Predicting the Cascading Failure Propagation Path in Complex Networks Based On Attention-LSTM Neural Networks

Complex networks are vulnerable to cascading failure through which a few initial failure events lead to catastrophic network damages. Quick and accurate failure cascade prediction lays a solid foundation for taking effective measures to mitigate cascading failure. In this paper, we investigate predicting cascading failure propagation path in complex networks by using neural networks. A cascading failure simulation model considering the cumulative effect of overloading of components over time is firstly established to generate cascading failure cases in complex networks. Then, a cascading failure prediction model combining an attention mechanism and long and short-term memory (LSTM) neural networks is proposed for failure cascade prediction. We generate cascading failure cases as the ground truth with the cascading failure simulation model and make predictions with the Attention-LSTM neural network in synthesized complex networks. Simulation results show that our proposed method can predict the path of cascading failure propagation quickly and accurately.