2.793

2018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

具有遗忘群体的社会网络多维观点动力学分析与应用

刘青松 李明鹏 柴利

刘青松, 李明鹏, 柴利. 具有遗忘群体的社会网络多维观点动力学分析与应用. 自动化学报, 2021, 47(x): 1−10 doi: 10.16383/j.aas.c210091
引用本文: 刘青松, 李明鹏, 柴利. 具有遗忘群体的社会网络多维观点动力学分析与应用. 自动化学报, 2021, 47(x): 1−10 doi: 10.16383/j.aas.c210091
Liu Qing-Song, Li Ming-Peng, Chai Li. Analysis and application of multidimensional opinion dynamics on social networks with oblivion individuals. Acta Automatica Sinica, 2021, 47(x): 1−10 doi: 10.16383/j.aas.c210091
Citation: Liu Qing-Song, Li Ming-Peng, Chai Li. Analysis and application of multidimensional opinion dynamics on social networks with oblivion individuals. Acta Automatica Sinica, 2021, 47(x): 1−10 doi: 10.16383/j.aas.c210091

具有遗忘群体的社会网络多维观点动力学分析与应用

doi: 10.16383/j.aas.c210091
基金项目: 国家自然科学基金(61903282, 61625305), 中国博士后科学基金(2020T130488)资助
详细信息
    作者简介:

    刘青松:武汉科技大学信息科学与工程学院副教授. 2019年获哈尔滨工业大学控制科学与工程系博士学位. 主要研究方向为社会网络, 观点动力学分析, 时滞系统和多智能体系统. E-mail: qingsongliu@wust.edu.cn

    李明鹏:武汉科技大学信息科学与工程学院硕士研究生. 2019年获武汉科技大学信息科学与工程学院学士学位. 主要研究方向为社会网络, 观点动力学分析. E-mail: limingpeng1997@163.com

    柴利:武汉科技大学信息科学与工程学院教授. 2002年获香港科技大学电子工程系博士学位. 主要研究方向为分布式优化, 滤波器组框架, 图信号处理, 网络化控制系统. 本文通信作者. E-mail: chaili@wust.edu.cn

Analysis and Application of Multidimensional Opinion Dynamics on Social Networks with Oblivion Individuals

Funds: Supported by National Natural Science Foundation of China (61903282, 61625305) and China Postdoctoral Science Foundation (2020T130488)
More Information
    Author Bio:

    LIU Qing-Song Associate professor at the School of Information Science and Engineering, Wuhan University of Science and Technology. He received the Ph.D. degree from the Department of Control Science and Engineering at Harbin Institute of Technology in 2019. His research interests include social networks, opinion dynamics analysis, time-delay systems and multiagent systems

    LI Ming-Peng Master student at the School of Information Science and Engineering, Wuhan University of Science and Technology. He received the B.S. degree from the School of Information Science and Engineering, Wuhan University of Science and Technology in 2019. His research interests include social networks, opinion dynamics analysis

    CHAI Li Professor at the School of Information Science and Engineering, Wuhan University of Science and Technology. He received the Ph.D. degree in electrical engineering from Hong Kong University of Science and Technology in 2002. His research interests include distributed optimization, filter bank frames, graph signal processing, and networked control systems. Corresponding author of this paper

  • 摘要: 在个体观点演化过程中, 由于通讯技术和实际环境的限制, 个体之间往往不能进行充分地交流. 另一方面, 由于社会群体的从众压力影响, 个体会改变已形成的观点. 本文研究具有遗忘群体和从众压力的拟强连通社会网络中表达/私人观点演化问题. 为了刻画不同话题之间表达/私人观点的相互影响, 提出一个新的多维观点动力学模型. 根据逻辑矩阵和网络影响子矩阵的正则性, 给出了表达和私人观点收敛的充分条件. 应用本文所提出的观点动力学模型, 复现了“多元无知”的社会现象. 仿真分析表明, 从众压力的恢复力越小, 表达观点与私人观点的差异越大.
  • 图  1  本文研究框架

    Fig.  1  The research framework of this paper

    图  2  具有遗忘个体的社会网络

    Fig.  2  Social networks with oblivion individuals

    图  3  多元无知

    Fig.  3  Pluralistic ignorance

    图  4  具有遗忘个体的社会网络

    Fig.  4  Social networks with oblivion individuals

    图  5  个体1的观点(左图: $C = C_{1}$, 右图: $C = C_{2} $)

    Fig.  5  Opinions of individual 1 (Left: $C = C_{1}$, Right: $C = C_{2} $)

    图  6  个体2的观点(左图: $C = C_{1}$, 右图: $C = C_{2} $)

    Fig.  6  Opinions of individual 2 (Left: $C = C_{1}$, Right: $C = C_{2} $)

    图  7  个体3的观点(左图: $C = C_{1}$, 右图: $C = C_{2} $)

    Fig.  7  Opinions of individual 3 (Left: $C = C_{1}$, Right: $C = C_{2} $)

    图  8  个体4的观点(左图: $C = C_{1}$, 右图: $C = C_{2} $)

    Fig.  8  Opinions of individual 4 (Left: $C = C_{1}$, Right: $C = C_{2} $)

    图  9  个体5的观点(左图: $C = C_{1}$, 右图: $C = C_{2} $)

    Fig.  9  Opinions of individual 5 (Left: $C = C_{1}$, Right: $C = C_{2} $)

    图  10  个体6的观点(左图: $C = C_{1}$, 右图: $C = C_{2} $)

    Fig.  10  Opinions of individual 6 (Left: $C = C_{1}$, Right: $C = C_{2} $)

    图  11  个体7的观点(左图: $C = C_{1}$, 右图: $C = C_{2} $)

    Fig.  11  Opinions of individual 7 (Left: $C = C_{1}$, Right: $C = C_{2} $)

    图  12  观点动力学模型(17): $ C = C_{1}$

    Fig.  12  Opinion dynamics model (17) with $ C = C_{1}$

  • [1] DeGroot M H. Reaching a consensus. Journal of the American Statistical Association, 1974, 69(345): 118−121 doi: 10.1080/01621459.1974.10480137
    [2] Friedkin N E, Johnson E C. Influence networks and opinion change. Advances in Group Processes, 1999, 16(1): 1−29
    [3] 王龙, 田野, 杜金铭. 社会网络上的观念动力学. 中国科学: 信息科学, 2018, 48(1): 3−23 doi: 10.1360/N112017-00096

    Wang Long, Tian Ye, Du Jin-Ming. Opinion dynamics in social networks. Scientia Sinica: Informationis, 2018, 48(1): 3−23 doi: 10.1360/N112017-00096
    [4] Olfati-Saber R, Murray R M. Consensus problems in networks of agents with switching topology and time-delays. IEEE Transactions on Automatic Control, 2004, 49(9): 1520−1533 doi: 10.1109/TAC.2004.834113
    [5] Zhou B, Lin Z. Consensus of high-order multi-agent systems with large input and communication delays. Automatica, 2014, 50(2): 452−464 doi: 10.1016/j.automatica.2013.12.006
    [6] Liu Q, Zhou B. Consensus of discrete-time multiagent systems with state, input, and communication delays. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50(11): 4425−4437 doi: 10.1109/TSMC.2018.2852944
    [7] 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: 1−9 doi: 10.16383/j.aas.c180102

    Zhang Meng, Han Min. Finite-time synchronization between uncertain complex networks based on unidirectional coupling method. Acta Automatica Sinica, 2020, 46: 1−9 doi: 10.16383/j.aas.c180102
    [8] 王卓, 秦博东, 徐雍, 鲁仁全, 魏庆来. 复杂无向网络连通性的一种高效判定算法. 自动化学报, 2020, 46(10): 2129−2136

    Wang Zhuo, Qin Bo-Dong, Xu Yong, Lu Ren-Quan, Wei Qing-Lai. An efficient algorithm for determining the connectivity of complex undirected networks. Acta Automatica Sinica, 2020, 46(10): 2129−2136
    [9] 章联生, 金耀初, 宋永端. 时滞忆阻神经网络动力学分析与控制综述. 自动化学报, 2021, 47(4): 765−779

    Zhang Lian-Sheng, Jin Yao-Chu, Song Yong-Duan. An overview of dynamics analysis and control of memristive neural networks with delays. Acta Automatica Sinica, 2021, 47(4): 765−779
    [10] 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
    [11] Parsegov S E, Proskurnikov A V, Tempo R, Friedkin N E. A new model of opinion dynamics for social actors with multiple interdependent attitudes and prejudices. In: 54th IEEE Conference on Decision and Control, 2015, 3475−3480
    [12] Friedkin N E, Proskurnikov A V, Tempo R, Parsegov S E. Network science on belief system dynamics under logic constraints. Science, 2016, 354(6310): 321−326 doi: 10.1126/science.aag2624
    [13] Friedkin N E. The problem of social control and coordination of complex systems in sociology: A look at the community cleavage problem. IEEE Control Systems Magazine, 2015, 35(3): 40−51 doi: 10.1109/MCS.2015.2406655
    [14] Hegselmann R, Krause U. Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 2002, 5(3): 1−33
    [15] Zhang Z, Gao Y, Li Z. Consensus reaching for social network group decision making by considering leadership and bounded confidence. Knowledge-Based Systems, 2020, 204: 106240 doi: 10.1016/j.knosys.2020.106240
    [16] Parsegov S E, Proskurnikov A V, Tempo R, Friedkin N E. Novel multidimensional models of opinion dynamics in social networks. IEEE Transactions on Automatic Control, 2017, 62(5): 2270−2285 doi: 10.1109/TAC.2016.2613905
    [17] Li H J, Bu Z, Wang Z, Cao J. Dynamical clustering in electronic commerce systems via optimization and leadership expansion. IEEE Transactions on Industrial Informatics, 2020, 16(8): 5327−5334 doi: 10.1109/TII.2019.2960835
    [18] Abelson R P. Mathematical models of the distribution of attitudes under controversy. Advances in Experimental Social Psychology, 1964, 3: 1−54
    [19] Taylor, M. Towards a mathematical theory of influence and attitude change. Human Relations, 1968, 21(2): 121−139 doi: 10.1177/001872676802100202
    [20] Asch S E, Guetzkow H. Effects of group pressure upon the modification and distortion of judgments. Groups Leadership and Men, 1951: 295−303
    [21] Javarone M A. Social influences in opinion dynamics: the role of conformity. Physica A: Statistical Mechanics and its Applications, 2014, 414: 19−30 doi: 10.1016/j.physa.2014.07.018
    [22] Cheng C, Yu C. Opinion dynamics with bounded confidence and group pressure. Physica A: Statistical Mechanics and its Applications, 2019, 532: 121900 doi: 10.1016/j.physa.2019.121900
    [23] Shang Y. Resilient consensus for expressed and private opinions. IEEE Transactions on Cybernetics, 2021, 51(1): 318−331 doi: 10.1109/TCYB.2019.2939929
    [24] Ye M, Qin Y, Govaert A, Anderson B D O, Cao M. An influence network model to study discrepancies in expressed and private opinions. Automatica, 2019, 107: 371−381 doi: 10.1016/j.automatica.2019.05.059
    [25] Lin X, Jiao Q, Wang L. Opinion propagation over signed networks: models and convergence analysis. IEEE Transactions on Automatic Control, 2019, 64(8): 3431−3438 doi: 10.1109/TAC.2018.2879568
    [26] Su W, Chen G, Hong Y. Noise leads to quasi-consensus of Hegselmann-Krause opinion dynamics. Automatica, 2017, 85: 448−454 doi: 10.1016/j.automatica.2017.08.008
    [27] Proskurnikov A V, Tempo R. A tutorial on modeling and analysis of dynamic social networks. Part I. Annual Reviews in Control, 2017, 43: 65−79 doi: 10.1016/j.arcontrol.2017.03.002
    [28] Ravazzi C, Frasca P, Tempo R, Ishii H. Ergodic randomized algorithms and dynamics over networks. IEEE Transactions on Control of Network Systems, 2015, 2(1): 78−87 doi: 10.1109/TCNS.2014.2367571
    [29] Prentice D A, Miller D T. Pluralistic ignorance and alcohol use on campus: Some consequences of misperceiving the social norm. Journal of Personality and Social Psychology, 1993, 64(2): 243−256 doi: 10.1037/0022-3514.64.2.243
    [30] 杨卓璇, 马源培, 李慧嘉. 基于DEA模型的中国水行业上市企业的效率和业务类型关系研究. 聊城大学学报(自然科学版), 2020, 33(6): 12−26

    Yang Zhuo-Xuan, Ma Yuan-Pei, Li Hui-Jia. The relationship between efficiency and services types of water industry enterprises in China based on DEA model. Joural of Liaocheng University (Natural Science Edition), 2020, 33(6): 12−26
    [31] 马源培, 杨卓璇, 李慧嘉. 结合Bass模型和LTV的创新产品扩散预测. 聊城大学学报(自然科学版), 2020, 33(4): 26−32

    Ma Yuan-Pei, Yang Zhuo-Xuan, Li Hui-Jia. Innovative product diffusion forecasting combined Bass model and LTV. Joural of Liaocheng University (Natural Science Edition), 2020, 33(4): 26−32
  • 加载中
计量
  • 文章访问数:  196
  • HTML全文浏览量:  56
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-01-29
  • 录用日期:  2021-04-29
  • 网络出版日期:  2021-06-29

目录

    /

    返回文章
    返回