The Edge-statistics Conjecture for edge-inducibility

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Let kk and llll be integers with 0<ll<(k2)0<ll<{k\choose 2}. For n≥kn\geq k, let I(n,k,ll)I(n,k,ll) be the maximum, over all nn-vertex graphs GG, of the probability that a uniformly random kk-vertex subset of GG spans exactly llll edges, and define the edge-inducibility by

ind⁡(k,ll)=lim⁡n→∞I(n,k,ll).\operatorname{ind}(k,ll)=\lim_{n\to\infty} I(n,k,ll).

The Edge-statistics Conjecture. For all integers kk and ℓ\ell with 0<ℓ<(k2)0<\ell<{k\choose 2},

ind⁡(k,ℓ)≤1e+ok(1).\operatorname{ind}(k,\ell)\leq \frac{1}{e}+o_k(1).

The conjecture gives a uniform asymptotic upper bound on the probability of observing any nontrivial prescribed number of edges in a random kk-vertex subset of a very large graph. The paper states that earlier work proved substantial ranges and that the present result resolves the remaining cases, so the conjecture is solved.

References

Primary source

Jacob Fox and Lisa Sauermann, “A Completion of the Proof of the Edge-statistics Conjecture”, arXiv:1809.01352 (2020).

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