Estimating graph parameters with random walks

Anna Ben-Hamou, Roberto Imbuzeiro Oliveira, Yuval Peres

Abstract: An algorithm observes the trajectories of random walks over an unknown graph $G$, starting from the same vertex $x$, as well as the degrees along the trajectories. For all finite connected graphs, one can estimate the number of edges $m$ up to a bounded factor in $O(t_{\text{rel}}^{3/4}​\sqrt{m/d})$ steps, where $t_{\text{rel}}$ is the relaxation time of the lazy random walk on $G$ and $d$ is the minimum degree in $G$. Alternatively, $m$ can be estimated in $O(t_{\text{unif}}​+t_{\text{rel}}^{5/6}\sqrt{​n})$, where $n$ is the number of vertices and $t_{\text{unif}}$​ is the uniform mixing time on $G$. The number of vertices $n$ can then be estimated up to a bounded factor in an additional $O(t_{\text{unif}}​m/n)$ steps. Our algorithms are based on counting the number of intersections of random walk paths $X,Y$, i.e. the number of pairs $(t,s)$ such that $X_t​=Y_s​$. This improves on previous estimates which only consider collisions (i.e. times $t$ with $X_t​=Y_t$​). We also show that the complexity of our algorithms is optimal, even when restricting to graphs with a prescribed relaxation time. Finally, we show that, given either $m$ or the mixing time of $G$, we can compute the “other parameter” with a self-stopping algorithm.