基于边攻击成本的复杂网络鲁棒性研究

传统的复杂网络边攻击策略有效性研究中往往忽略边的攻击成本因素，针对这一问题，文中提出了基于边攻击成本的复杂网络鲁棒性研究方法.该方法将边的攻击成本因素考虑在内，采用边的权重近似衡量边攻击成本，以最大连通子图相对值作为网络鲁棒性度量指标.在考虑边攻击成本时对合成网络和真实网络的鲁棒性进行了研究，并分析了边权重对真实网络鲁棒性的影响.实验结果表明：在边攻击成本较小时，边权由小到大策略攻击网络效果较好；对于合成网络来说，采用边权由大到小攻击策略时，BA（Barabasi-Albert）无标度网络比WS（Watts-Strogatz）小世界网络的鲁棒性强；真实复杂网络鲁棒性可以通过调节边权得到优化.与现有方法相比，利用该方法研究复杂网络鲁棒性较符合实际，仿真实验验证了所提方法的有效性和可行性.

Research on Robustness of Complex Networks with Edge's Attack Cost

The researches on the efficiency of edge attack strategies for complex networks often ignored the edge's attack cost in the past.Aiming at this problem,we propose a new method to investigate the network robustness based on edge's attack cost in this paper.This method takes the factor of edge's attack cost into account.Edge's weight is used to approximately measure the attack cost of edges.The relative size of giant connected component is used to quantify the network robustness.The robustness of synthetic datasets and real networks is investigated when the attack cost of edges is present.Moreover,the impacts of edge's weight on network robustness are discussed.The experimental results show that when the edge's attack cost is low,the attack effect of low-weight removal strategy (LW) is the best for complex networks.For synthetic datasets,BA(Barabasi-Albert) networks are more robust than WS(Watts-Strogatz) networks under high-weight removal strategy (HW).The robustness of real networks can be optimized by adjusting the edge's weights.Compared with existing methods,our method may be more practical for investigating the network robustness.Simulation results show the validity and feasibility of the method.