Development of a mitigation strategy against the cascading propagation of risk in R&D network

Considering severe consequences caused by cascading propagation of risk to R&D network, it is essential to develop a mitigation strategy against it for keeping network safety. Firstly, we propose the generation algorithm of R&D network based on two rules of preferential attachment. Then we develop a mitigation strategy on restoring failed firms against cascading propagation of risk, where three types of restoration methods (i.e. random restoration, high-degree restoration and high-capacity restoration) and restoration mechanism are described. Finally, we explore the effects of mitigation strategy under different values some critical parameters through numerical simulation. The simulation results show that all three restoration methods can generally enhance the robustness of R&D network, which is increasingly improved with the increasing homogeneity of firms' capacities distribution. High-capacity restoration is the most efficient one on mitigating cascading propagation of risk for any degree of capacity distribution, any proportion of restored firms and any kind of attack. But the gap between high-capacity restoration and any other restoration in the efficiency of mitigating cascading propagation of risk is increasingly less with the increasing proportion of restored firms. For any given restoration method, random attack has the least impact on the robustness of R&D network, whereas the other attacks have almost the similar impacts. This research work will provide a useful theoretical basis on building the optimal risk-mitigation strategy to keep the safety of R&D network in the real world.