Network recovery based on system crash early warning in a cascading failure model

Abstract

Abstract This paper investigates the possibility of saving a network that is predicted to have a cascading failure that will eventually lead to a total collapse. We model cascading failures using the recently proposed KQ model. Then predict an impending total collapse by monitoring critical slowing down indicators and subsequently attempt to prevent the total collapse of the network by adding new nodes. To this end, we systematically evaluate five node addition rules, the effect of intervention delay and network degree heterogeneity. Surprisingly, unlike for random homogeneous networks, we find that a delayed intervention is preferred for saving scale free networks. We also find that for homogeneous networks, the best strategy is to wire newly added nodes to existing nodes in a uniformly random manner. For heterogeneous networks, however, a random selection of nodes based on their degree mostly outperforms a uniform random selection. These results provide new insights into restoring networks by adding nodes after observing early warnings of an impending complete breakdown.