中国安全科学学报 ›› 2022, Vol. 32 ›› Issue (8): 91-97.doi: 10.16265/j.cnki.issn1003-3033.2022.08.2624

• 安全工程技术 • 上一篇    下一篇

新冠疫情前后机场加权网络抗毁性分析*

郭九霞(), 杨宗鑫, 夏正洪, 唐卫贞   

  1. 中国民用航空飞行学院 空中交通管理学院,四川 广汉618307
  • 收稿日期:2022-02-17 修回日期:2022-05-13 出版日期:2022-09-05 发布日期:2023-02-28
  • 作者简介:

    郭九霞 (1981—),女,山西安泽人,博士,副教授,主要从事复杂系统韧性、空中交通管理等方面的研究。E-mail:

    夏正洪,副教授。

    唐卫贞,教授。

  • 基金资助:
    民航局安全能力建设资金资助((2021)130号); 中国高校产学研创新基金资助(2021ALA02025); 中国高校产学合作协同育人基金资助(KJ-XTYR-09018); 民航局教育人才类项目(MHJY2022044)

Invulnerability analysis for airport weighted networks before and after COVID-19

GUO Jiuxia(), YANG Zongxin, XIA Zhenghong, TANG Weizhen   

  1. School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China
  • Received:2022-02-17 Revised:2022-05-13 Online:2022-09-05 Published:2023-02-28

摘要:

为提升机场网络应对突发事件的能力,基于复杂网络,分析新冠疫情(COVID-19)前后我国机场网络的拓扑特性;利用节点强度加权处理机场网络,识别不同攻击策略下加权网络特征指标损失拟合曲线拐点,提出网络抗毁性评估方法。结果表明:疫情前后机场加权网络拓扑结构未发生明显变化,但连通程度略显稀疏;我国机场网络在不同蓄意攻击策略下抗毁性较差,当攻击比例达8.6%时,出现损失拟合曲线拐点,全局网络效率相对损失达24.39%,最大联通子图缩减率达14.67%,平均度相对损失达76.87%,平均聚类系数相对损失达68.84%;拐点之后,网络效率、最大联通子图缩减损失均加快,网络处于瘫痪状态。

关键词: 新冠疫情(COVID-19), 机场加权网络, 抗毁性, 损失曲线, 拐点

Abstract:

In order to improve airport network's ability to cope with emergencies, topological characteristics of Chinese airport network before and after COVID-19 were analyzed based on complex network theory. And the network was weighted by using node strength, and an invulnerability assessment method was developed after identifying inflection points of loss fitting curves for weighted network characteristics metrics under different attack strategies. The results show that the topological structure of airport weighted network has no significant changes before and after the pandemic, but its connectivity is slightly sparse. And the airport network in China is much more vulnerable under different intentional attack strategies. When attack ratio reaches 8.6%, inflection point of loss fitting curves will appear, and relative loss of global network efficiency will amount to 24.39%, while reduction rate of the largest connected subgraph reaches 14.67%, and relative loss of average degree and average clustering coefficient is up to 76.87% and 68.84%, respectively. Moreover, loss of network efficiency and the largest connected subgraph reduction rate accelerates after inflection points, in which stage the network will be paralyzed.

Key words: corona virus disease 2019 (COVID-19), airport weighted network, invulnerability, loss curve, inflection point