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Preferential information dynamics model for online social networks

Author

Listed:
  • Fu, Minglei
  • Yang, Hongbo
  • Feng, Jun
  • Guo, Wen
  • Le, Zichun
  • Lande, Dmytro
  • Manko, Dmytro

Abstract

In recent years, online social networks have become an important site for companies to promote their latest products. Consequently, evaluating how many clients are affected by preferential information distributed in online social networks has become essential. In this paper, a novel dynamic model called the follower super forwarder client (FSFC) model is proposed to address the spreading behavior of preferential information in online social networks. The mean field theory is adopted to describe the formulas of the FSFC model and the key parameters of the model are derived from the past forwarding data of the preferential information. The edge between a large-degree node to a small-degree node has a greater weight. In addition, two kinds of infection probabilities are adopted for large-degree node forwarders and small-degree node forwarders. To evaluate the performance of the FSFC model, preferential data published on the Sina microblog (www.weibo.com) for the Vivo smartphone, Alibaba’s Tmall shopping site, and the Xiaomi phone were selected as real cases. Simulation results indicate that the relative errors of the output of the FSFC model compared with the actual data are 0.0068% (Vivo smartphone), 0.0085% (Tmall), and 0.032% (Xiaomi phone), respectively. The results verify that the FSFC model is a feasible model for describing the spreading behavior of preferential information in online social networks.

Suggested Citation

  • Fu, Minglei & Yang, Hongbo & Feng, Jun & Guo, Wen & Le, Zichun & Lande, Dmytro & Manko, Dmytro, 2018. "Preferential information dynamics model for online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 993-1005.
    • Handle: RePEc:eee:phsmap:v:506:y:2018:i:c:p:993-1005
    DOI: 10.1016/j.physa.2018.05.017
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    as
    1. Huang, Yunhan & Ding, Li & Feng, Yun, 2016. "A novel epidemic spreading model with decreasing infection rate based on infection times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 1041-1048.
    2. Wanduku, Divine, 2017. "Complete global analysis of a two-scale network SIRS epidemic dynamic model with distributed delay and random perturbations," Applied Mathematics and Computation, Elsevier, vol. 294(C), pages 49-76.
    3. Li, Xun & Cao, Lang, 2016. "Diffusion processes of fragmentary information on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 624-634.
    4. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    5. Juang, Jonq & Liang, Yu-Hao, 2015. "Analysis of a general SIS model with infective vectors on the complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 382-395.
    6. Wan, Chen & Li, Tao & Guan, Zhi-Hong & Wang, Yuanmei & Liu, Xiongding, 2017. "Spreading dynamics of an e-commerce preferential information model on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 192-200.
    7. Li, Tao & Liu, Xiongding & Wu, Jie & Wan, Chen & Guan, Zhi-Hong & Wang, Yuanmei, 2016. "An epidemic spreading model on adaptive scale-free networks with feedback mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 649-656.
    8. Sen Pei & Lev Muchnik & Shaoting Tang & Zhiming Zheng & Hernán A Makse, 2015. "Exploring the Complex Pattern of Information Spreading in Online Blog Communities," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
    9. Chu, Xiangwei & Zhang, Zhongzhi & Guan, Jihong & Zhou, Shuigeng, 2011. "Epidemic spreading with nonlinear infectivity in weighted scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(3), pages 471-481.
    10. Li, Chun-Hsien, 2015. "Dynamics of a network-based SIS epidemic model with nonmonotone incidence rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 234-243.
    11. Yang, Junyuan & Chen, Yuming, 2017. "Effect of infection age on an SIR epidemic model with demography on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 527-541.
    12. 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.
    13. Xiang, Hong & Liu, Ying-Ping & Huo, Hai-Feng, 2017. "Stability of an SAIRS alcoholism model on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 276-292.
    14. Wang, Haiying & Wang, Jun & Ding, Liting & Wei, Wei, 2017. "Knowledge transmission model with consideration of self-learning mechanism in complex networks," Applied Mathematics and Computation, Elsevier, vol. 304(C), pages 83-92.
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