Concentration for Poisson U-Statistics: Subgraph Counts in Random Geometric Graphs

TL;DR

This paper establishes concentration inequalities for subgraph counts in random geometric graphs based on Poisson point processes, providing bounds with Gaussian decay for lower tails and optimal bounds for upper tails, applicable even without finite intensity measures.

Contribution

It introduces new concentration inequalities for Poisson U-statistics related to subgraph counts, extending applicability to processes with infinite intensity measures.

Findings

Gaussian decay for lower tail probabilities

Optimal bounds for upper tail probabilities

Applicable to Poisson processes with infinite intensity

Abstract

Concentration inequalities for subgraph counts in random geometric graphs built over Poisson point processes are proved. The estimates give upper bounds for the probabilities $\mathbb{P}(N\geq M +r)$ and $\mathbb{P}(N\leq M - r)$ where $M$ is either a median or the expectation of a subgraph count $N$. The bounds for the lower tail have a fast Gaussian decay and the bounds for the upper tail satisfy an optimality condition. A special feature of the presented inequalities is that the underlying Poisson process does not need to have finite intensity measure. The tail estimates for subgraph counts follow from concentration inequalities for more general local Poisson U-statistics. These bounds are proved using recent general concentration results for Poisson U-statistics and techniques based on the convex distance for Poisson point processes.