Competing growth processes with random growth rates and random birth times

TL;DR

This paper introduces a novel technique combining continuous-time embedding and Poisson limit theorems to analyze extremal objects in complex stochastic networks and processes, providing new insights into their behavior.

Contribution

It develops a versatile limit theorem based on extreme value theory and demonstrates its application across various complex stochastic models.

Findings

New limit theorem for extremal object sizes

Application to preferential attachment networks with fitness

Insights into branching processes with selection and mutation

Abstract

Finding the most powerful node in a dynamic random network, the largest set in a partition-valued stochastic process, or the largest family in an evolving population at a given time, can be a very difficult problem. This is particularly the case when the underlying stochastic process has complex dependencies and the individual strength of an object has an impact that only plays out over time. We propose a novel technique to deal with such problems and show how it can be applied to a broad range of examples where it produces new insight and surprising results. The method relies on two steps: In the first step, which is highly problem dependent, the problem is embedded into continuous time so that the evolution of the sizes of objects after their individual birth times become approximately independent while we only need minimal control over the birth times themselves. Once such an embedding is achieved, the second step is to apply a Poisson limit theorem that allows a comparison of object sizes in a critical window and therefore allows a description of features of extremal objects. In this paper we prove such a versatile limit theorem, based on extreme value theory, and show how the technique can be used to study extremal behaviour in different types of preferential attachment networks with fitness, branching processes with selection and mutation, and random permutations with random cycle weights.