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Information diffusion structure on social networks with general degree distribution

Author

Listed:
  • Wenyao Li

    (College of Computer Science, Sichuan University, Chendu 610065, P. R. China)

  • Shuang Zhang

    (College of Computer Science, Sichuan University, Chendu 610065, P. R. China)

  • Wei Wang

    (Cybersecurity Research Institute, Sichuan University, Chengdu 610065, P. R. China)

  • Tao Lin

    (College of Computer Science, Sichuan University, Chendu 610065, P. R. China)

Abstract

In this paper, we study the information diffusion structure on social networks with general degree distribution. To describe the information diffusion structure, we adopt six different viewpoints of metrics, including structural virality, distance variance, distance variability, distance susceptibility, cascade depth and cascade width. On Erdös–Rényi (ER) networks, we can intuitively see that as the diffusion tree becomes denser, the depth of the diffusion tree first increases to a peak and then decreases with the infection rate increasing, in addition the distance distribution of the diffusion tree obeys exponential distribution, and the metrics except cascade width decrease after reaching their peak values. When the information diffuses on scale-free (SF) networks, the diffusion trees are similar with the ones on ER networks. In other words, compared with the degree distribution, the infection rate is the main factor of diffusion tree in the same network scale.

Suggested Citation

  • Wenyao Li & Shuang Zhang & Wei Wang & Tao Lin, 2021. "Information diffusion structure on social networks with general degree distribution," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 32(04), pages 1-15, April.
    • Handle: RePEc:wsi:ijmpcx:v:32:y:2021:i:04:n:s0129183121500479
    DOI: 10.1142/S0129183121500479
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