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We propose a simple preferential attachment model of growing network using the complementary probability of Barab\'asi-Albert (BA) model, i.e., $\Pi(k_i) \propto 1-\frac{k_i}{\sum_j k_j}$. In this network, new nodes are preferentially…

Physics and Society · Physics 2016-01-20 A. Lachgar , A. Achahbar

If we pick $n$ random points uniformly in $[0,1]^d$ and connect each point to its $k-$nearest neighbors, then it is well known that there exists a giant connected component with high probability. We prove that in $[0,1]^d$ it suffices to…

Combinatorics · Mathematics 2017-11-15 George C. Linderman , Gal Mishne , Yuval Kluger , Stefan Steinerberger

Recently several authors have proposed stochastic evolutionary models for the growth of complex networks that give rise to power-law distributions. These models are based on the notion of preferential attachment leading to the ``rich get…

Physics and Society · Physics 2007-05-23 Trevor Fenner , Mark Levene , George Loizou

We propose a model that generates a new class of networks exhibiting power-law degree distribution with a spectrum of exponents depending on the number of links ($m$) with which incoming nodes join the existing network. Unlike the…

Physics and Society · Physics 2018-01-09 Kamrul Hassan , Liana Islam

We present analytical results for the distribution of shortest path lengths between random pairs of nodes in configuration model networks. The results, which are based on recursion equations, are shown to be in good agreement with numerical…

Disordered Systems and Neural Networks · Physics 2016-06-16 Mor Nitzan , Eytan Katzav , Reimer Kühn , Ofer Biham

Analytical results are presented for the structure of networks that evolve via a preferential-attachment-random-deletion (PARD) model in the regime of overall network growth and in the regime of overall contraction. The phase transition…

Statistical Mechanics · Physics 2025-01-13 Barak Budnick , Ofer Biham , Eytan Katzav

Bond percolation on infinite heavy-tailed power-law random networks lacks a proper phase transition; or one may say, there is a phase transition at {\em zero percolation probability}. Nevertheless, a finite size percolation threshold…

Disordered Systems and Neural Networks · Physics 2007-05-23 Nima Sarshar , Patrick Oscar Boykin , Vwani P. Roychowdhury

A family of models of growing hypergraphs with preferential rules of new linking is introduced and studied. The model hypergraphs evolve via the hyperedge-based growth as well as the node-based one, thus generalizing the…

Physics and Society · Physics 2023-09-04 Dahae Roh , Kwang-Il Goh

We describe the anomalous phase transition of the emergence of the giant connected component in scale-free networks growing under mechanism of preferential linking. We obtain exact results for the size of the giant connected component and…

Statistical Mechanics · Physics 2009-11-07 S. N. Dorogovtsev , J. F. F. Mendes , A. N. Samukhin

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 give an exact solution for the complete distribution of component sizes in random networks with arbitrary degree distributions. The solution tells us the probability that a randomly chosen node belongs to a component of size s, for any…

Statistical Mechanics · Physics 2007-10-18 M. E. J. Newman

Preferential attachment is a popular generative mechanism to explain the widespread observation of power law distributed networks. We introduce an alternative explanation for the phenomenon by allowing the link growth rates to vary across…

Physics and Society · Physics 2007-06-18 Birgitte Freiesleben de Blasio , Odd O Aalen

In this paper we provide explicit upper bounds on some distances between the (law of the) output of a random Gaussian NN and (the law of) a random Gaussian vector. Our results concern both shallow random Gaussian neural networks with…

We consider first passage times $\tau_u = \inf\{n:\; Y_n>u\}$ for the perpetuity sequence $$ Y_n = B_1 + A_1 B_2 + \cdots + (A_1\ldots A_{n-1})B_n, $$ where $(A_n,B_n)$ are i.i.d. random variables with values in ${\mathbb R} ^+\times…

Probability · Mathematics 2017-04-13 Dariusz Buraczewski , Ewa Damek , Jacek Zienkiewicz

The Norros-Reittu model is a random graph with $n$ vertices and i.i.d. weights assigned to them. The number of edges between any two vertices follows an independent Poisson distribution whose parameter is increasing in the weights of the…

Probability · Mathematics 2023-11-30 Matthias Lienau , Matthias Schulte

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

We consider random temporal graphs, a version of the classical Erd\H{o}s--R\'enyi random graph G(n,p) where additionally, each edge has a distinct random time stamp, and connectivity is constrained to sequences of edges with increasing time…

Probability · Mathematics 2023-06-21 Nicolas Broutin , Nina Kamčev , Gabor Lugosi

A class of cubic networks composed of a regular one-dimensional lattice and a set of long-range links is introduced. Networks parametrized by a positive integer k are constructed by starting from a one-dimensional lattice and iteratively…

Disordered Systems and Neural Networks · Physics 2009-11-13 Róbert Juhász

We study transport properties such as electrical and frictionless flow conductance on scale-free and Erdos-Renyi networks. We consider the conductance G between two arbitrarily chosen nodes where each link has the same unit resistance. Our…

Disordered Systems and Neural Networks · Physics 2016-08-16 Eduardo López , Shai Carmi , Shlomo Havlin , Sergey V. Buldyrev , H. Eugene Stanley

We introduce the study of \textit{randomly oriented divisor graphs}. For each $\rho \in [0,1]$, the randomly oriented divisor graph $\mathcal{D}_\rho(N)$ is obtained from the divisor graph on $\{1, 2, \ldots, N\}$ by directing each edge…

Combinatorics · Mathematics 2026-04-08 Jihyung Kim , Tristan Phillips