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Affinity based information diffusion model in social networks

Author

Listed:
  • Hongli Liu

    (School of Business, East China University of Science and Technology, Shanghai 200237, P. R. China)

  • Yun Xie

    (School of Business, East China University of Science and Technology, Shanghai 200237, P. R. China)

  • Haibo Hu

    (School of Business, East China University of Science and Technology, Shanghai 200237, P. R. China)

  • Zhigao Chen

    (School of Business, East China University of Science and Technology, Shanghai 200237, P. R. China)

Abstract

There is a widespread intuitive sense that people prefer participating in spreading the information in which they are interested. The affinity of people with information disseminated can affect the information propagation in social networks. In this paper, we propose an information diffusion model incorporating the mechanism of affinity of people with information which considers the fitness of affinity values of people with affinity threshold of the information. We find that the final size of information diffusion is affected by affinity threshold of the information, average degree of the network and the probability of people's losing their interest in the information. We also explore the effects of other factors on information spreading by numerical simulations and find that the probabilities of people's questioning and confirming the information can affect the propagation speed, but not the final scope.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:ijmpcx:v:25:y:2014:i:05:n:s012918311440004x
    DOI: 10.1142/S012918311440004X
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    Cited by:

    1. Wang, Haiying & Wang, Jun & Small, Michael, 2018. "Knowledge transmission model with differing initial transmission and retransmission process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 478-488.
    2. 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.

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