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Related papers: Degree distributions in AB random geometric graphs

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We propose a distribution-free approach to the study of random geometric graphs. The distribution of vertices follows a Poisson point process with intensity function $nf(\cdot)$, where $n\in \mathbb{N}$, and $f$ is a probability density…

Probability · Mathematics 2012-10-22 Srikanth K. Iyer , Debleena Thacker

In random graph models, the degree distribution of an individual node should be distinguished from the (empirical) degree distribution of the graph that records the fractions of nodes with given degree. We introduce a general framework to…

Social and Information Networks · Computer Science 2018-11-14 Siddharth Pal , Armand M. Makowski

Recent work on the structure of social networks and the internet has focussed attention on graphs with distributions of vertex degree that are significantly different from the Poisson degree distributions that have been widely studied in…

Statistical Mechanics · Physics 2009-10-31 M. E. J. Newman , S. H. Strogatz , D. J. Watts

In the stochastic network model of Britton and Lindholm [Dynamic random networks in dynamic populations. Journal of Statistical Physics, 2010], the number of individuals evolves according to a supercritical linear birth and death process,…

Probability · Mathematics 2018-07-05 Fabian Kück , Dominic Schuhmacher

In complex networks the degrees of adjacent nodes may often appear dependent -- which presents a modelling challenge. We present a working framework for studying networks with an arbitrary joint distribution for the degrees of adjacent…

Combinatorics · Mathematics 2020-08-25 Samuel , G. Balogh , Gergely Palla , Ivan Kryven

We present analytical results for the structural evolution of random networks undergoing contraction processes via generic node deletion scenarios, namely, random deletion, preferential deletion and propagating deletion. Focusing on…

Physics and Society · Physics 2020-09-10 I. Tishby , O. Biham , E. Katzav

Degree distribution, or equivalently called degree sequence, has been commonly used to be one of most significant measures for studying a large number of complex networks with which some well-known results have been obtained. By contrast,…

Physics and Society · Physics 2020-02-19 Fei Ma , Xiaoming Wang , Ping Wang

Recent work on the internet, social networks, and the power grid has addressed the resilience of these networks to either random or targeted deletion of network nodes. Such deletions include, for example, the failure of internet routers or…

Statistical Mechanics · Physics 2009-10-31 D. S. Callaway , M. E. J. Newman , S. H. Strogatz , D. J. Watts

A common way of classifying network connectivity is the association of the nodal degree distribution to specific probability distribution models. During the last decades, researchers classified many networks using the Poisson or Pareto…

Social and Information Networks · Computer Science 2016-10-04 O. Giustolisi , A. Simone , L. Ridolfi

Despite great effort spent measuring topological features of large networks like the Internet, it was recently argued that sampling based on taking paths through the network (e.g., traceroutes) introduces a fundamental bias in the observed…

Disordered Systems and Neural Networks · Physics 2009-09-29 Aaron Clauset , Cristopher Moore

This letter characterizes the exact distribution function of a typical Voronoi area in a $K$-tier Poisson network. The users obey a generalized association (GA) rule, which is a superset of nearest base station association and maximum…

Information Theory · Computer Science 2020-07-02 Washim Uddin Mondal , Goutam Das

One of the most influential recent results in network analysis is that many natural networks exhibit a power-law or log-normal degree distribution. This has inspired numerous generative models that match this property. However, more recent…

Data Structures and Algorithms · Computer Science 2011-09-01 Isabelle Stanton , Ali Pinar

Random geometric graphs consist of randomly distributed nodes (points), with pairs of nodes within a given mutual distance linked. In the usual model the distribution of nodes is uniform on a square, and in the limit of infinitely many…

Disordered Systems and Neural Networks · Physics 2018-09-27 Carl P. Dettmann

Given two independent Poisson point processes $\Phi^{(1)},\Phi^{(2)}$ in $R^d$, the continuum AB percolation model is the graph with points of $\Phi^{(1)}$ as vertices and with edges between any pair of points for which the intersection of…

Probability · Mathematics 2010-12-20 Srikanth K. Iyer , D. Yogeshwaran

Complex systems, ranging from soft materials to wireless communication, are often organised as random geometric networks in which nodes and edges evenly fill up the volume of some space. Studying such networks is difficult because they…

Probability · Mathematics 2022-07-19 Ivan Kryven , Rik Versendaal

Hypergraphs, graph generalizations where edges are conglomerates of $r$ nodes called hyperedges of rank $r\geq 2$, are excellent models to study systems with interactions that are beyond the pairwise level. For hypergraphs, the node degree…

Statistical Mechanics · Physics 2013-07-11 Eduardo López

Let S_{n,k} denote the random geometric graph obtained by placing points inside a square of area n according to a Poisson point process of intensity 1 and joining each such point to the k=k(n) points of the process nearest to it. In this…

Probability · Mathematics 2013-09-18 Victor Falgas-Ravry

We study the joint degree counts in proportional attachment random graphs and find a simple representation for the limit distribution in infinite sequence space. We show weak convergence with respect to the p-norm topology for appropriate p…

Probability · Mathematics 2016-12-09 Erol A. Peköz , Adrian Röllin , Nathan Ross

Let $G=G_{n,k}$ denote the graph formed by placing points in a square of area $n$ according to a Poisson process of density 1 and joining each pair of points which are both $k$ nearest neighbours of each other. Then $G_{n,k}$ can be used as…

Combinatorics · Mathematics 2014-06-04 Neville Ball

A random geometric graph $G(\mathcal{X}_n, r_n)$ is formed by taking a binomial process $\mathcal{X}_n$ as the set of vertices and joining any two distinct points with an edge if they lie within distance $r_n$ of each other. We investigate…

Probability · Mathematics 2026-04-28 Junpei Otsuka
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