IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i24p4688-d999630.html
   My bibliography  Save this article

Information Spreading Considering Repeated Judgment with Non-Recursion

Author

Listed:
  • Yufang Fu

    (School of Economics & Management, Zhejiang Ocean University, Zhoushan 316022, China
    These authors contributed equally to this work and should be regarded as co-first authors.)

  • Bin Cao

    (School of Management, Xi’an University of Architecture & Technology, Xi’an 710055, China
    These authors contributed equally to this work and should be regarded as co-first authors.)

  • Wei Zhang

    (School of Management, Xi’an University of Architecture & Technology, Xi’an 710055, China)

  • Zongwei Luo

    (BNU-UIC Institute of AI and Future Networks, Beijing Normal University, Zhuhai 519000, China
    Faculty of Science and Technology, Hong Kong Baptist University-Beijing Normal University United International College, Zhuhai 519000, China)

Abstract

This paper investigates an information spreading mechanism under repeated judgment. In a generalized model, we prove that given a necessary condition, information under repeated judgment can sustain continuous spreading. Furthermore, we generalize the aforementioned spreading model on heterogeneous networks and calculate the analytic solution of the final state, in which spreaders finally have a stable scale to ensure that information can continuously spread when repeated judgment of information takes place. Moreover, the simulation results show that the more neighbors the spreaders have, the quicker the information vanishes. This finding suggests that in terms of information spreading under repeated judgement, it is not better to have more neighbors, quite contrary to common opinion.

Suggested Citation

  • Yufang Fu & Bin Cao & Wei Zhang & Zongwei Luo, 2022. "Information Spreading Considering Repeated Judgment with Non-Recursion," Mathematics, MDPI, vol. 10(24), pages 1-16, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4688-:d:999630
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/24/4688/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/24/4688/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cao, Bin & Han, Shui-hua & Jin, Zhen, 2016. "Modeling of knowledge transmission by considering the level of forgetfulness in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 277-287.
    2. Yang, Dingda & Liao, Xiangwen & Shen, Huawei & Cheng, Xueqi & Chen, Guolong, 2018. "Modeling the reemergence of information diffusion in social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1493-1500.
    3. Bin Cao & Qingyu Zhang & Mei Cao, 2022. "Optimizing Hybrid-Channel Supply Chains with Promotional Effort and Differential Product Quality: A Game-Theoretic Analysis," Mathematics, MDPI, vol. 10(11), pages 1-21, May.
    4. Kawamoto, Tatsuro & Hatano, Naomichi, 2014. "Viral spreading of daily information in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 34-41.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huo, Liang’an & Chen, Sijing, 2020. "Rumor propagation model with consideration of scientific knowledge level and social reinforcement in heterogeneous network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    2. Xu, Jinghong & Du, Zhitao & Guo, Jianchao & Fu, Xiangling & Zhang, Yuqiang & Wu, Ye, 2018. "Empirical and modeling studies of WeChat information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1113-1120.
    3. Huo, Liang’an & Song, Naixiang, 2016. "Dynamical interplay between the dissemination of scientific knowledge and rumor spreading in emergency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 73-84.
    4. Wang, Sixin & Mei, Jun & Xia, Dan & Yang, Zhanying & Hu, Junhao, 2022. "Finite-time optimal feedback control mechanism for knowledge transmission in complex networks via model predictive control," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    5. Wang, Haiying & Wang, Jun & Small, Michael & Moore, Jack Murdoch, 2019. "Review mechanism promotes knowledge transmission in complex networks," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 113-125.
    6. Zhu, He & Ma, Jing, 2018. "Knowledge diffusion in complex networks by considering time-varying information channels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 225-235.
    7. Xiaodan Kong & Qi Xu & Tao Zhu, 2019. "Dynamic Evolution of Knowledge Sharing Behavior among Enterprises in the Cluster Innovation Network Based on Evolutionary Game Theory," Sustainability, MDPI, vol. 12(1), pages 1-23, December.
    8. Shuiwang Zhang & Jingcheng Ding & Qianlan Ding, 2023. "Retailer-Led Low-Carbon Supply Chain Coordination Considering Sales Effort," Sustainability, MDPI, vol. 15(13), pages 1-24, July.
    9. Zhu, Linhe & Liu, Mengxue & Li, Yimin, 2019. "The dynamics analysis of a rumor propagation model in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 118-137.
    10. Wang, Haiying & Wang, Jun & Small, Michael, 2018. "Knowledge transmission model with differing initial transmission and retransmission process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 478-488.
    11. Mei, Jun & Wang, Sixin & Xia, Dan & Hu, Junhao, 2022. "Global stability and optimal control analysis of a knowledge transmission model in multilayer networks," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    12. Chołoniewski, Jan & Sienkiewicz, Julian & Leban, Gregor & Hołyst, Janusz A., 2019. "Modeling of temporal fluctuation scaling in online news network with independent cascade model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 129-144.
    13. Wang, Zhixiao & Rui, Xiaobin & Yuan, Guan & Cui, Jingjing & Hadzibeganovic, Tarik, 2021. "Endemic information-contagion outbreaks in complex networks with potential spreaders based recurrent-state transmission dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    14. Shuihua Han & Bin Cao & Yufang Fu & Zongwei Luo, 2018. "A liner shipping competitive model with consideration of service quality management," Annals of Operations Research, Springer, vol. 270(1), pages 155-177, November.
    15. Wang, Zhiping & Yin, Haofei & Jiang, Xin, 2020. "Exploring the dynamic growth mechanism of social networks using evolutionary hypergraph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 544(C).
    16. Liao, Shi-Gen & Yi, Shu-Ping, 2021. "Modeling and analyzing knowledge transmission process considering free-riding behavior of knowledge acquisition: A waterborne disease approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    17. Liao, Shi-Gen & Yi, Shu-Ping, 2021. "Modeling and analysis knowledge transmission process in complex networks by considering internalization mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    18. Hongying Xiao & Zhaofeng Li & Yuanyuan Zhang & Hong Lin & Yuxiao Zhao, 2023. "A Dual Rumor Spreading Model with Consideration of Fans versus Ordinary People," Mathematics, MDPI, vol. 11(13), pages 1-14, July.
    19. Wang, Haiying & Wang, Jun & Ding, Liting & Wei, Wei, 2017. "Knowledge transmission model with consideration of self-learning mechanism in complex networks," Applied Mathematics and Computation, Elsevier, vol. 304(C), pages 83-92.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4688-:d:999630. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.