Avoiding catastrophic failure in correlated networks of networks
By S. D. S. Reis, Yan-Long Hu, Andres Babino, J. S. Andrade Jr., S. Canals, Mariano Sigman, Hernán A. Makse. Nature Physics, 2014
236 citations recorded by the source corpus.
Connecting complex networks is known to exacerbate perturbations and lead to cascading failures, but natural networks of networks are surprisingly stable. A theory now proposes that network structure holds the key to understanding this paradox. Networks in nature do not act in isolation, but instead exchange information and depend on one another to function properly1,2,3. Theory has shown that connecting random networks may very easily result in abrupt failures3,4,5,6. This finding reveals an intriguing paradox7,8: if natural systems organize in interconnected networks, how can they be so stable? Here we provide a solution to this conundrum, showing that the stability of a system of networks relies on the relation between the internal structure of a network and its pattern of connections to other networks. Specifically, we demonstrate that if interconnections are provided by network hubs, and the connections between networks are moderately convergent, the system of networks is stable and robust to failure. We test this theoretical prediction on two independent experiments of functional brain networks (in task and resting states), which show that brain networks are connected with a topology that maximizes stability according to the theory.
Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/4008ee1f6632c8bb4bad74a1a57a1cd3530588c8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
In Statistical and Nonlinear Physics
- Matjaz Perc
- Manlio De Domenico
- Marco Grassia
- Filippo Radicchi
- Hernán A. Makse
- Giuseppe Mangioni
- J. P. Gleeson
- Oriol Artime
Citations in the corpus
- Robustness and resilience of complex networks (2024, methodology): “The concept of network design has been further extended 206 to interdependent networks 162,224 .”