Immunization strategy based on discrete particle swarm optimization algorithm in BBV network

Yang Min, Zhang Jiayue, Zhang Damin
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引用次数: 3

Abstract

It is a key that how to find the most important nodes and to immune them in network. The maximum numbers and degree centrality of nodes are funded by The DPSO (discrete particle swarm optimization) strategy, the network is cut as small as possible, and then the infected nodes are limited in sub network to prevent virus propagation. Whereas due to the loss information of expressing nodes nature of strength in weighted network, we combine the maximum numbers and semi-local centrality of nodes, to identify nodes and cut the network. We compare the efficiency of those methods in weighted network, simulation results show that the method can restrain virus propagation effectively.
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基于离散粒子群优化算法的BBV网络免疫策略
如何在网络中找到最重要的节点并使其免疫是一个关键问题。采用离散粒子群优化(DPSO)策略确定节点的最大数量和中心度,将网络切割得尽可能小,然后将感染节点限制在子网络中,以防止病毒传播。然而,由于加权网络中表达节点强度性质的信息存在损失,我们将节点的最大数量和半局部中心性结合起来,进行节点识别和网络切割。在加权网络中比较了这些方法的效率,仿真结果表明该方法能有效地抑制病毒的传播。
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