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Stochastic Processes and their Applications
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Limit theory of sparse random geometric graphs in high dimensions

Authors: Gilles Bonnet; Christian Hirsch; Daniel Rosen; Daniel Willhalm;

Limit theory of sparse random geometric graphs in high dimensions

Abstract

We study topological and geometric functionals of $l_\infty$-random geometric graphs on the high-dimensional torus in a sparse regime, where the expected number of neighbors decays exponentially in the dimension. More precisely, we establish moment asymptotics, functional central limit theorems and Poisson approximation theorems for certain functionals that are additive under disjoint unions of graphs. For instance, this includes simplex counts and Betti numbers of the Rips complex, as well as general subgraph counts of the random geometric graph. We also present multi-additive extensions that cover the case of persistent Betti numbers of the Rips complex.

34 pages

Keywords

Simplicial sets and complexes in algebraic topology, Functional limit theorems; invariance principles, functional central limit theorem, high dimension, Probability (math.PR), Random graphs (graph-theoretic aspects), Functional central limit theorem, random geometric graph, High dimension, 60D05 (Primary) 55U10, 60F05 (Secondary), FOS: Mathematics, Density (toughness, etc.), Betti numbers, Geometric probability and stochastic geometry, Poisson approximation, Random geometric graph, Mathematics - Probability

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
hybrid