Scaling and correlation of human movements in cyberspace and physical space

Zhi-Dan Zhao, Zi-Gang Huang, Liang Huang, Huan Liu, Ying-Cheng Lai

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


Understanding the dynamics of human movements is key to issues of significant current interest such as behavioral prediction, recommendation, and control of epidemic spreading. We collect and analyze big data sets of human movements in both cyberspace (through browsing of websites) and physical space (through mobile towers) and find a superlinear scaling relation between the mean frequency of visit ⟨f⟩ and its fluctuation σ:σ∼⟨f⟩ β with β≈1.2 . The probability distribution of the visiting frequency is found to be a stretched exponential function. We develop a model incorporating two essential ingredients, preferential return and exploration, and show that these are necessary for generating the scaling relation extracted from real data. A striking finding is that human movements in cyberspace and physical space are strongly correlated, indicating a distinctive behavioral identifying characteristic and implying that the behaviors in one space can be used to predict those in the other.
Original languageEnglish
Article number050802(R)
JournalPhysical Review. E, Statistical, Nonlinear and Soft Matter Physics
Publication statusPublished - 12 Nov 2014


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