Chen Chao-Yang, Tan Dingrong, Meng Xiangyi, Gao Jianxi
School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, People's Republic of China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China.
Sci Rep. 2022 Nov 14;12(1):19465. doi: 10.1038/s41598-022-23430-3.
It is essential to study the robustness and centrality of interdependent networks for building reliable interdependent systems. Here, we consider a nonlinear load-capacity cascading failure model on interdependent networks, where the initial load distribution is not random, as usually assumed, but determined by the influence of each node in the interdependent network. The node influence is measured by an automated entropy-weighted multi-attribute algorithm that takes into account both different centrality measures of nodes and the interdependence of node pairs, then averaging for not only the node itself but also its nearest neighbors and next-nearest neighbors. The resilience of interdependent networks under such a more practical and accurate setting is thoroughly investigated for various network parameters, as well as how nodes from different layers are coupled and the corresponding coupling strength. The results thereby can help better monitoring interdependent systems.
为构建可靠的相互依赖系统，研究相互依赖网络的鲁棒性和中心性至关重要。在此，我们考虑相互依赖网络上的非线性负载 - 容量级联故障模型，其中初始负载分布并非如通常所假设的那样是随机的，而是由相互依赖网络中每个节点的影响所决定。节点影响通过一种自动熵权多属性算法来衡量，该算法既考虑了节点的不同中心性度量，又考虑了节点对之间的相互依赖性，然后不仅对节点本身，还对其最近邻和次近邻进行平均。在这种更实际且准确的设定下，针对各种网络参数，以及不同层的节点如何耦合及相应的耦合强度，深入研究了相互依赖网络的弹性。由此得到的结果有助于更好地监测相互依赖系统。
