Regularity method and large deviation principles for the Erdos--R\'enyi hypergraph
Abstract
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 Erdos--R\'enyi hypergraph for any fixed r 2, generalizing and improving on previous results for the Erdos--R\'enyi 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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