Emergence of Robustness in Network of Networks

Abstract

A model of interdependent networks of networks (NoN) has been introduced recently in the context of brain activation to identify the neural collective influencers in the brain NoN. Here we develop a new approach to derive an exact expression for the random percolation transition in Erdös-Rényi NoN. Analytical calculations are in excellent agreement with numerical simulations and highlight the robustness of the NoN against random node failures. Interestingly, the phase diagram of the model unveils particular patterns of interconnectivity for which the NoN is most vulnerable. Our results help to understand the emergence of robustness in such interdependent architectures.

5 pages, 2 figures

References in corpus (5)

• Influence maximization in complex networks through optimal percolation
• Percolation on sparse networks
• Avoiding catastrophic failure in correlated networks of networks
• Eradicating Catastrophic Collapse in Interdependent Networks via Reinforced Nodes
• Model of Brain Activation Predicts the Neural Collective Influence Map of the Brain

Cited by in corpus (3)

• Correlated network of networks enhances robustness against catastrophic failures
• Modeling multi-scale data via a network of networks
• Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks