Network resilience assessment and reinforcement strategy against cascading failure

Network resilience, measuring the degree of network performance decline and recovery capacity after perturbation onset, is highly related to capability against a cascading failure. However, the network resilience assessment and reinforcement strategy remain challenging for the network with a potential cascade risk. In this paper, we propose three resilience reinforcement strategies based on the nodal capacity redundancy at the different structure scales and develop a network resilience assessment method considering both the structure and nodal load. The performance of the reinforcement strategy has a close correlation with the nodal capacity redundancy, which performs as the node with larger capacity redundancy is reinforced, the better reinforcement efficiency. Moreover, the heterogeneity of the nodal load profoundly affects the reinforcement efficiency. To enhance network resilience, the reinforcement strategies proposed are then improved based on the optimization theory. Theoretical analysis and experiments for both the Barabási-Albert scale-free network and Erdős-Rényi random network under various initial conditions demonstrate that the modified reinforcement strategy outperforms existing methods in terms of the reinforcement efficiency. This paper provides a general paradigm to address the potential cascade risk, which will enable us to design more resilient networks against cascading failures.