Sharpness in the k-nearest neighbours random geometric graph model

TL;DR

This paper proves a conjecture about the sharpness of the connectivity threshold in k-nearest neighbors random geometric graphs, showing that increasing k by a constant can significantly boost connectivity probability.

Contribution

It confirms the conjecture that a small increase in k can sharply improve connectivity probability in the model, and establishes a new threshold for s-connectedness.

Findings

Proved the sharpness conjecture for connectivity thresholds.

Established a relation between k and s-connectedness probabilities.

Provided bounds for the increase in k needed for higher connectivity.

Abstract

Let $S_{n,k}$ denote the random geometric graph obtained by placing points in a square box of area $n$ according to a Poisson process of intensity 1 and joining each point to its $k$ nearest neighbours. Balister, Bollob\'as, Sarkar and Walters conjectured that for every $0< \epsilon <1$ and all $n$ sufficiently large there exists $C=C(\epsilon)$ such that whenever the probability $S_{n,k}$ is connected is at least $\epsilon $ then the probability $S_{n,k+C}$ is connected is at least $1-\epsilon $. In this paper we prove this conjecture. As a corollary we prove that there is a constant $C'$ such that whenever $k=k(n)$ is a sequence of integers such that the probability $S_{n,k(n)}$ is connected tends to one as $n$ tends to infinity, then for any $s(n)$ with $s(n)=o(\log n)$, the probability that $S_{n,k(n)+C's\log \log n}$ is $s$-connected tends to one This proves another conjecture of Balister, Bollob\'as, Sarkar and Walters.