A Uniform Sampling Procedure for Abstract Triangulations of Surfaces

TL;DR

This paper introduces a new uniform sampling method for balanced triangulations of surfaces, leveraging permutation connections, and provides extensive experimental analysis of the genus distribution and symmetries of sampled surfaces.

Contribution

The paper presents a novel permutation-based sampling procedure for graph-encoded surfaces and offers extensive empirical analysis of surface genus and symmetry properties.

Findings

Empirical mean genus closely follows a specific formula as n increases.

Genus distribution concentrates on a small subset of all possible genera for large n.

Average number of symmetries decreases super-exponentially with n.

Abstract

We present a procedure to sample uniformly from the set of combinatorial isomorphism types of balanced triangulations of surfaces - also known as graph-encoded surfaces. For a given number $n$, the sample is a weighted set of graph-encoded surfaces with $2n$ triangles. The sampling procedure relies on connections between graph-encoded surfaces and permutations, and basic properties of the symmetric group. We implement our method and present a number of experimental findings based on the analysis of $138$ million runs of our sampling procedure, producing graph-encoded surfaces with up to $280$ triangles. Namely, we determine that, for $n$ fixed, the empirical mean genus $\bar{g}(n)$ of our sample is very close to $\bar{g}(n) = \frac{n-1}{2} - (16.98n -110.61)^{1/4}$. Moreover, we present experimental evidence that the associated genus distribution more and more concentrates on a vanishing portion of all possible genera as $n$ tends to infinity. Finally, we observe from our data that the mean number of non-trivial symmetries of a uniformly chosen graph encoding of a surface decays to zero at a rate super-exponential in $n$.