Hypergraph planted clique conjecture

Let DD-uniform hypergraphs on the vertex set [N][N] be hypergraphs whose hyperedges are incident on DD vertices. Under the null model, let GGD(N,p)G\sim\mathcal{G}_D(N,p), where each hyperedge is included independently with probability pp. Under the planted model, let GGD(N,p;K)G\sim\mathcal{G}_D(N,p;K), where KDK\geq D vertices are chosen uniformly at random, all (KD)\binom{K}{D} hyperedges among them are included, and every remaining hyperedge is included independently with probability pp. For a test ψN:HD,N{0,1}\psi_N:{\mathbb{H}}_{D,N}\mapsto\{0,1\}, define

E(ψN)=12EH0[ψN(G)]+12EH1[1ψN(G)].\mathcal{E}(\psi_N)=\frac{1}{2}\mathbb{E}_{H_0}[\psi_N(G)]+\frac{1}{2}\mathbb{E}_{H_1}[1-\psi_N(G)].

Hypergraph planted clique conjecture. Suppose that p=1/2p=1/2 and that D2D\geq 2 is a fixed integer. If

lim supNlogKlogN<1,\limsup_{N\to\infty}\frac{\log K}{\log\sqrt{N}}<1,

then for every sequence of tests {ψN}N1\{\psi_N\}_{N\geq 1} computable in time polynomial in NDN^D,

lim infNE(ψN)12.\liminf_{N\to\infty}\mathcal{E}(\psi_N)\geq\frac{1}{2}.

This is the assumed computational hardness of detecting a planted clique in a random DD-uniform hypergraph below the N\sqrt{N} scale, and it serves as the primitive for the paper's lower bounds on polynomial-time adaptation. No resolution status is supplied in the source.

Sources & referencesView supporting material

Primary source

Ashwin Pananjady and Richard J. Samworth, “Isotonic regression with unknown permutations: Statistics, computation, and adaptation”, arXiv:2009.02609 (2021).

Additional references

2 papers in this index state this conjecture (2020). The statement above is taken from the most recent of them; the others are arXiv:2008.00926.

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