Sandpile cascades on interacting tree-like networks

TL;DR

This paper models cascading failures in interdependent networks using a sandpile model, revealing that coupling stabilizes individual networks but can cause larger joint avalanches, informing strategies for network resilience.

Contribution

It introduces a multi-type branching process approach to analyze sandpile cascades on coupled networks, providing new insights into how interdependence affects cascade size and frequency.

Findings

Large avalanches are mitigated within individual networks due to coupling.

Interdependent networks more frequently experience large avalanches simultaneously.

Increased coupling amplifies the size and frequency of avalanches in the second network.

Abstract

The vulnerability of an isolated network to cascades is fundamentally affected by its interactions with other networks. Motivated by failures cascading among electrical grids, we study the Bak-Tang-Wiesenfeld sandpile model on two sparsely-coupled random regular graphs. By approximating avalanches (cascades) as a multi-type branching process and using a generalization of Lagrange's expansion to multiple variables, we calculate the distribution of avalanche sizes within each network. Due to coupling, large avalanches in the individual networks are mitigated--in contrast to the conclusion for a simpler model [36]. Yet when compared to uncoupled networks, interdependent networks more frequently suffer avalanches that are large in both networks. Thus sparse connections between networks stabilize them individually but destabilize them jointly, as coupling introduces reservoirs for extra load yet also inflicts new stresses. These results suggest that in practice, to greedily mitigate large avalanches in one network, add connections between networks; conversely, to mitigate avalanches that are large in both networks, remove connections between networks. We also show that when only one network receives load, the largest avalanches in the second network increase in size and in frequency, an effect that is amplified with increased coupling between networks and with increased disparity in total capacity. Our framework is applicable to modular networks as well as to interacting networks and provides building blocks for better prediction of cascading processes on networks in general.