{"title":"具有边界的一维软随机几何图的最长边","authors":"Arnaud Rousselle, Ercan Sönmez","doi":"10.1080/15326349.2023.2256825","DOIUrl":null,"url":null,"abstract":"AbstractThe object of study is a soft random geometric graph with vertices given by a Poisson point process on a line and edges between vertices present with probability that has a polynomial decay in the distance between them. Various aspects of such models related to connectivity structures have been studied extensively. In this article, we study the random graph from the perspective of extreme value theory and focus on the occurrence of single long edges. The model we investigate has non-periodic boundary and is parameterized by a positive constant α, which is the power for the polynomial decay of the probabilities determining the presence of an edge. As a main result, we provide a precise description of the magnitude of the longest edge in terms of asymptotic behavior in distribution. Thereby we illustrate a crucial dependence on the power α and we recover a phase transition which coincides with exactly the same phases in Benjamini and Berger[ Citation2].Keywords: Extreme value theorymaximum edge-lengthPoisson approximationrandom graphssoft random geometric graphMSC: Primary: 05C8060G70Secondary: 60F0505C8282B21 Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe IMB receives support from the EIPHI Graduate School (contract ANR-17-EURE-0002).","PeriodicalId":21970,"journal":{"name":"Stochastic Models","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The longest edge of the one-dimensional soft random geometric graph with boundaries\",\"authors\":\"Arnaud Rousselle, Ercan Sönmez\",\"doi\":\"10.1080/15326349.2023.2256825\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AbstractThe object of study is a soft random geometric graph with vertices given by a Poisson point process on a line and edges between vertices present with probability that has a polynomial decay in the distance between them. Various aspects of such models related to connectivity structures have been studied extensively. In this article, we study the random graph from the perspective of extreme value theory and focus on the occurrence of single long edges. The model we investigate has non-periodic boundary and is parameterized by a positive constant α, which is the power for the polynomial decay of the probabilities determining the presence of an edge. As a main result, we provide a precise description of the magnitude of the longest edge in terms of asymptotic behavior in distribution. Thereby we illustrate a crucial dependence on the power α and we recover a phase transition which coincides with exactly the same phases in Benjamini and Berger[ Citation2].Keywords: Extreme value theorymaximum edge-lengthPoisson approximationrandom graphssoft random geometric graphMSC: Primary: 05C8060G70Secondary: 60F0505C8282B21 Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe IMB receives support from the EIPHI Graduate School (contract ANR-17-EURE-0002).\",\"PeriodicalId\":21970,\"journal\":{\"name\":\"Stochastic Models\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2023-09-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stochastic Models\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/15326349.2023.2256825\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stochastic Models","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/15326349.2023.2256825","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
The longest edge of the one-dimensional soft random geometric graph with boundaries
AbstractThe object of study is a soft random geometric graph with vertices given by a Poisson point process on a line and edges between vertices present with probability that has a polynomial decay in the distance between them. Various aspects of such models related to connectivity structures have been studied extensively. In this article, we study the random graph from the perspective of extreme value theory and focus on the occurrence of single long edges. The model we investigate has non-periodic boundary and is parameterized by a positive constant α, which is the power for the polynomial decay of the probabilities determining the presence of an edge. As a main result, we provide a precise description of the magnitude of the longest edge in terms of asymptotic behavior in distribution. Thereby we illustrate a crucial dependence on the power α and we recover a phase transition which coincides with exactly the same phases in Benjamini and Berger[ Citation2].Keywords: Extreme value theorymaximum edge-lengthPoisson approximationrandom graphssoft random geometric graphMSC: Primary: 05C8060G70Secondary: 60F0505C8282B21 Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe IMB receives support from the EIPHI Graduate School (contract ANR-17-EURE-0002).
期刊介绍:
Stochastic Models publishes papers discussing the theory and applications of probability as they arise in the modeling of phenomena in the natural sciences, social sciences and technology. It presents novel contributions to mathematical theory, using structural, analytical, algorithmic or experimental approaches. In an interdisciplinary context, it discusses practical applications of stochastic models to diverse areas such as biology, computer science, telecommunications modeling, inventories and dams, reliability, storage, queueing theory, mathematical finance and operations research.