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Modeling the reemergence of information diffusion in social network

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  • Yang, Dingda
  • Liao, Xiangwen
  • Shen, Huawei
  • Cheng, Xueqi
  • Chen, Guolong

Abstract

Information diffusion in networks is an important research topic in various fields. Existing studies either focus on modeling the process of information diffusion, e.g., independent cascade model and linear threshold model, or investigate information diffusion in networks with certain structural characteristics such as scale-free networks and small world networks. However, there are still several phenomena that have not been captured by existing information diffusion models. One of the prominent phenomena is the reemergence of information diffusion, i.e., a piece of information reemerges after the completion of its initial diffusion process. In this paper, we propose an optimized information diffusion model by introducing a new informed state into traditional susceptible–infected–removed model. We verify the proposed model via simulations in real-world social networks, and the results indicate that the model can reproduce the reemergence of information during the diffusion process.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:1493-1500
    DOI: 10.1016/j.physa.2017.08.115
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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).
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    5. 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.

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