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Hauptverfasser: Aamari, Eddie, Arias-Castro, Ery, Berenfeld, Clément
Format: Preprint
Veröffentlicht: 2021
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2105.03122
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author Aamari, Eddie
Arias-Castro, Ery
Berenfeld, Clément
author_facet Aamari, Eddie
Arias-Castro, Ery
Berenfeld, Clément
contents In network analysis, a measure of node centrality provides a scale indicating how central a node is within a network. The coreness is a popular notion of centrality that accounts for the maximal smallest degree of a subgraph containing a given node. In this paper, we study the coreness of random geometric graphs and show that, with an increasing number of nodes and properly chosen connectivity radius, the coreness converges to a new object, that we call the continuum coreness. In the process, we show that other popular notions of centrality measures, namely the H-index and its iterates, also converge under the same setting to new limiting objects.
format Preprint
id arxiv_https___arxiv_org_abs_2105_03122
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle The Coreness and H-Index of Random Geometric Graphs
Aamari, Eddie
Arias-Castro, Ery
Berenfeld, Clément
Statistics Theory
Probability
In network analysis, a measure of node centrality provides a scale indicating how central a node is within a network. The coreness is a popular notion of centrality that accounts for the maximal smallest degree of a subgraph containing a given node. In this paper, we study the coreness of random geometric graphs and show that, with an increasing number of nodes and properly chosen connectivity radius, the coreness converges to a new object, that we call the continuum coreness. In the process, we show that other popular notions of centrality measures, namely the H-index and its iterates, also converge under the same setting to new limiting objects.
title The Coreness and H-Index of Random Geometric Graphs
topic Statistics Theory
Probability
url https://arxiv.org/abs/2105.03122