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Related papers: Preferential Attachment Trees with Vertex Death: P…

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We consider an evolving random discrete tree model called Preferential Attachment with Vertex Death, as introduced by Deijfen. Initialised with an alive root labelled $1$, at each step $n\geq1$ either a new vertex with label $n+1$ is…

Probability · Mathematics 2025-03-12 Markus Heydenreich , Bas Lodewijks

A vertex of a randomly growing graph is called a persistent hub if at all but finitely many moments of time it has the maximal degree in the graph. We establish the existence of a persistent hub in the Barab\'asi--Albert random graph model…

Probability · Mathematics 2016-12-30 Pavel Galashin

We consider models of evolving networks $\{\mathcal{G}_n:n\geq 0\}$ modulated by two parameters: an attachment function $f:\mathbb{N}_0\to\mathbb{R}_+$ and a (possibly random) attachment sequence $\{m_i:i\geq 1\}$. Starting with a single…

Probability · Mathematics 2021-05-17 Sayan Banerjee , Shankar Bhamidi

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 prove almost sure convergence of the maximum degree in an evolving tree model combining local choice and preferential attachment. At each step in the growth of the graph, a new vertex is introduced. A fixed, finite number of possible…

Probability · Mathematics 2014-03-19 Yury Malyshkin , Elliot Paquette

A dynamic model for a random network evolving in continuous time is defined where new vertices are born and existing vertices may die. The fitness of a vertex is defined as the accumulated in-degree of the vertex and a new vertex is…

Probability · Mathematics 2015-09-24 Maria Deijfen

Consider a generalised preferential attachment tree with attachment function $f$, that is a random tree, where at each time-step a node connects to an existing node $v$ with probability proportional to $f(\mathrm{deg}(v))$, where…

Probability · Mathematics 2026-04-17 Tejas Iyer

Preferential attachment graphs are random graphs designed to mimic properties of typical real world networks. They are constructed by a random process that iteratively adds vertices and attaches them preferentially to vertices that already…

Discrete Mathematics · Computer Science 2018-03-30 Jan Dreier , Philipp Kuinke , Peter Rossmanith

A variation of the preferential attachment random graph model of Barab\'asi and Albert is defined that incorporates planted communities. The graph is built progressively, with new vertices attaching to the existing ones one-by-one. At every…

Machine Learning · Statistics 2018-01-30 Bruce Hajek , Suryanarayana Sankagiri

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

We study an asymptotical behavior of the maximal degree in the degree distribution in an evolving tree model combining the local choice and the Mori's preferential attachment. In the considered model, the random graph is constructed in the…

Probability · Mathematics 2017-10-27 Yury Malyshkin

We study preferential attachment mechanisms in random graphs that are parameterized by (i) a constant bias affecting the degree-biased distribution on the vertex set and (ii) the distribution of times at which new vertices are created by…

Probability · Mathematics 2017-10-09 Benjamin Bloem-Reddy , Peter Orbanz

A preferential attachment model for a growing network incorporating deletion of edges is studied and the expected asymptotic degree distribution is analyzed. At each time step $t=1,2,\ldots$, with probability $\pi_1>0$ a new vertex with one…

Physics and Society · Physics 2015-09-30 Maria Deijfen , Mathias Lindholm

We consider a preferential attachment model that incorporates an anomaly. Our goal is to understand the evolution of the network before and after the occurrence of the anomaly by studying the influence of the anomaly on the structural…

Physics and Society · Physics 2025-05-07 Qiu Liang , Remco van der Hofstad , Nelly Litvak

We define a dynamic model of random networks, where new vertices are connected to old ones with a probability proportional to a sublinear function of their degree. We first give a strong limit law for the empirical degree distribution, and…

Probability · Mathematics 2008-07-31 Steffen Dereich , Peter Morters

We use the framework of multivariate regular variation to analyse the extremal behaviour of preferential attachment models. To this end, we follow a directed linear preferential attachment model for a random, heavy-tailed number of steps in…

Probability · Mathematics 2024-08-06 Anja Janßen , Max Ziegenbalg

We prove almost sure convergence of the maximum degree in an evolving graph model combining a growing number of local choices with sublinear preferential attachment. At each step in the growth of the graph, a new vertex is introduced. Then…

Probability · Mathematics 2019-11-19 Yury Malyshkin

A multi-type preferential attachment tree is introduced, and studied using general multi-type branching processes. For the $p$-type case we derive a framework for studying the tree where a type $i$ vertex generates new type $j$ vertices…

Probability · Mathematics 2018-09-05 Sebastian Rosengren

In this paper, we study the existence of perfect matchings and Hamiltonian cycles in the preferential attachment model. In this model, vertices are added to the graph one by one, and each time a new vertex is created it establishes a…

Combinatorics · Mathematics 2016-10-26 Alan Frieze , Xavier Pérez-Giménez , Paweł Prałat , Benjamin Reiniger

We introduce a model of a preferential attachment based random graph which extends the family of models in which condensation phenomena can occur. Each vertex has an associated uniform random variable which we call its location. Our model…

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