Alon–Hefetz–Krivelevich–Tyomkyn superlinear sparsity conjecture

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For an nn-vertex graph GG, let XG,kX_{G,k} be the number of edges induced by a uniformly random kk-vertex subset of GG. Define

I(n,k,ℓ)=max⁡{Pr⁡(XG,k=ℓ):∣V(G)∣=n}I(n,k,\ell)=\max\{\Pr(X_{G,k}=\ell):|V(G)|=n\}

and

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

Alon–Hefetz–Krivelevich–Tyomkyn's superlinear sparsity conjecture. For all k,ℓk,\ell satisfying

min⁡\originalleft{ℓ,(k2)−ℓ\aftergroup\originalright}=ωk(k),\min\mathopen{}\mathclose\bgroup\originalleft\{\ell,\binom{k}{2}-\ell\aftergroup\egroup\originalright\}=\omega_k(k),

we have

ind⁡(k,ℓ)=ok(1).\operatorname{ind}(k,\ell)=o_k(1).

This predicts asymptotic anticoncentration whenever both the number of induced edges and the number of nonedges grow superlinearly in kk. Its status is not resolved in the supplied text.

References

Primary source

Matthew Kwan, Benny Sudakov and Tuan Tran, “Anticoncentration for subgraph statistics”, arXiv:1807.05202 (2018).

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