Credit to:


Edge expansion of a weighted graph is defined as:

We say a graph is an expander if, for all we have . for some large .

Small set expansion: For any with , we have .

Theorem 2.3 (The hypercontractive inequality). For all , if and , then

Theorem: The -noisy hypercube is a small set expander.

Proof: The key step will be to use the hypercontractive inequality. This is because we can express the expansion by the following inner-product:

Now using Holder and Hypercontractivity:

We conclude that:


Some other good things to remember:

  • concentration, anti-concentration for low degree functions
  • Junta’s are the classic example of non-expanding sets in the hypercube.
  • Hypercontractivity: says stuff about the noise operator, and about relationships between different norms
  • Small influence you’re a Junta (Freidgut Junta theorem)
  • KKL gives you a coordinate with high influence
  • Expansion could be pretty good. It’s best if you get a graph that people already know about, or a Cayley graph. But in a pinch might be useful.