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Immunization against the Spread of Rumors in Homogenous Networks

Author

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  • Laijun Zhao
  • Jiajia Wang
  • Rongbing Huang

Abstract

Since most rumors are harmful, how to control the spread of such rumors is important. In this paper, we studied the process of "immunization" against rumors by modeling the process of rumor spreading and changing the termination mechanism for the spread of rumors to make the model more realistic. We derived mean-field equations to describe the dynamics of the rumor spread. By carrying out steady-state analysis, we derived the spreading threshold value that must be exceeded for the rumor to spread. We further discuss a possible strategy for immunization against rumors and obtain an immunization threshold value that represents the minimum level required to stop the rumor from spreading. Numerical simulations revealed that the average degree of the network and parameters of transformation probability significantly influence the spread of rumors. More importantly, the simulations revealed that immunizing a higher proportion of individuals is not necessarily better because of the waste of resources and the generation of unnecessary information. So the optimal immunization rate should be the immunization threshold.

Suggested Citation

  • Laijun Zhao & Jiajia Wang & Rongbing Huang, 2015. "Immunization against the Spread of Rumors in Homogenous Networks," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-17, May.
    • Handle: RePEc:plo:pone00:0124978
    DOI: 10.1371/journal.pone.0124978
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    References listed on IDEAS

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    2. 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.
    3. Chen Jianhong & Song Qinghua & Zhou Zhiyong, 2017. "Agent-Based Simulation of Rumor Propagation on Social Network Based on Active Immune Mechanism," Journal of Systems Science and Information, De Gruyter, vol. 5(6), pages 571-584, December.
    4. Zhang, Yuhuai & Zhu, Jianjun, 2019. "Dynamic behavior of an I2S2R rumor propagation model on weighted contract networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    5. Zhang, Yuhuai & Zhu, Jianjun, 2018. "Stability analysis of I2S2R rumor spreading model in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 862-881.
    6. Lingnan He & Haoshen Yang & Xiling Xiong & Kaisheng Lai, 2019. "Online Rumor Transmission Among Younger and Older Adults," SAGE Open, , vol. 9(3), pages 21582440198, September.
    7. Hu, Yuhan & Pan, Qiuhui & Hou, Wenbing & He, Mingfeng, 2018. "Rumor spreading model considering the proportion of wisemen in the crowd," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 1084-1094.
    8. Nian, Fuzhong & Ren, Song & Dang, Zhongkai, 2017. "The propagation-weighted priority immunization strategy based on propagation tree," Chaos, Solitons & Fractals, Elsevier, vol. 99(C), pages 72-78.
    9. 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.
    10. Askarizadeh, Mojgan & Tork Ladani, Behrouz & Manshaei, Mohammad Hossein, 2019. "An evolutionary game model for analysis of rumor propagation and control in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 21-39.
    11. Wang, Chaoqian, 2020. "Dynamics of conflicting opinions considering rationality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    12. 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.
    13. Li, Jianfei & Li, Bei & Shen, Yang & Tang, Kun, 2022. "Study on the steady state of the propagation model of consumers’ perceived service quality in the community group-buying," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).

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