Multiset Metric Dimension of Binomial Random Graphs

TL;DR

This paper investigates the multiset metric dimension of binomial random graphs, providing probabilistic bounds in the regime where the average degree scales as a power of the number of vertices.

Contribution

It establishes probabilistic bounds for the multiset metric dimension of G(n,p) in a specific degree regime, advancing understanding of this parameter in random graphs.

Findings

Bounds on multiset metric dimension with high probability

Results applicable for degree scaling as n^x for fixed x in (0,1)

Extension of previous studies to probabilistic settings

Abstract

For a graph $G = (V,E)$ and a subset $R \subseteq V$, we say that $R$ is \textit{multiset resolving} for $G$ if for every pair of vertices $v,w$, the \textit{multisets} $\{d(v,r): r \in R\}$ and $\{d(w,r):r \in R\}$ are distinct, where $d(x,y)$ is the graph distance between vertices $x$ and $y$. The \textit{multiset metric dimension} of $G$ is the size of a smallest set $R \subseteq V$ that is multiset resolving (or $\infty$ if no such set exists). This graph parameter was introduced by Simanjuntak, Siagian, and Vitr\'{i}k in 2017~\cite{simanjuntak2017multiset}, and has since been studied for a variety of graph families. We prove bounds which hold with high probability for the multiset metric dimension of the binomial random graph $G(n,p)$ in the regime $d = (n-1)p = \Theta(n^{x})$ for fixed $x \in (0,1)$.