Strong noise sensitivity and random graphs

Eyal Lubetzky, Jeffrey E. Steif

Research output: Contribution to journalArticlepeer-review


The noise sensitivity of a Boolean function describes its likelihood to flip under small perturbations of its input. Introduced in the seminal work of Benjamini, Kalai and Schramm [Inst. Hautes Études Sci. Publ. Math. 90 (1999) 5-43], it was there shown to be governed by the first level of Fourier coefficients in the central case of monotone functions at a constant critical probability pc. Here we study noise sensitivity and a natural stronger version of it, addressing the effect of noise given a specific witness in the original input. Our main context is the Erdõs-Rényi random graph, where already the property of containing a given graph is sufficiently rich to separate these notions. In particular, our analysis implies (strong) noise sensitivity in settings where the BKS criterion involving the first Fourier level does not apply, for example, when pc →0 polynomially fast in the number of variables.

Original languageEnglish (US)
Pages (from-to)3239-3278
Number of pages40
JournalAnnals of Probability
Issue number6
StatePublished - 2015


  • Noise sensitivity of Boolean functions
  • Random graphs

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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