Gracar, Peter ORCID: 0000-0001-8340-8340, Heydenreich, Markus, Moench, Christian and Moerters, Peter (2022). Recurrence versus transience for weight-dependent random connection models. Electron. J. Probab., 27. CLEVELAND: INST MATHEMATICAL STATISTICS-IMS. ISSN 1083-6489

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Abstract

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-y, which determines the power-law exponent of the degree distributions. A further independent parameter ?? 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-y and ??, 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] and the age-dependent random connection model recently introduced by Gracar et al. [Queueing Syst. 93.3-4 (2019)]

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Gracar, PeterUNSPECIFIEDorcid.org/0000-0001-8340-8340UNSPECIFIED
Heydenreich, MarkusUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Moench, ChristianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Moerters, PeterUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-674647
DOI: 10.1214/22-EJP748
Journal or Publication Title: Electron. J. Probab.
Volume: 27
Date: 2022
Publisher: INST MATHEMATICAL STATISTICS-IMS
Place of Publication: CLEVELAND
ISSN: 1083-6489
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
PERCOLATION; UNIQUENESS; CLUSTERMultiple languages
Statistics & ProbabilityMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/67464

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