IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v499y2018icp276-287.html
   My bibliography  Save this article

Rumor diffusion in heterogeneous networks by considering the individuals’ subjective judgment and diverse characteristics

Author

Listed:
  • Ma, Jing
  • Zhu, He

Abstract

In this study, we propose a novel rumor spreading model in consideration of the individuals’ subjective judgment and diverse characteristics. To reflect the diversity of the individuals’ characteristics, we introduce two probability distribution functions, which could be chosen arbitrarily or given by empirical data, to characterize individuals’ mastering degree of knowledge with respect to the domain of a specific rumor and individuals’ rationality degree. Different from existing models, no two persons in our model are identical, and each individual can judge the authenticity of the information, e.g., rumors, with his distinctive characteristics. In addition, by means of the mean-field method, we establish the expression of the dynamics of the rumor propagation in the complex heterogeneous networks and derive the rumor spreading threshold. Through the theoretical analysis, we find that the threshold is independent of the forms of the two introduced functions. Furthermore, we prove the stability of the rumor-free equilibrium set E0. That is if and only if R0<1, the rumor-free equilibrium set E0 is globally asymptotically stable. Finally, we conduct a series of numerical simulations to verify the theoretical results and comprehensively illustrate the evolution of the model. The simulation results show that because of the diversity of individuals’ characteristics, it becomes more difficult for the rumor to disseminate in the networks and the higher the mean of knowledge and the mean of rationality are, the more time it will take for the model to evolve to the steady state.

Suggested Citation

  • Ma, Jing & Zhu, He, 2018. "Rumor diffusion in heterogeneous networks by considering the individuals’ subjective judgment and diverse characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 276-287.
  • Handle: RePEc:eee:phsmap:v:499:y:2018:i:c:p:276-287
    DOI: 10.1016/j.physa.2018.02.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118301134
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2018.02.037?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Huo, Liang'an & Ma, Chenyang, 2017. "Dynamical analysis of rumor spreading model with impulse vaccination and time delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 653-665.
    2. Bettencourt, Luís M.A. & Cintrón-Arias, Ariel & Kaiser, David I. & Castillo-Chávez, Carlos, 2006. "The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 513-536.
    3. Zhang, Nan & Huang, Hong & Su, Boni & Zhao, Jinlong & Zhang, Bo, 2014. "Dynamic 8-state ICSAR rumor propagation model considering official rumor refutation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 333-346.
    4. Kosfeld, Michael, 2005. "Rumours and markets," Journal of Mathematical Economics, Elsevier, vol. 41(6), pages 646-664, September.
    5. Li, Dandan & Ma, Jing, 2017. "How the government’s punishment and individual’s sensitivity affect the rumor spreading in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 284-292.
    6. Wang, Jiajia & Zhao, Laijun & Huang, Rongbing, 2014. "2SI2R rumor spreading model in homogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 153-161.
    7. Xia, Ling-Ling & Jiang, Guo-Ping & Song, Bo & Song, Yu-Rong, 2015. "Rumor spreading model considering hesitating mechanism in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 295-303.
    8. Li, Dandan & Ma, Jing & Tian, Zihao & Zhu, Hengmin, 2015. "An evolutionary game for the diffusion of rumor in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 51-58.
    9. Nekovee, M. & Moreno, Y. & Bianconi, G. & Marsili, M., 2007. "Theory of rumour spreading in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 457-470.
    10. Ma, Jing & Li, Dandan & Tian, Zihao, 2016. "Rumor spreading in online social networks by considering the bipolar social reinforcement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 108-115.
    11. Fan, Chong-jun & Jin, Yang & Huo, Liang-an & Liu, Chen & Yang, Yun-peng & Wang, Ya-qiong, 2016. "Effect of individual behavior on the interplay between awareness and disease spreading in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 523-530.
    12. Liu, Yun & Diao, Su-Meng & Zhu, Yi-Xiang & Liu, Qing, 2016. "SHIR competitive information diffusion model for online social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 543-553.
    13. Afassinou, Komi, 2014. "Analysis of the impact of education rate on the rumor spreading mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 43-52.
    14. Zhao, Laijun & Wang, Jiajia & Chen, Yucheng & Wang, Qin & Cheng, Jingjing & Cui, Hongxin, 2012. "SIHR rumor spreading model in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2444-2453.
    15. Zhao, Laijun & Xie, Wanlin & Gao, H. Oliver & Qiu, Xiaoyan & Wang, Xiaoli & Zhang, Shuhai, 2013. "A rumor spreading model with variable forgetting rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6146-6154.
    16. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Jian & Yang, Lu-Xing & Yang, Xiaofan & Tang, Yuan Yan, 2020. "Cost-effective anti-rumor message-pushing schemes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    2. Huang, He & Pan, Jialin & Chen, Yahong, 2024. "The competitive diffusion of knowledge and rumor in a multiplex network: A mathematical model," Applied Mathematics and Computation, Elsevier, vol. 475(C).
    3. Lu, Peng & Deng, Liping & Liao, Hongbing, 2019. "Conditional effects of individual judgment heterogeneity in information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 335-344.
    4. Lu, Peng & Yao, Qi & Lu, Pengfei, 2019. "Two-stage predictions of evolutionary dynamics during the rumor dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 349-369.
    5. Lu, Peng, 2019. "Heterogeneity, judgment, and social trust of agents in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 447-461.

    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. Lu, Peng, 2019. "Heterogeneity, judgment, and social trust of agents in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 447-461.
    2. Lu, Peng & Deng, Liping & Liao, Hongbing, 2019. "Conditional effects of individual judgment heterogeneity in information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 335-344.
    3. Lu, Peng & Yao, Qi & Lu, Pengfei, 2019. "Two-stage predictions of evolutionary dynamics during the rumor dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 349-369.
    4. Li, Dandan & Ma, Jing, 2017. "How the government’s punishment and individual’s sensitivity affect the rumor spreading in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 284-292.
    5. Hosni, Adil Imad Eddine & Li, Kan & Ahmad, Sadique, 2020. "Analysis of the impact of online social networks addiction on the propagation of rumors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    6. Huo, Liang’an & Cheng, Yingying, 2019. "Dynamical analysis of a IWSR rumor spreading model with considering the self-growth mechanism and indiscernible degree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    7. Jia, Pingqi & Wang, Chao & Zhang, Gaoyu & Ma, Jianfeng, 2019. "A rumor spreading model based on two propagation channels in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 342-353.
    8. Yao, Yao & Xiao, Xi & Zhang, Chengping & Dou, Changsheng & Xia, Shutao, 2019. "Stability analysis of an SDILR model based on rumor recurrence on social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    9. Shuzhen Yu & Zhiyong Yu & Haijun Jiang, 2022. "Stability, Hopf Bifurcation and Optimal Control of Multilingual Rumor-Spreading Model with Isolation Mechanism," Mathematics, MDPI, vol. 10(23), pages 1-29, December.
    10. Linhe Zhu & Hongyong Zhao, 2017. "Dynamical behaviours and control measures of rumour-spreading model with consideration of network topology," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(10), pages 2064-2078, July.
    11. Pan, Cheng & Yang, Lu-Xing & Yang, Xiaofan & Wu, Yingbo & Tang, Yuan Yan, 2018. "An effective rumor-containing strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 80-91.
    12. Huo, Liang’an & Cheng, Yingying & Liu, Chen & Ding, Fan, 2018. "Dynamic analysis of rumor spreading model for considering active network nodes and nonlinear spreading rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 24-35.
    13. Yao Hongxing & Zou Yushi, 2019. "Research on Rumor Spreading Model with Time Delay and Control Effect," Journal of Systems Science and Information, De Gruyter, vol. 7(4), pages 373-389, August.
    14. Zhang, Yaming & Su, Yanyuan & Weigang, Li & Liu, Haiou, 2018. "Rumor and authoritative information propagation model considering super spreading in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 395-411.
    15. 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).
    16. Zhang, Jing & Wang, Xiaoli & Xie, Yanxi & Wang, Meihua, 2022. "Research on multi-topic network public opinion propagation model with time delay in emergencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    17. Zhu, He & Ma, Jing, 2019. "Analysis of SHIR rumor propagation in random heterogeneous networks with dynamic friendships," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 257-271.
    18. Jinxian Li & Yanping Hu & Zhen Jin, 2019. "Rumor Spreading of an SIHR Model in Heterogeneous Networks Based on Probability Generating Function," Complexity, Hindawi, vol. 2019, pages 1-15, June.
    19. Huo, Liang’an & Jiang, Jiehui & Gong, Sixing & He, Bing, 2016. "Dynamical behavior of a rumor transmission model with Holling-type II functional response in emergency event," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 228-240.
    20. Chen, Guanghua, 2019. "ILSCR rumor spreading model to discuss the control of rumor spreading in emergency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 88-97.

    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:eee:phsmap:v:499:y:2018:i:c:p:276-287. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.