The Effect of Induced Subgraphs on Quasi-Randomness

TL;DR

This paper shows that the distribution of induced copies of a fixed graph H in a graph can determine whether the graph is quasi-random, linking local subgraph patterns to global randomness properties.

Contribution

It establishes that matching the induced subgraph distribution of H to that in G(n,p) implies the graph is quasi-random or p'-quasi-random, a novel characterization.

Findings

Distribution of induced copies of H determines quasi-randomness.

Matching induced subgraph distribution implies global randomness properties.

Introduces new proof techniques combining probabilistic, algebraic, and combinatorial methods.

Abstract

One of the main questions that arise when studying random and quasi-random structures is which properties P are such that any object that satisfies P "behaves" like a truly random one. In the context of graphs, Chung, Graham, and Wilson call a graph p-quasi-random} if it satisfies a long list of the properties that hold in G(n,p) with high probability, like edge distribution, spectral gap, cut size, and more. Our main result here is that the following holds for any fixed graph H: if the distribution of induced copies of H in a graph G is close (in a well defined way) to the distribution we would expect to have in G(n,p), then G is either p-quasi-random or p'-quasi-random, where p' is the unique non-trivial solution of a certain polynomial equation. We thus infer that having the correct distribution of induced copies of any single graph H is enough to guarantee that a graph has the properties of a random one. The proof techniques we develop here, which combine probabilistic, algebraic and combinatorial tools, may be of independent interest to the study of quasi-random structures.