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Analysis of competitive information diffusion in a group-based population over social networks

Author

Listed:
  • Fu, Guiyuan
  • Chen, Feier
  • Liu, Jianguo
  • Han, Jingti

Abstract

The dynamics of competitive information diffusion over a connected social network is investigated in this paper. A modified SIR model for two competitive information is presented, where each individual may turn to either of the two information after interacting with a spreader, while the spreader associated with one information may change into the other information. The population is divided into three subgroups: innovators, ordinary and laggard subgroups, respectively. It is assumed that individuals in different subgroups have different spreading rates and switching rates, when they interact with others. The influence of innovators and network topology on the dynamics of the competitive information diffusion is analyzed through numerous numerical simulations. It is observed that innovators and larger network degree can help enlarge the coverage of the information among the population, but they cannot help one information to compete with the other one. Moreover, innovators cannot always accelerate the convergence speed, which depends more on the network topology.

Suggested Citation

  • Fu, Guiyuan & Chen, Feier & Liu, Jianguo & Han, Jingti, 2019. "Analysis of competitive information diffusion in a group-based population over social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 409-419.
    • Handle: RePEc:eee:phsmap:v:525:y:2019:i:c:p:409-419
    DOI: 10.1016/j.physa.2019.03.035
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    References listed on IDEAS

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    1. Zhuang, Yun-Bei & Chen, J.J. & Li, Zhi-hong, 2017. "Modeling the cooperative and competitive contagions in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 141-151.
    2. Hongli Liu & Yun Xie & Haibo Hu & Zhigao Chen, 2014. "Affinity based information diffusion model in social networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 25(05), pages 1-12.
    3. 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.
    4. Aral, Sinan & Muchnik, Lev & Sundararajan, Arun, 2013. "Engineering social contagions: Optimal network seeding in the presence of homophily," Network Science, Cambridge University Press, vol. 1(2), pages 125-153, August.
    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. Shahla Jafari & Hamidreza Navidi, 2018. "A Game-Theoretic Approach for Modeling Competitive Diffusion over Social Networks," Games, MDPI, vol. 9(1), pages 1-13, February.
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