Please wait a minute...
文章检索
复杂系统与复杂性科学  2023, Vol. 20 Issue (4): 18-25    DOI: 10.13306/j.1672-3813.2023.04.003
  本期目录 | 过刊浏览 | 高级检索 |
基于局部熵的级联故障模型初始负载定义方式
董昂, 吴亚丽, 任远光, 冯梦琦
西安理工大学 a.自动化与信息工程学院;b.陕西省复杂系统控制与智能信息处理重点实验室,西安 710048
Initial Load Definition of Cascading Failure Model Based on Local Entropy
DONG Ang, WU Yali, REN Yuanguang, FENG Mengqi
a. School of Automation and Information Engineering; b. Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an 710048, China
全文: PDF(3889 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 复杂网络中失效节点或边产生的级联效应,严重时将导致整个网络瘫痪。初始负载的定义方式对级联故障模型分析具有重要影响。综合考虑采用度定义缺乏拓扑信息及介数定义计算量过大,充分利用网络的拓扑信息和节点的邻域信息,首次提出了基于局部熵的初始负载定义方式。典型的复杂网络级联故障模型实例,验证了所提局部熵定义的有效性。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
董昂
吴亚丽
任远光
冯梦琦
董昂
吴亚丽
任远光
冯梦琦
关键词 复杂网络级联故障局部熵初始负载    
Abstract:Cascading failures caused by failed nodes or edges in the network may lead to the paralysis of the whole network. The definition of initial load has a very important impact on the analysis of cascading failure model. In this paper, considering the lack of topological information brought by degree and the huge computation brought by betweenness centrality, we make full use of the topological information as well as neighbors’ information of nodes and firstly propose the initial load definition based on local entropy. Some typical examples about cascading failures in the complex networks demonstrate the effectiveness of proposed definition based on local entropy.
Key wordscomplex networks    cascading failures    local entropy    initial load
收稿日期: 2022-08-20      出版日期: 2023-12-28
:  TP391.9  
基金资助:陕西省重点研发计划(2021JM-343)
通讯作者: 吴亚丽(1975-),女,山西运城人,博士,教授,主要研究方向为智能优化算法理论与应用、复杂系统建模与优化。   
作者简介: 董昂(1999-),男,陕西咸阳人,硕士研究生,主要研究方向为复杂网络。
引用本文:   
董昂, 吴亚丽, 任远光, 冯梦琦. 基于局部熵的级联故障模型初始负载定义方式[J]. 复杂系统与复杂性科学, 2023, 20(4): 18-25.
DONG Ang, WU Yali, REN Yuanguang, FENG Mengqi. Initial Load Definition of Cascading Failure Model Based on Local Entropy[J]. Complex Systems and Complexity Science, 2023, 20(4): 18-25.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2023.04.003      或      https://fzkx.qdu.edu.cn/CN/Y2023/V20/I4/18
[1] 窦炳琳,张世永.复杂网络上级联失效的负载容量模型[J].系统仿真学报,2011,23(7):1459-1463,1468.
DOU B L, ZHANG S Y. Load-capacity model for cascading failures of complex networks[J]. Journal of System Simulation,2011,23(7):1459-1463,1468.
[2] 种鹏云,尹惠.蓄意攻击策略下危险品运输网络级联失效仿真[J].复杂系统与复杂性科学,2018,15(1):45-55,74.
ZHONG P Y, YIN H. Simulation of cascading failures on hazardous materials transportation network under targeted attack[J]. Complex Systems And Complexity Science,2018,15(1):45-55,74.
[3] 张强,曹军海,宋太亮,等.基于SIRV病毒传播理论的装备保障网络级联失效分析[J].系统仿真学报,2020,32(10):1847-1853.
ZHANG Q, CAO J H, SONG T L, et al. Cascading failure analysis of equipment support network based on SIRV virus propagation theory[J]. Journal of System Simulation,2020,32(10):1847-1853.
[4] 郭明健,高岩.基于复杂网络理论的电力网络抗毁性分析[J/OL].复杂系统与复杂性科学,2022,19(4):1-6.
GUO M J, GAO Y. Invulnerability analysis of power network based on complex networks[J/OL]. Complex Systems and Complexity Science,2022,19(4):1-6.
[5] 丁琳,张嗣瀛.基于局域路由的复杂通信网络级联动力学建模[J].复杂系统与复杂性科学,2014,11(3):79-85.
DING L, ZHANG S Y. Cascading dynamics model for complex communication networks based on local routing[J]. Complex Systems And Complexity Science,2014,11(3):79-85.
[6] 王建伟,荣莉莉,王铎.基于节点局域特征的复杂网络上相继故障模型[J].管理科学学报,2010,13(8):42-50.
WANG J W, RONG L L, WANG D. Cascading failures model on complex networks based on local characteristics of nodes[J]. Journal of Management Science in China,2010,13(8):42-50.
[7] 吴祯涛,李学仁,杜军,等.多社团加权复杂网络建模及其级联抗毁性研究[J].系统科学与数学,2019,39(11):1729-1740.
WU Z T, LI X R, DU J, et al. Research on modeling and cascading invulnerability of weighted complex networks with community structure[J]. Journal of Systems Science and Mathematical Sciences,2019,39(11):1729-1740.
[8] MOTTER A E, LAI Y C. Cascade-based attacks on complex networks[J]. Physical Review E,2002,66(6):065102.
[9] 董崇杰.全局分配策略在级联故障中的建模与研究[J].系统仿真学报,2018,30(11):4172-4179.
DONG C J. Modeling and research of global allocation policy in cascading failures[J]. Journal of System Simulation,2018,30(11):4172-4179.
[10] DOBSON I, CARRERAS B A, LYNCH V E, et al. Complex systems analysis of series of blackouts: cascading failure, critical points, and self-organization[J]. Chaos,2007,17(2):967-979.
[11] WANG J W, RONG L L. Cascading failures in barabási–albert scale-free networks with a breakdown probability[J]. International Journal of Modern Physics C,2009,20(4):585-595.
[12] WANG J, RONG L, ZHANG L. A new cascading model on scale-free network with tunable parameter[C]. First International Conference on Intelligent Networks and Intelligent Systems. Wuhan,China:IEEE,2008:321-324.
[13] WANG J, RONG L, ZHANG L, et al. Attack vulnerability of scale-free networks due to cascading failures[J]. Physica A: Statistical Mechanics and its Applications,2008,387(26):6671-6678.
[14] RASHEVSKY N. Life, information theory, and topology[J]. Bulletin of Mathematical Biology,1955,17(3):229-235
[15] TRUCCO E. A Note on the information content of graphs[J]. Bulletin of Mathematical Biology,1956,18(2):129-135.
[16] DEHMER M. Information Processing in complex networks: graph entropy and information functionals[J]. Applied Mathematics and Computation,2008,201(1/2):82-94.
[17] QIAO T, SHAN W, CHANG Z. How to identify the most powerful node in complex networks? a novel entropy centrality approach[J]. Entropy,2017,19(11):614.
[18] NIE T Y, GUO Z, ZHAO K, et al. Using mapping entropy to identify node centrality in complex networks[J]. Physica A,2016,453:290-297.
[19] GUO C, YANG L, CHEN X, et al. Influential nodes identification in complex networks via information entropy[J]. Entropy,2020,22(2):242.
[20] 韩丽,刘彬,邓玉静,等.加权无标度网络的级联失效模型[J].软件学报,2017,28(10):2769-2781.
HAN L, LIU B, DENG Y J, et al. Cascading failure model of weighted scale-free networks[J]. Journal of Software,2017,28(10): 2769-2781.
[21] BING W, KIM B J. A high robustness and low-cost model for cascading failures[J]. Europhysics Letters,2007, 78(4):48001.
[1] 聂廷远, 王艳伟, 聂晶晶, 刘鹏飞. 基于注意力机制和复杂网络的FPGA可布性预测[J]. 复杂系统与复杂性科学, 2026, 23(1): 53-59.
[2] 户佐安, 杨江浩, 邓锦程. 考虑多元变量的世界航空网络综合鲁棒性研究[J]. 复杂系统与复杂性科学, 2026, 23(1): 60-69.
[3] 胡金梅, 邹艳丽, 王鸿俊, 张海. 基于二阶邻居负载再分配的电网级联故障研究[J]. 复杂系统与复杂性科学, 2026, 23(1): 1-9.
[4] 孙小慧, 刘毅, 米玉梅, 吕凯. 韧性视角下城市地铁与常规公交网络关键站点及线路识别[J]. 复杂系统与复杂性科学, 2026, 23(1): 26-36.
[5] 牟奇锋, 李晓倩. 基于邻接矩阵的复杂网络演化融合迭代方法[J]. 复杂系统与复杂性科学, 2026, 23(1): 79-86.
[6] 孙文静, 余路粉, 潘文林, 蓝春江. 基于节点影响因子和贡献因子的复杂网络重要节点识别[J]. 复杂系统与复杂性科学, 2026, 23(1): 87-95.
[7] 卢新彪, 刘泽诚, 陈贵允, 杨铁流, 高兴. 基于图卷积网络的复杂网络能控性提升方法[J]. 复杂系统与复杂性科学, 2025, 22(4): 24-28.
[8] 周青, 李依函, 陈文冲. “互联网+”企业创新生态系统网络演化分析[J]. 复杂系统与复杂性科学, 2025, 22(4): 1-7.
[9] 余文倩, 马福祥, 陈阳, 马秀娟. 基于自适应的高阶网络鲁棒性分析[J]. 复杂系统与复杂性科学, 2025, 22(4): 15-23.
[10] 章浩淳, 寇博潇, 张泰杰, 唐智慧. 基于Granger Causality的滑坡机理网络客观权值确定方法[J]. 复杂系统与复杂性科学, 2025, 22(4): 63-70.
[11] 韩世翔, 闫光辉, 裴华艳. 复杂网络上双向免疫对传染病传播的影响[J]. 复杂系统与复杂性科学, 2025, 22(4): 55-62.
[12] 张琦, 汪小帆. 复杂网络观点动力学分析与干预若干研究进展[J]. 复杂系统与复杂性科学, 2025, 22(2): 31-44.
[13] 张明磊, 宋玉蓉, 曲鸿博. 基于图注意力机制的复杂网络关键节点识别[J]. 复杂系统与复杂性科学, 2025, 22(2): 113-119.
[14] 陶昭, 侯忠生. 复杂网络的无模型自适应牵制控制[J]. 复杂系统与复杂性科学, 2025, 22(2): 120-127.
[15] 李伟莎, 王淑良, 宋博. 基于强化学习风电并网策略下的韧性分析[J]. 复杂系统与复杂性科学, 2025, 22(2): 128-134.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed