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Auteurs principaux: Díaz, Josep, Hartle, Harrison, Moore, Cristopher
Format: Preprint
Publié: 2025
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Accès en ligne:https://arxiv.org/abs/2504.21244
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author Díaz, Josep
Hartle, Harrison
Moore, Cristopher
author_facet Díaz, Josep
Hartle, Harrison
Moore, Cristopher
contents In 2013, Bollobás, Mitsche, and Pralat at gave upper and lower bounds for the likely metric dimension of random Erdős-Rényi graphs $G(n,p)$ for a large range of expected degrees $d=pn$. However, their results only apply when $d \ge \log^5 n$, leaving open sparser random graphs with $d < \log^5 n$. Here we provide upper and lower bounds on the likely metric dimension of $G(n,p)$ from just above the connectivity transition, i.e., where $d=pn=c \log n$ for some $c > 1$, up to $d=\log^5 n$. Our lower bound technique is based on an entropic argument which is more general than the use of Suen's inequality by Bollobás, Mitsche, and Pralat, whereas our upper bound is similar to theirs.
format Preprint
id arxiv_https___arxiv_org_abs_2504_21244
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle The Metric Dimension of Sparse Random Graphs
Díaz, Josep
Hartle, Harrison
Moore, Cristopher
Combinatorics
Data Structures and Algorithms
Social and Information Networks
Probability
In 2013, Bollobás, Mitsche, and Pralat at gave upper and lower bounds for the likely metric dimension of random Erdős-Rényi graphs $G(n,p)$ for a large range of expected degrees $d=pn$. However, their results only apply when $d \ge \log^5 n$, leaving open sparser random graphs with $d < \log^5 n$. Here we provide upper and lower bounds on the likely metric dimension of $G(n,p)$ from just above the connectivity transition, i.e., where $d=pn=c \log n$ for some $c > 1$, up to $d=\log^5 n$. Our lower bound technique is based on an entropic argument which is more general than the use of Suen's inequality by Bollobás, Mitsche, and Pralat, whereas our upper bound is similar to theirs.
title The Metric Dimension of Sparse Random Graphs
topic Combinatorics
Data Structures and Algorithms
Social and Information Networks
Probability
url https://arxiv.org/abs/2504.21244