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SPIR: The potential spreaders involved SIR model for information diffusion in social networks

Author

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  • Rui, Xiaobin
  • Meng, Fanrong
  • Wang, Zhixiao
  • Yuan, Guan
  • Du, Changjiang

Abstract

The Susceptible–Infective–Removed (SIR) model is one of the most widely used models for the information diffusion research in social networks. Many researchers have devoted themselves to improving the classic SIR model in different aspects. However, on the one hand, the equations of these improved models are regarded as continuous functions, while the corresponding simulation experiments use discrete time, leading to the mismatch between numerical solutions got from mathematical method and experimental results obtained by simulating the spreading behaviour of each node. On the other hand, if the equations of these improved models are solved discretely, susceptible nodes will be calculated repeatedly, resulting in a big deviation from the actual value. In order to solve the above problem, this paper proposes a Susceptible–Potential–Infective–Removed (SPIR) model that analyses the diffusion process based on the discrete time according to simulation. Besides, this model also introduces a potential spreader set which solve the problem of repeated calculation effectively. To test the SPIR model, various experiments have been carried out from different angles on both artificial networks and real world networks. The Pearson correlation coefficient between numerical solutions of our SPIR equations and corresponding simulation results is mostly bigger than 0.95, which reveals that the proposed SPIR model is able to depict the information diffusion process with high accuracy.

Suggested Citation

  • Rui, Xiaobin & Meng, Fanrong & Wang, Zhixiao & Yuan, Guan & Du, Changjiang, 2018. "SPIR: The potential spreaders involved SIR model for information diffusion in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 254-269.
    • Handle: RePEc:eee:phsmap:v:506:y:2018:i:c:p:254-269
    DOI: 10.1016/j.physa.2018.04.062
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    References listed on IDEAS

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    1. Zhao, Laijun & Cui, Hongxin & Qiu, Xiaoyan & Wang, Xiaoli & Wang, Jiajia, 2013. "SIR rumor spreading model in the new media age," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 995-1003.
    2. Qian, Zhen & Tang, Shaoting & Zhang, Xiao & Zheng, Zhiming, 2015. "The independent spreaders involved SIR Rumor model in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 95-102.
    3. Li, Weihua & Tang, Shaoting & Pei, Sen & Yan, Shu & Jiang, Shijin & Teng, Xian & Zheng, Zhiming, 2014. "The rumor diffusion process with emerging independent spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 397(C), pages 121-128.
    4. Wang, Jiajia & Zhao, Laijun & Huang, Rongbing, 2014. "SIRaRu rumor spreading model in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 43-55.
    5. 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.
    6. 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.
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