A Cascading Failure Model by Quantifying Interactions

TL;DR

This paper introduces a simple interaction-based model to simulate cascading failures in complex systems, demonstrating that targeted removal of key links can significantly reduce failure risk.

Contribution

The paper presents a novel cascading failure model using an interaction matrix and introduces an index to identify critical links for mitigation.

Findings

The model captures key features of real cascades.

Important links follow a power-law distribution.

Targeted link removal reduces failure risk effectively.

Abstract

Cascading failures triggered by trivial initial events are encountered in many complex systems. It is the interaction and coupling between components of the system that causes cascading failures. We propose a simple model to simulate cascading failure by using the matrix that determines how components interact with each other. A careful comparison is made between the original cascades and the simulated cascades by the proposed model. It is seen that the model can capture general features of the original cascades, suggesting that the interaction matrix can well reflect the relationship between components. An index is also defined to identify important links and the distribution follows an obvious power law. By eliminating a small number of most important links the risk of cascading failures can be significantly mitigated, which is dramatically different from getting rid of the same number of links randomly.