Realistic modelling of information spread using peer-to-peer diffusion patterns

Bin Zhou*, Sen Pei*, Lev Muchnik, Xiangyi Meng, Xiaoke Xu, Alon Sela, Shlomo Havlin, H. Eugene Stanley

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


In computational social science, epidemic-inspired spread models have been widely used to simulate information diffusion. However, recent empirical studies suggest that simple epidemic-like models typically fail to generate the structure of real-world diffusion trees. Such discrepancy calls for a better understanding of how information spreads from person to person in real-world social networks. Here, we analyse comprehensive diffusion records and associated social networks in three distinct online social platforms. We find that the diffusion probability along a social tie follows a power-law relationship with the numbers of disseminator’s followers and receiver’s followees. To develop a more realistic model of information diffusion, we incorporate this finding together with a heterogeneous response time into a cascade model. After adjusting for observational bias, the proposed model reproduces key structural features of real-world diffusion trees across the three platforms. Our finding provides a practical approach to designing more realistic generative models of information diffusion.

Original languageAmerican English
Pages (from-to)1198-1207
Number of pages10
JournalNature Human Behaviour
Issue number11
StatePublished - Nov 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature Limited.


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