Ageing of complex networks

TL;DR

This paper models the aging process of complex networks, specifically tree networks, by combining growth and rewiring processes, and compares different aging dynamics to understand their effects on network topology.

Contribution

It introduces a stochastic aging model for tree networks that preserves degree distribution and compares local versus non-local rewiring effects.

Findings

Non-local rewiring accelerates network aging.

Aging preserves degree distribution but alters topology.

Different rewiring strategies lead to distinct aging dynamics.

Abstract

Many real-world complex networks arise as a result of a competition between growth and rewiring processes. Usually the initial part of the evolution is dominated by growth while the later one rather by rewiring. The initial growth allows the network to reach a certain size while rewiring to optimise its function and topology. As a model example we consider tree networks which first grow in a stochastic process of node attachment and then age in a stochastic process of local topology changes. The ageing is implemented as a Markov process that preserves the node-degree distribution. We quantify differences between the initial and aged network topologies and study the dynamics of the evolution. We implement two versions of the ageing dynamics. One is based on reshuffling of leaves and the other on reshuffling of branches. The latter one generates much faster ageing due to non-local nature of changes.