Failure behavior in a connected configuration model under a critical loading mechanism

TL;DR

This paper analyzes a cascading failure process in a random graph model, revealing scale-free failure sizes under critical loading, with implications for understanding network robustness and failure propagation.

Contribution

It introduces a rigorous analysis of scale-free failure behavior in configuration models under critical load, extending understanding of cascade failures in complex networks.

Findings

Failure sizes follow a scale-free distribution under critical loading.

Scale-free behavior persists in the giant component of the network.

Results are validated through simulations on various graph structures.

Abstract

We study a cascading edge failure mechanism on a connected random graph with a prescribed degree sequence, sampled using the configuration model. This mechanism prescribes that every edge failure puts an additional strain on the remaining network, possibly triggering more failures. We show that under a critical loading mechanism that depends on the global structure of the network, the number of edge failure exhibits scale-free behavior (up to a certain threshold). Our result is a consequence of the failure mechanism and the graph topology. More specifically, the critical loading mechanism leads to scale-free failure sizes for any network where no disconnections take place. The disintegration of the configuration model ensures that the dominant contribution to the failure size comes from edge failures in the giant component, for which we show that the scale-free property prevails. We prove this rigorously for sublinear thresholds, and we explain intuitively why the analysis follows through for linear thresholds. Moreover, our result holds for other graph structures as well, which we validate with simulation experiments.