English

Random geometric graphs and the spherical Wishart matrix

Probability 2021-10-22 v1 Combinatorics

Abstract

We consider the random geometric graph on nn vertices drawn uniformly from a dd--dimensional sphere. We focus on the sparse regime, when the expected degree is constant independent of dd and nn. We show that, when dd is larger than nn by logarithmic factors, this graph is comparable to the Erd\H{o}s--R\'enyi random graph of the same edge density in the \emph{inclusion divergence} between the graph laws. This divergence functions in certain ways like a relaxation of the total variation distance, but is strong enough to distinguish Erd\H{o}s--R\'enyi graphs of different densities with a higher resolution than the total variation distance. To do the analysis, we derive some exact statistics of the \emph{spherical Wishart matrix}, the Gram matrix of nn independent uniformly random dd--dimensional spherical vectors. In particular we give expressions for the characteristic function of the spherical Wishart matrix which are well--approximated using steepest descent.

Keywords

Cite

@article{arxiv.2110.10785,
  title  = {Random geometric graphs and the spherical Wishart matrix},
  author = {Elliot Paquette and Andrew Vander Werf},
  journal= {arXiv preprint arXiv:2110.10785},
  year   = {2021}
}
R2 v1 2026-06-24T07:03:23.973Z