2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

节点分类及失效对网络能控性的影响

孔芝 袁航 王立夫 郭戈

孔芝, 袁航, 王立夫, 郭戈. 节点分类及失效对网络能控性的影响. 自动化学报, 2022, 48(4): 1048−1059 doi: 10.16383/j.aas.c200900
引用本文: 孔芝, 袁航, 王立夫, 郭戈. 节点分类及失效对网络能控性的影响. 自动化学报, 2022, 48(4): 1048−1059 doi: 10.16383/j.aas.c200900
Kong Zhi, Yuan Hang, Wang Li-Fu, Guo Ge. Node classification and the influence of node failure on network controllability. Acta Automatica Sinica, 2022, 48(4): 1048−1059 doi: 10.16383/j.aas.c200900
Citation: Kong Zhi, Yuan Hang, Wang Li-Fu, Guo Ge. Node classification and the influence of node failure on network controllability. Acta Automatica Sinica, 2022, 48(4): 1048−1059 doi: 10.16383/j.aas.c200900

节点分类及失效对网络能控性的影响

doi: 10.16383/j.aas.c200900
基金项目: 国家自然科学基金(61573077, U1808205), 中央高校基本科研业务费专项基金(N2023022)资助
详细信息
    作者简介:

    孔芝:东北大学秦皇岛分校副教授. 研究方向为知识发现, 决策分析, 智能优化算法和复杂网络. E-mail: kongz@neuq.edu.cn

    袁航:东北大学秦皇岛分校硕士研究生. 研究方向为复杂网络能控性. E-mail: yuanhang951115@163.com

    王立夫:东北大学秦皇岛分校副教授. 研究方向为复杂网络, 同步控制, 能控性和交通网络. 本文通信作者. E-mail: wlfkz@qq.com

    郭戈:东北大学秦皇岛分校教授. 研究方向为智能交通系统, 交通大数据分析, 人工智能应用和信息物理系统. E-mail: geguo@yeah.net

Node Classification and the Influence of Node Failure on Network Controllability

Funds: Supported by National Natural Science Foundation of China (61573077, U1808205), Fundamental Research Funds for the Central Universities (N2023022)
More Information
    Author Bio:

    KONG Zhi Associate professor at Northeastern University at Qinhuangdao. Her research interest covers knowledge discovery, decision analysis, intelligent optimization algorithms, and complex networks

    YUAN Hang Master student at Northeastern University at Qinhuangdao. His research interest covers controllability of complex networks

    WANG Li-Fu Associate professor at Northeastern University at Qinhuangdao. His research interest covers complex networks, synchronous control, controllability, and traffic networks. Corresponding author of this paper

    GUO Ge Professor at Northeastern University at Qinhuangdao. His research interest covers intelligent transportation systems, traffic big data analysis, artificial intelligence applications, and information physical systems

  • 摘要: 复杂系统间的相互作用能够用复杂网络描述. 复杂网络中某些节点遭受攻击或破坏会造成网络故障, 导致整个网络能控性变化. 不同节点失效会对网络能控性有不同的影响. 本文提出一种网络节点的分类方式, 将网络中的节点根据边的方向和匹配关系分成九种类型, 并给出了辨识节点类型的算法. 另外, 本文给出了基于此分类方式下复杂网络中某类节点失效时, 网络中驱动节点数量(用来衡量网络能控性大小的指标)的变化规律. 并通过模型网络进行仿真实验, 验证了当节点失效时本文给出的驱动节点数量变化情况, 同时还分析社交网络中不同类型节点的占比与实际中人际交往的对应关系.
  • 图  1  有向图和二分图的匹配

    Fig.  1  Matching of directed graph and bipartite graph

    图  2  节点与边的四种关系

    Fig.  2  Four relations between nodes and edges

    图  3  节点分类

    Fig.  3  Node classification

    图  4  算法流程图

    Fig.  4  Algorithm flow chart

    图  5  ER网络$V_{I}$$V_{O}$失效可控性变化

    Fig.  5  Controllability changes of $V_{I}$ and $V_{O}$ failure in ER networks

    图  6  BA网络$V_{I}$$V_{O}$失效可控性变化

    Fig.  6  Controllability changes of $V_{I}$ and $V_{O}$ failure in BA networks

    图  7  WS网络$V_{I}$$V_{O}$失效可控性变化

    Fig.  7  Controllability changes of $V_{I}$ and $V_{O}$ failure in WS networks

    图  8  ER网络$V_{IO}$失效可控性变化

    Fig.  8  Controllability changes of $V_{IO}$ f ailure in ER networks

    图  9  BA网络$V_{IO}$失效可控性变化

    Fig.  9  Controllability changes of $V_{IO}$ failure in BA networks

    图  10  WS网络$V_{IO}$失效可控性变化

    Fig.  10  Controllability changes of $V_{IO}$ failure in WS networks

    图  11  社交网络节点失效能控性变化

    Fig.  11  Controllability changes of node failure in social networks

    表  1  模型网络不同类型节点占比表

    Table  1  Proportion of different types of nodes in the model network

    ER网络BA网络WS网络
    节点数500500500
    边数248519681000
    INM27250
    IM24430
    ONM244610
    OM31392
    INM&ONM272266358
    INM&OM5148105
    IM&ONM423320
    IM&OM3205
    $ V_{D}$000
    下载: 导出CSV

    表  2  实际网络不同类型节点占比表

    Table  2  Proportion of different types of nodes in the actual network

    经理社交网络律师社交网络银行员工社交网络 (1)银行员工社交网络 (2)银行员工社交网络 (3)学生社交网络 (1)学生社交网络 (2)
    节点21711111117373
    190892305127243263
    $\langle k \rangle$9.0512.562.734.642.543.333.60
    INM0112111
    IM3000000
    ONM0120044
    OM0010122
    INM&ONM18673834447
    INM&OM021031310
    IM&ONM0030235
    IM&OM0000130
    $V_{D} $0000034
    下载: 导出CSV
  • [1] Watts D J. The “new” science of networks. Annual Review of Sociology, 2004, 30(1): 243-270 doi: 10.1146/annurev.soc.30.020404.104342
    [2] Yan G, Vértes P E, Towlson E K, Chew Y L, Walker D s, Schafer W R. Network control principles predict neuron function in the caenorhabditis elegans connectome. Nature, 2017, 550(7677): 519-523 doi: 10.1038/nature24056
    [3] Zhang Z K, Liu C, Zhan X X, Lu X, Zhang C X, Zhang Y C. Dynamics of information diffusion and its applications on complex networks. Physics Reports, 2016, 651(1): 1-34
    [4] Urena R, Kou G, Dong Y C, Chiclana F, Herrera-Viedma E. A review on trust propagation and opinion dynamics in social networks and group decision making frameworks. Information Sciences, 2019, 478(1): 461-475
    [5] 任卓明. 动态复杂网络节点影响力的研究进展. 物理学报, 2020, 69(4): 048901 doi: 10.7498/aps.69.20190830

    Ren Zhuo-Ming. Research progress of node influence in dynamic complex networks. Acta Physica Sinica, 2020, 69(4): 048901 doi: 10.7498/aps.69.20190830
    [6] Li A, Cornelius S P, Liu Y Y, Wang L, Barabási A L. The fundamental advantages of temporal networks. Science, 2017, 358(6366): 1042-1046 doi: 10.1126/science.aai7488
    [7] Gao J X, Liu Y Y, D'Souza R M, Barabási A L. Target control of complex networks. Nature Communications, 2014, 5(1): 5415-5422 doi: 10.1038/ncomms6415
    [8] Wang X F, Chen G R. Pinning control of scale-free dynamical networks. Physica A, 2002, 310(3): 521-531
    [9] Li X, Wang X F, Chen G R. Pinning a complex dynamical network to its equilibrium. IEEE Transactions on Circuits and Systems I: Regular Papers, 2004, 51(10): 2074-2087 doi: 10.1109/TCSI.2004.835655
    [10] Liu X W, Chen T P. Cluster synchronization in directed networks via intermittent pinning control. IEEE Transactions on Neural Networks, 2011, 22(7): 1009-1020 doi: 10.1109/TNN.2011.2139224
    [11] Liu X W, Chen T P. Finite-time and fixed-time cluster synchronization with or without pinning control. IEEE Transactions on Cybernetics, 2018, 48(1): 240-252 doi: 10.1109/TCYB.2016.2630703
    [12] Yu W W, DeLellis P, Chen G R. Distributed adaptive control of synchronization in complex networks. IEEE Transactions on Automatic Control, 2012, 57(8): 2153-2158 doi: 10.1109/TAC.2012.2183190
    [13] Su H S, Rong Z H, Chen M Z. Decentralized adaptive pinning control for cluster synchronization of complex dynamical networks. IEEE Transactions on Cybernetics, 2013, 43(1): 394-399 doi: 10.1109/TSMCB.2012.2202647
    [14] 杨青林, 王立夫, 李欢, 余牧舟. 基于相对距离的复杂网络谱粗粒化方法. 物理学报, 2019, 68(10): 100501 doi: 10.7498/aps.68.20181848

    Yang Qing-Lin, Wang Li-Fu, Li Huan, Yu Mu-Zhou. A spectral coarse-graining algorithm based on relative distance. Acta Physica Sinica, 2019, 68(10): 100501 doi: 10.7498/aps.68.20181848
    [15] 王振华, 刘宗华. 复杂网络上的部分同步化: 奇异态、遥同步与集团同步. 物理学报, 2020, 69(8): 088902 doi: 10.7498/aps.69.20191973

    Wang Zhen-Hua, Liu Zong-Hua. Partial synchronization on complex networks: singular state, remote synchronization and group synchronization. Acta Physica Sinica, 2020, 69(8): 088902 doi: 10.7498/aps.69.20191973
    [16] Ren H R, Karimi H R, Lu R Q, Wu Y Q. Synchronization of network systems via aperiodic sampled-data control with constant delay and application to unmanned ground vehicles. IEEE Transactions on Industrial Electronics, 2020, 67(6): 4980-4990 doi: 10.1109/TIE.2019.2928241
    [17] 张檬, 韩敏. 基于单向耦合法的不确定复杂网络间有限时间同步. 自动化学报, 2020, 46(x): 1-9

    Zhang Meng, Han Min. Finite-time synchronization between uncertain complex networks based on unidirectional coupling method. Acta Automatica Sinica, 2020, 46(x): 1-9
    [18] 潘永昊, 于洪涛. 基于网络同步的链路预测连边机理分析研究. 自动化学报, 2020, 46(12): 2607-2616

    Pan Yong-Hao, Yu Hong-Tao. Analysis of linkage mechanism of link prediction based on network synchronization. Acta Automatica Sinica, 2020, 46(12): 2607-2616
    [19] 郭天姣, 涂俐兰. 噪声下相互依存网络的自适应$ H_{\infty}$异质同步. 自动化学报, 2020, 46(6): 1229-1239

    GUO Tian-Jiao, TU Li-Lan. Adaptive $ H_{\infty}$ Heterogeneous Synchronization for 0.3 nterdependent Networks With Noise. Acta Automatica Sinica, 2020, 46(6): 1229-1239
    [20] Ren H R, Lu R Q, Xiong J L, Wu Y Q, Shi P. Optimal filtered and smoothed estimators for discrete-time linear systems with multiple packet dropouts under markovian communication constraints. IEEE Transactions on Cybernetics, 2020, 50(9): 4169-4180 doi: 10.1109/TCYB.2019.2924485
    [21] Liu Y Y, Slotine J J, Barabási A L. Controllability of complex networks. Nature, 2011, 473(7346): 167-173 doi: 10.1038/nature10011
    [22] Yuan Z, Zhao C, Di Z, Wang W X, Lai Y C. Exact controllability of complex networks. Nature Communications, 2013, 4(1): 2447-2455 doi: 10.1038/ncomms3447
    [23] Posfai M, Hovel P. Structural controllability of temporal networks. New Journal of Physics, 2014, 16(12): 123055 doi: 10.1088/1367-2630/16/12/123055
    [24] Wu J N, Li X, Chen G R. Controllability of deep-coupling dynamical networks. IEEE Transactions on Circuits and Systems I: Regular Papers, 2020, 67(12): 1-12 doi: 10.1109/TCSI.2020.3033927
    [25] Tommaso M, Danielle S. Bassett, Fabio P. Structural controllability of symmetric networks IEEE Transactions on Automatic Control, 2019, 64(9): 3740-3747 doi: 10.1109/TAC.2018.2881112
    [26] Sun C, Hu G Q, Xie L H. Controllability of multi-agent networks with antagonistic interactions. IEEE Transactions on Automatic Control, 2017, 62(10): 5457-5462 doi: 10.1109/TAC.2017.2697202
    [27] Posfai M, Gao J, Cornelius S P, Barabási A L. Controllability of multiplex multi-time-scale networks. Physical Review E, 2016, 94(3): 032316
    [28] Pu C L, Pei W J, Michaelson A. Robustness analysis of network controllability. Physica A, 2012, 391(18): 4420-4425 doi: 10.1016/j.physa.2012.04.019
    [29] Liu Y Y, Slotine J J, Barabási A L. Control centrality and hierarchical structure in complex networks. PloS One, 2012, 7(9): e44459 doi: 10.1371/journal.pone.0044459
    [30] Lu Z M, Li X F. Attack vulnerability of network controllability. PloS One, 2016, 11(9): e0162289 doi: 10.1371/journal.pone.0162289
    [31] Pu C L, Cui W. Vulnerability of complex networks under path-based attacks. Physica A: Statistical Mechanics and its Applications, 2015, 419(1): 622-629
    [32] Hopcroft J E, Karp R M. An $ n.{5/2}$ algorithm for maximum matchings in bipartite graphs. SIAM Journal on Computing, 1971, 2(4): 122-125
    [33] Erdos P, Renyi A. On random graphs. Publicationes Mathematicae, 1959, 6(1): 290-297
    [34] Barabási A L, Albert R. Emergence of scaling in random networks. Science, 1999, 286(5439): 509-512 doi: 10.1126/science.286.5439.509
    [35] Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks. Nature, 1998, 393(6684): 440-442 doi: 10.1038/30918
    [36] Coleman J S. Introduction to mathermatical sociology[Online], available: http://moreno.ss.uci.edu/data.html, September 10, 2020
  • 加载中
图(11) / 表(2)
计量
  • 文章访问数:  1521
  • HTML全文浏览量:  503
  • PDF下载量:  212
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-11-02
  • 录用日期:  2021-01-15
  • 网络出版日期:  2021-03-02
  • 刊出日期:  2022-04-13

目录

    /

    返回文章
    返回