Evolution of robustness in growing random networks

TL;DR

This paper studies how the robustness of growing random networks, measured by the Kirchhoff index, evolves over time as nodes and edges are added, providing formulas and simulations to predict network robustness.

Contribution

It derives closed-form expressions for the evolution of the Kirchhoff index in growing networks, linking local node properties to global robustness changes.

Findings

Derived formulas for Kirchhoff index variation during network growth

Identified how node connection properties influence global robustness

Confirmed theoretical predictions with numerical simulations

Abstract

Networks are widely used to model the interaction between individual dynamical systems. In many instances, the total number of units as well as the interaction coupling are not fixed in time, but rather constantly evolve. In terms of networks, this means that the number of nodes and edges change in time. Various properties of coupled dynamical systems essentially depend on the structure of the interaction network, such as their robustness to noise. It is therefore of interest to predict how these properties are affected when the network grows and what is their relation to the growing mechanism. Here, we focus on the time-evolution of the network's Kirchhoff index. We derive closed form expressions for its variation in various scenarios including both the addition of edges and nodes. For the latter case, we investigate the evolution where a single node with one and two edges connecting to existing nodes are added recursively to a network. In both cases we derive relations between the properties of the nodes to which the new one connects, and the global evolution of the network robustness. In particular, we show how different scalings of the Kirchhoff index as a function of the number of nodes are obtained. We illustrate and confirm the theory with numerical simulations of growing random networks.