Localization Game for Random Geometric Graphs

TL;DR

This paper investigates the localization game on random geometric graphs, determining the minimal number of vertices needed for cops to locate a robber, especially in graphs just above the connectivity threshold.

Contribution

The paper establishes bounds on the localization number for random geometric graphs near the connectivity threshold, advancing understanding of pursuit-evasion dynamics in geometric settings.

Findings

Localization number is determined up to poly-logarithmic factors.

Results apply to graphs slightly above the connectivity threshold.

Provides bounds relevant for pursuit-evasion strategies in geometric graphs.

Abstract

The localization game is a two player combinatorial game played on a graph $G=(V,E)$. The cops choose a set of vertices $S_1 \subseteq V$ with $|S_1|=k$. The robber then chooses a vertex $v \in V$ whose location is hidden from the cops, but the cops learn the graph distance between the current position of the robber and the vertices in $S_1$. If this information is sufficient to locate the robber, the cops win immediately; otherwise the cops choose another set of vertices $S_2 \subseteq V$ with $|S_2|=k$, and the robber may move to a neighbouring vertex. The new distances are presented to the robber, and if the cops can deduce the new location of the robber based on all information they accumulated thus far, then they win; otherwise, a new round begins. If the robber has a strategy to avoid being captured, then she wins. The localization number is defined to be the smallest integer $k$ so that the cops win the game. In this paper we determine the localization number (up to poly-logarithmic factors) of the random geometric graph $G \in \mathcal G(n,r)$ slightly above the connectivity threshold.