Wu Chengpei, Xu Siyi, Yu Zhuoran, Li Junli
School of Computer Science, Sichuan Normal University, Chengdu 610068, China.
Visual Computing and Virtual Reality Key Laboratory of Sichuan, Sichuan Normal University, Chengdu 610068, China.
Entropy (Basel). 2023 Jun 15;25(6):945. doi: 10.3390/e25060945.
From the perspective of network attackers, finding attack sequences that can cause significant damage to network controllability is an important task, which also helps defenders improve robustness during network constructions. Therefore, developing effective attack strategies is a key aspect of research on network controllability and its robustness. In this paper, we propose a Leaf Node Neighbor-based Attack (LNNA) strategy that can effectively disrupt the controllability of undirected networks. The LNNA strategy targets the neighbors of leaf nodes, and when there are no leaf nodes in the network, the strategy attacks the neighbors of nodes with a higher degree to produce the leaf nodes. Results from simulations on synthetic and real-world networks demonstrate the effectiveness of the proposed method. In particular, our findings suggest that removing neighbors of low-degree nodes (i.e., nodes with degree 1 or 2) can significantly reduce the controllability robustness of networks. Thus, protecting such low-degree nodes and their neighbors during network construction can lead to networks with improved controllability robustness.
从网络攻击者的角度来看,找到能够对网络可控性造成重大损害的攻击序列是一项重要任务,这也有助于防御者在网络构建过程中提高鲁棒性。因此,制定有效的攻击策略是网络可控性及其鲁棒性研究的一个关键方面。在本文中,我们提出了一种基于叶节点邻居的攻击(LNNA)策略,该策略可以有效地破坏无向网络的可控性。LNNA策略针对叶节点的邻居,当网络中没有叶节点时,该策略会攻击度数较高的节点的邻居以产生叶节点。在合成网络和真实网络上的模拟结果证明了所提方法的有效性。特别是,我们的研究结果表明,移除低度节点(即度数为1或2的节点)的邻居可以显著降低网络的可控性鲁棒性。因此,在网络构建过程中保护此类低度节点及其邻居可以得到可控性鲁棒性更高的网络。