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  1. ; (, The Annals of Applied Probability)
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  2. ; ; (, Duke Mathematical Journal)
    We develop a quantitative large deviations theory for random hypergraphs, which rests on tensor decomposition and counting lemmas under a novel family of cut-type norms. As our main application, we obtain sharp asymptotics for joint upper and lower tails of homomorphism counts in the r-uniform Erdo ̋s–Rényi hypergraph for any fixed r≥2, generalizing and improving on previous results for the Erdo ̋s–Rényi graph (r=2). The theory is sufficiently quantitative to allow the density of the hypergraph to vanish at a polynomial rate, and additionally yields tail asymptotics for other nonlinear functionals, such as induced homomorphism counts. 
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