Alon–Hefetz–Krivelevich–Tyomkyn superlinear sparsity conjecture

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

and

ind(k,)=limnI(n,k,).\operatorname{ind}(k,\ell)=\lim_{n\to\infty}I(n,k,\ell).

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

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

we have

ind(k,)=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.

Sources & referencesView supporting material

Primary source

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

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