Cascading failures in spatially-embedded random networks

Abstract

Cascading failures constitute an important vulnerability of interconnected systems. Here we focus on the study of such failures on networks in which the connectivity of nodes is constrained by geographical distance. Specifically, we use random geometric graphs as representative examples of such spatial networks, and study the properties of cascading failures on them in the presence of distributed flow. The key finding of this study is that the process of cascading failures is non-self-averaging on spatial networks, and thus, aggregate inferences made from analyzing an ensemble of such networks lead to incorrect conclusions when applied to a single network, no matter how large the network is. We demonstrate that this lack of self-averaging disappears with the introduction of a small fraction of long-range links into the network. We simulate the well studied preemptive node removal strategy for cascade mitigation and show that it is largely ineffective in the case of spatial networks. We introduce an altruistic strategy designed to limit the loss of network nodes in the event of a cascade triggering failure and show that it performs better than the preemptive strategy. Finally, we consider a real-world spatial network viz. a European power transmission network and validate that our findings from the study of random geometric graphs are also borne out by simulations of cascading failures on the empirical network.

13 pages, 15 figures

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• Building Damage-Resilient Dominating Sets in Complex Networks against Random and Targeted Attacks
• Limits of Predictability of Cascading Overload Failures in Spatially-Embedded Networks with Distributed Flows
• Influence Maximization for Fixed Heterogeneous Thresholds
• Isolation probabilities in dynamic soft random geometric graphs
• Generation of Synthetic Spatially Embedded Power Grid Networks
• Anomalous Contagion and Renormalization in Dynamical Networks with Nodal Mobility
• Cascading Failures in Finite-Size Random Geometric Networks