Uniform random recursive trees are, historically, one the first models used to represent the growth of networks.

In this talk, we present two methods for cutting down uniform random recursive trees designed to reduce the number of steps until the tree is destroyed. The first method deterministically removes vertices in decreasing order of their degree, while the second method deletes vertices randomly chosen and biased according to their degree. We compare our results on the number of cuts to destruction to those of the classic cutting process, which consists of deleting, at each step, uniformly at random edges that are connected to the root.