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Research on robustness of R&D network under cascading propagation of risk with gray attack information

机译：带有灰色攻击信息的风险级联传播下研发网络的鲁棒性研究

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摘要

Facing the cascading propagation phenomenon of risk in R&D network and the imprecision of attack information, this paper builds the cascading propagation model of risk with gray attack information. In this model, gray attack information described by node degree is measured by negative and positive deviations, and the critical threshold of resisting risk is also proposed as a new indicator of robustness of R&D network. Then the paper analyzes the robustness of R&D network under cascading propagation of risk with gray attack information through numerical simulation. The results show that R&D network has the strongest robustness under random attack, but has the weakest one under intentional attack; robustness of R&D network increases with the increase of deviation from attack information, which becomes increasingly significant when all enterprises' capacities distribution is heterogeneous; robustness of R&D network under one attack decreases with the increasing heterogeneity of all enterprises' capacities distribution; robustness of R&D network is more sensitive to the negative deviation than to the positive deviation from attack information. This research work will provide a theoretical basis for preventing and controlling cascading propagation in R&D network in the future.
机译：面对研发网络中风险的级联传播现象和攻击信息的不精确性，建立了带有灰色攻击信息的风险级联传播模型。在该模型中，用节点度表示的灰色攻击信息是通过正负偏差来衡量的，并且还提出了抵抗风险的临界阈值作为研发网络健壮性的新指标。然后通过数值仿真分析了带有灰色攻击信息的风险级联传播下R＆D网络的鲁棒性。结果表明，R＆D网络在随机攻击下的鲁棒性最强，而在故意攻击下的鲁棒性最弱。 R＆D网络的鲁棒性随着与攻击信息的偏离的增加而增加，当所有企业的容量分布是异构的时，R＆D网络的鲁棒性就变得越来越重要。随着企业能力分布异质性的提高，研发网络在一次攻击中的鲁棒性降低。 R＆D网络的健壮性对攻击信息的负偏差比对攻击信息的正偏差更敏感。这项研究工作将为今后预防和控制R＆D网络中的级联传播提供理论依据。

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