Projects per year
Abstract
In this paper, we introduce a model of depthweighted random recursive trees, created by recursively joining a new leaf to an existing vertex v. In this model, the probability of choosing v depends on its depth in the tree. In particular, we assume that there is a function f such that if k has depth k then its probability of being chosen is proportional to f(k). We consider the expected value of the diameter of this model as determined by f, and for various increasing f we find expectations that range from polylogarithmic to linear.
Original language  English 

Pages (fromto)  851866 
Number of pages  16 
Journal  Random Structures & Algorithms 
Volume  56 
Issue number  3 
DOIs  
Publication status  Published  May 2020 
Keywords
 height
 random recursive trees
 random trees
Projects
 1 Finished

Advances in the analysis of random structures and their applications: relationships among models
Australian Research Council (ARC)
1/08/12 → 31/12/17
Project: Research