Identifying early-warning indicators of tipping points in networked systems against sequential attacks

TL;DR

This paper investigates early-warning indicators of tipping points in various networked systems under simultaneous attacks, identifying warning regions and robustness relationships that can predict system failure and aid in risk management.

Contribution

It introduces a method to detect warning regions and analyze robustness in networked systems facing concurrent disruptions, extending understanding beyond single-point failure models.

Findings

Warning regions precede tipping points in networks.

Network robustness correlates with the size of simultaneous attacks.

The approach applies to diverse network architectures.

Abstract

Network structures in a wide array of systems such as social networks, transportation, power and water distribution infrastructures, and biological and ecological systems can exhibit critical thresholds or tipping points beyond which there are disproportionate losses in the system functionality. There is growing concern over tipping points and failure tolerance of such systems as tipping points can lead to an abrupt loss of intended functionality and possibly non-recoverable states. While attack tolerance of networked systems has been intensively studied for the disruptions originating from a single point of failure, there have been instances where real-world systems are subject to simultaneous or sudden onset of concurrent disruption at multiple locations. Using open-source data from the United States Airspace Airport network and Indian Railways Network, and random networks as prototype class of systems, we study their responses to synthetic attack strategies of varying sizes. For both types of networks, we observe the presence of warning regions, which serve as a precursor to the tipping point. Further, we observe the statistically significant relationships between network robustness and size of simultaneous distribution, which generalizes to the networks with different topological attributes for random failures and targeted attacks. We show that our approach can determine the entire robustness characteristics of networks of disparate architecture subject to disruptions of varying sizes. Our approach can serve as a paradigm to understand the tipping point in real-world systems, and the principle can be extended to other disciplines to address critical issues of risk management and resilience.