A Cascading Failure Model by Quantifying Interactions
For researchers studying cascading failures in complex systems, this work provides a method to identify critical links and mitigate risk, but the model is simple and the results are qualitative.
The paper proposes a simple model using an interaction matrix to simulate cascading failures in complex systems. The model captures general features of real cascades, and eliminating a small number of important links significantly mitigates risk, unlike random removal.
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.