基于极大连通子图边界的复杂网络恢复研究

Research on Recovering of Complex Networks Based on Boundary Nodes of Giant Connected Component

网络恢复是应对不可避免的故障或失效的重要途径,合理的恢复策略有助于降低资源损耗且提高网络鲁棒性能。为研究复杂网络恢复动力学行为及其与网络鲁棒性之间关系,构建了基于极大连通子图边界的复杂网络恢复模型(Recovery Model of Boundary Nodes, RMBN),设计了网络平均恢复(Average Recovery of Boundary Nodes, ARBN)和择优恢复(Priority Recovery of Boundary Nodes, PRBN)策略。不同恢复策略在3种网络模型上的仿真结果表明,随着恢复比例的增大,网络鲁棒性逐渐增强且恢复作用时间更早、恢复能力更强,为复杂网络拓扑结构设计与鲁棒性优化提供借鉴。

Network recovery is an important way to solve the inevitable failure, and the reasonable recovery strategy can reduce the cost of resource and improve the network robustness. In order to study the dynamic behavior of recovery process and the relationship between recovery and network robustness, a Recovery Model of Boundary Nodes(RMBN) based on boundary of giant connected component is proposed, and two network Recovery strategies, Average Recovery of Boundary Nodes(ARBN) strategy and Priority Recovery of Boundary Nodes(PRBN) strategy are designed. The simulation results of different recovery strategies on three network models show that with the increase of recovery ratio, the network robustness is gradually enhanced, the recovery strategy takes effect earlier and the recovery capability becomes stronger, which may provide reference for the design of network topology and optimization of system robustness.