Paper detail

A threshold for cutoff in two-community random graphs

In this paper, we are interested in the impact of communities on the mixing behavior of the non-backtracking random walk. We consider sequences of sparse random graphs of size $N$ generated according to a variant of the classical configuration model which incorporates a two-community structure. The strength of the bottleneck is measured by a parameter $α$ which roughly corresponds to the fraction of edges that go from one community to the other. We show that if $α\gg \frac{1}{\log N}$, then the non-backtracking random walk exhibits cutoff at the same time as in the one-community case, but with a larger cutoff window, and that the distance profile inside this window converges to the Gaussian tail function. On the other hand, if $α\ll \frac{1}{\log N}$ or $α\asymp \frac{1}{\log N}$, then the mixing time is of order $1/α$ and there is no cutoff.