Recurrence versus transience for weight-dependent random connection models

  • We investigate random graphs on the points of a Poisson process in d-dimensional space, which combine scale-free degree distributions and long-range effects. Every Poisson point carries an independent random mark and given marks and positions of the points we form an edge between two points independently with a probability depending via a kernel on the two marks and the distance of the points. Different kernels allow the mark to play different roles, like weight, radius or birth time of a vertex. The kernels depend on a parameter γ, which determines the power-law exponent of the degree distributions. A further independent parameter δ characterises the decay of the connection probabilities of vertices as their distance increases. We prove transience of the infinite cluster in the entire supercritical phase in regimes given by the parameters γ and δ, and complement these results by recurrence results if d=2. Our results are particularly interesting for the soft Boolean graph modelWe investigate random graphs on the points of a Poisson process in d-dimensional space, which combine scale-free degree distributions and long-range effects. Every Poisson point carries an independent random mark and given marks and positions of the points we form an edge between two points independently with a probability depending via a kernel on the two marks and the distance of the points. Different kernels allow the mark to play different roles, like weight, radius or birth time of a vertex. The kernels depend on a parameter γ, which determines the power-law exponent of the degree distributions. A further independent parameter δ characterises the decay of the connection probabilities of vertices as their distance increases. We prove transience of the infinite cluster in the entire supercritical phase in regimes given by the parameters γ and δ, and complement these results by recurrence results if d=2. Our results are particularly interesting for the soft Boolean graph model discussed in the preprint [arXiv:2108:11252] [Titel anhand dieser ArXiv-ID in Citavi-Projekt übernehmen] and the age-dependent random connection model recently introduced by Gracar et al. [Queueing Syst. 93.3-4 (2019)]show moreshow less

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Metadaten
Author:Peter Gracar, Markus HeydenreichGND, Christian Mönch, Peter Mörters
URN:urn:nbn:de:bvb:384-opus4-1037605
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/103760
ISSN:1083-6489OPAC
Parent Title (English):Electronic Journal of Probability
Publisher:Institute of Mathematical Statistics
Type:Article
Language:English
Year of first Publication:2022
Publishing Institution:Universität Augsburg
Release Date:2023/04/21
Tag:Statistics, Probability and Uncertainty; Statistics and Probability
Volume:27
First Page:1
Last Page:31
DOI:https://doi.org/10.1214/22-ejp748
Institutes:Mathematisch-Naturwissenschaftlich-Technische Fakultät
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Mathematik
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Mathematik / Lehrstuhl für Stochastik und ihre Anwendungen
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)