English

Edge-statistics beyond $1/e$

Combinatorics 2025-10-29 v1

Abstract

For integers kk and \ell, let ind(k,)\operatorname{ind}(k, \ell) be the maximum proportion of kk-vertex subsets of a large graph that induce exactly \ell edges. The edge-statistics theorem (conjectured by Alon-Hefetz-Krivelevich-Tyomkyn, and proved by Kwan-Sudakov-Tran, Fox-Sauermann, and Martinsson-Mousset-Noever-Truji\'c) asserts that, for kk \to \infty and 0<<(k2)0 < \ell <\binom{k}{2}, one has ind(k,)1/e+o(1)\operatorname{ind}(k, \ell) \le 1/e + o(1). We investigate the ''stability'' of this problem: how can one improve this bound under additional assumptions on \ell? In particular, the edge-statistics theorem is tight when {1,k1,(k2)(k1),(k2)1}\ell\in \{1,k-1,\binom{k}{2}-(k-1),\binom{k}{2}-1\}; we show that for all other \ell, one can replace 1/e1/e with a strictly smaller constant. This extends an analogous result of Ueltzen in the setting of graph inducibility. We also obtain a much stronger (and essentially optimal) upper bound on ind(k,)\operatorname{ind}(k, \ell) when \ell is far from a multiple of kk, refining and extending previous bounds due to Fox and Sauermann.

Keywords

Cite

@article{arxiv.2510.24691,
  title  = {Edge-statistics beyond $1/e$},
  author = {Alexandr Grebennikov and Matthew Kwan},
  journal= {arXiv preprint arXiv:2510.24691},
  year   = {2025}
}

Comments

24 pages + 4 page appendix