probability · Mathematics 2026-05-28 Partha S. Dey , Grigory Terlov

For a sequence of random graphs, the limit law we refer to is the existence of a limiting probability of any graph property that can be expressed in terms of predicate logic. A zero-one limit law is shown by Shelah and Spencer for…

Probability · Mathematics 2024-08-15 Alperen Özdemir

We study a random graph $G_n$, which combines aspects of geometric random graphs and preferential attachment. The resulting random graphs have power-law degree sequences with finite mean and possibly infinite variance. In particular, the…

Combinatorics · Mathematics 2008-01-11 H. van den Esker

Graph symmetries intervene in diverse applications, from enumeration, to graph structure compression, to the discovery of graph dynamics (e.g., node arrival order inference). Whereas Erd\H{o}s-R\'enyi graphs are typically asymmetric, real…

Probability · Mathematics 2018-12-27 Tomasz Luczak , Abram Magner , Wojciech Szpankowski

We study logical limit laws for preferential attachment random graphs. In this random graph model, vertices and edges are introduced recursively: at time $1$, we start with vertices $0,1$ and $m$ edges between them. At step $n+1$ the vertex…

Probability · Mathematics 2021-08-19 Yury Malyshkin

We consider preferential attachment random graphs which may be obtained as follows: It starts with a single node. If a new node appears, it is linked by an edge to one or more existing node(s) with a probability proportional to function of…

Probability · Mathematics 2015-01-29 K. Doku-Amponsah , F. O. Mettle , E. N. N. Nortey