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Information spreading dynamics in hypernetworks

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  • Suo, Qi
  • Guo, Jin-Li
  • Shen, Ai-Zhong

Abstract

Contact pattern and spreading strategy fundamentally influence the spread of information. Current mathematical methods largely assume that contacts between individuals are fixed by networks. In fact, individuals are affected by all his/her neighbors in different social relationships. Here, we develop a mathematical approach to depict the information spreading process in hypernetworks. Each individual is viewed as a node, and each social relationship containing the individual is viewed as a hyperedge. Based on SIS epidemic model, we construct two spreading models. One model is based on global transmission, corresponding to RP strategy. The other is based on local transmission, corresponding to CP strategy. These models can degenerate into complex network models with a special parameter. Thus hypernetwork models extend the traditional models and are more realistic. Further, we discuss the impact of parameters including structure parameters of hypernetwork, spreading rate, recovering rate as well as information seed on the models. Propagation time and density of informed nodes can reveal the overall trend of information dissemination. Comparing these two models, we find out that there is no spreading threshold in RP, while there exists a spreading threshold in CP. The RP strategy induces a broader and faster information spreading process under the same parameters.

Suggested Citation

  • Suo, Qi & Guo, Jin-Li & Shen, Ai-Zhong, 2018. "Information spreading dynamics in hypernetworks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 475-487.
    • Handle: RePEc:eee:phsmap:v:495:y:2018:i:c:p:475-487
    DOI: 10.1016/j.physa.2017.12.108
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    References listed on IDEAS

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    Cited by:

    1. Liu, Xiaoyang & He, Daobing & Yang, Linfeng & Liu, Chao, 2019. "A novel negative feedback information dissemination model based on online social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 371-389.
    2. Shen, Ai-Zhong & Guo, Jin-Li & Wu, Guo-Lin & Jia, Shu-Wei, 2018. "The agglomeration phenomenon influence on the scaling law of the scientific collaboration system," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 461-467.
    3. Tian, Yang & Zhu, Xuzhen & Yang, Qiwen & Tian, Hui & Cui, Qimei, 2022. "Propagation characteristic of adoption thresholds heterogeneity in double-layer networks with edge weight distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    4. Hu, Sen & Hu, Bin & Cao, Ya, 2018. "The wider, the better? The interaction between the IoT diffusion and online retailers’ decisions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 196-209.

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