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Human Travel Patterns

Human Travel Patterns. Albert- László Barabási Center for Complex Networks Research, Northeastern U. Department of Medicine, Harvard U. BarabasiLab.com. Tropical forest in Yucatan, Mexico. “Lévy flight” by a spider monkey. Animal Motion. Pókmajom. Wandering Albatross.

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Human Travel Patterns

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  1. Human Travel Patterns Albert-LászlóBarabási Center for Complex Networks Research, Northeastern U. Department of Medicine, Harvard U. BarabasiLab.com

  2. Tropical forest in Yucatan, Mexico “Lévy flight” by a spider monkey

  3. Animal Motion Pókmajom Wandering Albatross Wandering Albatross Vishwanatan et. al. Nature(1996); Nature (1999). Sirályok repülése

  4. Basking shark Sims et al. Nature (2008).

  5. Sims et al. Nature (2008). Porbeagle shark

  6. Random Walks Lévy flights

  7. Human Motion Brockmann et. al. Nature (2006)

  8. A real human trajectory

  9. Mobile Phone Users

  10. Data Collection Limitations We know only the tower the user communicates with, not the real location. We know the location (tower) only when the user makes a call. Interevent times are bursty (non-Poisson process—Power law interevent time distribution).

  11. ALB, Nature 2005.

  12. Interevent Times A-L. B, Nature 2005. J. Candia et al.

  13. Mobile Phone Users 200 km 100 km 0 km 0 km 100 km 200 km 300 km

  14. β=1.75±0.15 • Two possible explanations • Each users follows a Lévy flight • The difference between individuals follows a power law

  15. Understanding human trajectories Center of Mass: Radius of Gyration:

  16. Characterizing human trajectories Radius of Gyration:

  17. Characterizing human trajectories Radius of Gyration:

  18. Scaling in human trajectories βr=1.65±0.15

  19. Mobile Phone Users 200 km 100 km 0 km 0 km 100 km 200 km 300 km

  20. Scaling in human trajectories β=1.75±0.15 βr=1.65±0.15 α=1.2

  21. Relationship between exponents Jump size distribution P(Δr)~(Δr)-βrepresents a convolution between *population heterogeneity P(rg)~rg-βr *Levy flight with exponent α truncated by rg

  22. Return time distributions

  23. Mobile Phone Users

  24. The shape of human trajectories

  25. The shape of human trajectories

  26. Mobile Phone Viruses Hypponen M. Scientific American Nov. 70-77 (2006).

  27. Spreading mechanism for cell phone viruses Long range infection process (similar to computer viruses) Short range infection process (similar to biological viruses, like influenza or SARS)

  28. MMS and Bluetooth Viruses Social Network (MMS virus) Human Motion (Bluetooth virus) González, Hidalgo and A-L.B., Nature 453, 779 (2008) Palla, A.-L. B & Vicsek (2007). Onella et al, PNAS (2007)

  29. Spatial Spreading Patterns of Bluetooth and MMS virus

  30. Spatial Spreading patterns of Bluetooth and MMS viruses Bluetooth Virus MMS Virus What is the origin of this saturation? Driven by Human Mobility: Slow, but can reach all users with time. Driven by the Social Network: Fast, but can reach only a finite fraction of users (the giant component).

  31. Spreading mechanism for cell phone viruses If the market share of an Operating System is under mc~10%, MMS viruses cannot spread.

  32. Mobile Phone Users 200 km 100 km 0 km 0 km 100 km 200 km 300 km

  33. Collaborators Pu Wang Marta Gonzalez Cesar Hidalgo www.BarabasiLab.com

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