An Exceeding Recovery Model for Enhancing Network Resilience Against Cascading Failures

Failure and recovery underlie many complex systems ranging from critical infrastructures to organisms. In many real complex systems, the reliability of repaired components is improved due to the exceeding recovery mechanism, and such systems typically have enhanced failure resistance. The main motivation of this study lies in developing an exceeding recovery model to capture the exceeding recovery mechanics of complex network systems. In the proposed model, cascading failure and exceeding recovery perform concomitantly. The network resilience analysis is performed in the Barabási–Albert and Erdős–Rényi networks by focusing on the exceeding recovery process from random and targeted attacks. The results show that for a given initial failure size, there is a critical value of the exceeding recovery coefficient above which the network can restore to the normal state, but below this value, the network abruptly collapses. The proposed model is compared with the conventional recovery model. The comparison indicates that the proposed model can recover to a significantly high level in a short recovery time and at a low recovery cost. The exceeding recovery mechanism strongly affects the failure–recovery property, which is expressed as reduced risk of a secondary failure at the micro level and enhanced heterogeneity of the load distribution at the macro level. These findings provide a guideline to address the exceeding recovery problem of a network and can help to design networks with better resilience against cascading failures.