Node-Level Resilience Loss in Dynamic Complex Networks

Giannis Moutsinas,Weisi Guo

doi:10.1038/s41598-020-60501-9

Giannis Moutsinas, Weisi Guo

Open Access

https://doi.org/10.1038/s41598-020-60501-9

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Journal: Scientific Reports	Publication Date: Feb 27, 2020
Citations: 28	License type: open-access

Affiliation: Cranfield University, The Alan Turing Institute

Abstract

In an increasingly connected world, the resilience of networked dynamical systems is important in the fields of ecology, economics, critical infrastructures, and organizational behaviour. Whilst we understand small-scale resilience well, our understanding of large-scale networked resilience is limited. Recent research in predicting the effective network-level resilience pattern has advanced our understanding of the coupling relationship between topology and dynamics. However, a method to estimate the resilience of an individual node within an arbitrarily large complex network governed by non-linear dynamics is still lacking. Here, we develop a sequential mean-field approach and show that after 1-3 steps of estimation, the node-level resilience function can be represented with up to 98% accuracy. This new understanding compresses the higher dimensional relationship into a one-dimensional dynamic for tractable understanding, mapping the relationship between local dynamics and the statistical properties of network topology. By applying this framework to case studies in ecology and biology, we are able to not only understand the general resilience pattern of the network, but also identify the nodes at the greatest risk of failure and predict the impact of perturbations. These findings not only shed new light on the causes of resilience loss from cascade effects in networked systems, but the identification capability could also be used to prioritize protection, quantify risk, and inform the design of new system architectures.

Highlights

A gap in understanding exists between individual dynamics and the coupled dynamics in a large-scale networked complex system
We present a framework for tractably analyzing the resilience of individual nodes as a function of the individual dynamics and the network property
We show that our method can be used to: (1) Estimate the equilibrium behaviour outside the critical region

Summary

Introduction

The resulting framework is a robust and accurate way of measuring the networked dynamics and resilience function at each node, with the ability to identify vulnerable nodes. The resulting framework is a robust and accurate way of measuring the networked dynamics and resilience function www.nature.com/scientificreports at each node, with the ability to identify vulnerable nodes.

Results

Conclusion