A Class of Random Recursive Tree Algorithms with Deletion

Abstract

We examine a discrete random recursive tree growth process that, at each time step, either adds or deletes a node from the tree with fixed, complementary probabilities. Node addition follows the usual uniform attachment model. For node removal, we identify a class of deletion rules guaranteeing the current tree conditioned on its size is uniformly distributed over its range. By using generating function theory and singularity analysis, we obtain asymptotic estimates for the expectation and variance of a tree’s size, as well as its expected leaf count and root degree. In all cases, the behavior of such trees falls into three regimes determined by the insertion probability. Interestingly, the results are independent of the specific class member deletion rule used.