Controlling the uncertain response of real multiplex networks to random damage

TL;DR

This paper demonstrates that real multiplex networks exhibit large fluctuations in response to random damage, and introduces a new metric called safeguard centrality to identify nodes critical for preventing system collapse.

Contribution

It reveals the limitations of mean-field approaches in predicting network robustness and proposes a large deviation approach along with safeguard centrality for better system control.

Findings

Large fluctuations in network response to damage.

Large deviation approach better predicts robustness.

Safeguard centrality identifies key nodes.

Abstract

We reveal large fluctuations in the response of real multiplex networks to random damage of nodes. These results indicate that the average response to random damage, traditionally considered in mean-field approaches to percolation, is a poor metric of system robustness. We show instead that a large deviation approach to percolation provides a more accurate characterization of system robustness. We identify an effective percolation threshold at which we observe a clear abrupt transition separating two distinct regimes in which the most likely response to damage is either a functional or a dismantled multiplex network. We leverage our findings to propose a new metric, named safeguard centrality, able to single out the nodes that control the response of the entire multiplex network to random damage. We show that safeguarding the function of top-scoring nodes is sufficient to prevent system collapse.