Information diffusion structure on social networks with general degree distribution

Wenyao Li, Shuang Zhang, Wei Wang and Tao Lin
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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

International Journal of Modern Physics C (IJMPC), 2021, vol. 32, issue 04, 1-15

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.

Keywords: Information spreading; diffusion structure; social network (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0129183121500479

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