Repeated averaging on expanders I: random $d$-regular graph

Source abstract

Repeated averaging on graphs is a natural prototype of local stochastic exchange, modeling consensus formation, information spreading, and wealth redistribution. Its convergence has attracted longstanding interest, from Bourgain's complete-graph $L^1$ cutoff question in the 80s (answered in recent years by Chatterjee, Diaconis, Sly, and Zhang) and the systematic study of Aldous and Lanoue. Addressing the still outstanding and widely asked $L^1$-cutoff question on general graphs, we establish a criterion based on size-biased sampling, and develop a general framework for sparse expanders. Specifically, we prove $L^1$ cutoff with a Gaussian profile on random $d$-regular graphs, for any $d\ge 3$ and uniformly over starting vertices. Remarkably, although the expected mass profile agrees exactly with the corresponding random-walk distribution, cutoff occurs strictly later than both the random walk cutoff time and the universal lower bound attained on complete graphs, revealing an unexpected entropy gap. The framework is robust, involving ideas of polymer representations, random geometry, universal-cover lifting, and adaptive revealing. Our companion work will extend it to Ramanujan graphs and bounded-degree configuration models.