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Spreading dynamics of an online social information model on scale-free networks

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  • Liu, Xiongding
  • Li, Tao
  • Xu, Hao
  • Liu, Wenjin

Abstract

In order to study the influence of the comment mechanism and the heterogeneity of underlying networks on the spreading of online social information, we present a new ICST (ignoramus-commentator-sharer-stifler) online social information spreading model based on scale-free networks. By using the mean-field theory, the spreading dynamics of the model is analyzed in detail. Then, the basic reproductive number R0 and equilibriums are derived. Theoretical results show that the basic reproduction number is significantly dependent on the topology of the underlying networks. The relationships among the basic reproduction number R0, sharing rate, effective comment rate are studied. Furthermore, the global stability of the information-elimination equilibrium, the permanence of online social information spreading and the global attractivity of information-prevailing equilibrium are proved in detail. In addition, we study the influence of weight in networks and analyze the corresponding of dynamics behaviors. The adaptive weight cannot change the basic reproductive number, but it can weaken the information spreading. Numerical simulations confirmed the analytical results.

Suggested Citation

  • Liu, Xiongding & Li, Tao & Xu, Hao & Liu, Wenjin, 2019. "Spreading dynamics of an online social information model on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 497-510.
    • Handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:497-510
    DOI: 10.1016/j.physa.2018.09.085
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    References listed on IDEAS

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    5. Huo, Liang'an & Wang, Li & Song, Naixiang & Ma, Chenyang & He, Bing, 2017. "Rumor spreading model considering the activity of spreaders in the homogeneous network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 855-865.
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    Cited by:

    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. Cheng, Yingying & Huo, Liang’an & Zhao, Laijun, 2020. "Rumor spreading in complex networks under stochastic node activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    3. Hou, Jingrui & Chi, Ming & Li, Tao & Guan, Zhi-Hong & Luo, Kai & Zhang, Ding-Xue, 2019. "Spreading dynamics of SVFR online fraud information model on heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    4. Yin, Fulian & Jiang, Xinyi & Qian, Xiqing & Xia, Xinyu & Pan, Yanyan & Wu, Jianhong, 2022. "Modeling and quantifying the influence of rumor and counter-rumor on information propagation dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    5. Ding, Haixin & Xie, Li, 2023. "Simulating rumor spreading and rebuttal strategy with rebuttal forgetting: An agent-based modeling approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    6. Dong, Xuefan & Lian, Ying, 2021. "A review of social media-based public opinion analyses: Challenges and recommendations," Technology in Society, Elsevier, vol. 67(C).
    7. Yanhui Wei & Liang’an Huo & Hongguang He, 2022. "Research on Rumor-Spreading Model with Holling Type III Functional Response," Mathematics, MDPI, vol. 10(4), pages 1-13, February.

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