Counting dense connected hypergraphs via the probabilistic method

TL;DR

This paper derives an asymptotic formula for counting connected r-uniform hypergraphs with many edges, extending previous results and employing probabilistic methods and local limit theorems.

Contribution

It provides a new asymptotic enumeration formula for dense connected hypergraphs, covering a range of edge densities and simplifying proof techniques.

Findings

Asymptotic formula for connected hypergraphs with m/n→∞

Use of probabilistic methods and local limit theorems

Simpler proof approach compared to sparse cases

Abstract

In 1990 Bender, Canfield and McKay gave an asymptotic formula for the number of connected graphs on $[n]=\{1,2,\ldots,n\}$ with $m$ edges, whenever $n\to\infty$ and $n-1\le m=m(n)\le \binom{n}{2}$. We give an asymptotic formula for the number $C_r(n,m)$ of connected $r$-uniform hypergraphs on $[n]$ with $m$ edges, whenever $r\ge 3$ is fixed and $m=m(n)$ with $m/n\to\infty$, i.e., the average degree tends to infinity. This complements recent results of Behrisch, Coja-Oghlan and Kang (the case $m=n/(r-1)+\Theta(n)$) and the present authors (the case $m=n/(r-1)+o(n)$, i.e., `nullity' or `excess' $o(n)$). The proof is based on probabilistic methods, and in particular on a bivariate local limit theorem for the number of vertices and edges in the largest component of a certain random hypergraph. The arguments are much simpler than in the sparse case; in particular, we can use `smoothing' techniques to directly prove the local limit theorem, without needing to first prove a central limit theorem.