Redundant interdependencies boost the robustness of multilayer networks

TL;DR

This paper introduces a new percolation model for multilayer networks where nodes are functional if they operate in at least two layers, showing that adding layers can enhance robustness through redundant interdependencies.

Contribution

The paper proposes a novel percolation model for multilayer networks that accounts for redundancy, demonstrating increased robustness with more layers, supported by a message-passing theoretical framework.

Findings

Adding layers increases network robustness due to redundancy.

The model generalizes existing two-layer percolation models.

The message-passing theory accurately predicts robustness in real networks.

Abstract

In the analysis of the robustness of multiplex networks, it is commonly assumed that a node is functioning only if its interdependent nodes are simultaneously functioning. According to this model, a multiplex network becomes more and more fragile as the number of layers increases. In this respect, the addition of a new layer of interdependent nodes to a preexisting multiplex network will never improve its robustness. Whereas such a model seems appropriate to understand the effect of interdependencies in the simplest scenario of a network composed of only two layers, it may seem not suitable to characterize the robustness of real systems formed by multiple network layers. It seems in fact unrealistic that a real system, evolved, through the development of multiple layers of interactions, towards a fragile structure. In this paper, we introduce a model of percolation where the condition that makes a node functional is that the node is functioning in at least two of the layers of the network. The model reduces to the commonly adopted percolation model for multiplex networks when the number of layers equals two. For larger number of layers, however, the model describes a scenario where the addition of new layers boosts the robustness of the system by creating redundant interdependencies among layers. We prove this fact thanks to the development of a message-passing theory able to characterize the model in both synthetic and real-world multiplex graphs.