human travel patterns albert-lászló barabási center for complex networks research, northeastern...
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Human Travel Patterns
Albert-László BarabásiAlbert-László Barabási
Center for Complex Networks Research, Northeastern U.Center for Complex Networks Research, Northeastern U.
Department of Medicine, Harvard U.Department of Medicine, Harvard U.
BarabasiLab.com
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Tropical forest in Yucatan, Mexico
“Lévy flight” by a spider monkey
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Animal MotionAnimal Motion
Vishwanatan et. al. Nature (1996); Nature (1999).
Wandering AlbatrossWandering Albatross
Wandering AlbatrossWandering Albatross
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Sims et al. Nature (2008).
1 Jun
22 Jun
24 May
7 Aug
11 Jul
28 Jul
5 Jan
10 Oct
24 Nov
Basking shark
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Porbeagle shark Sims et al. Nature (2008).
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Random Walks Lévy flights
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Human MotionHuman Motion
Brockmann et. al. Nature (2006)
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A real human trajectory
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Mobile Phone Users
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CCNRCCNR
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).
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ALB, Nature 2005.
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CCNRCCNR
Interevent TimesInterevent Times
J. Candia et al.
A-L. B, Nature 2005.
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0 km 300 km100 km 200 km
0 km
100
km
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Mobile Phone Users
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Two possible explanations
1.Each users follows a Lévy flight
2.The difference between individuals follows a power law
β=1.75±0.15
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Understanding human trajectoriesUnderstanding human trajectories
Radius of Radius of Gyration:Gyration:
Center of Mass:Center of Mass:
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Characterizing human trajectoriesCharacterizing human trajectories
Radius of Radius of Gyration:Gyration:
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Characterizing human trajectoriesCharacterizing human trajectories
Radius of Radius of Gyration:Gyration:
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βr=1.65±0.15
Scaling in human trajectoriesScaling in human trajectories
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0 km 300 km100 km 200 km
0 km
100
km
200
km
Mobile Phone Users
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β=1.75±0.15βr=1.65±0.15
Scaling in human trajectoriesScaling in human trajectories
α=1.2
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Relationship between exponentsRelationship between exponents
Jump size distribution P(Δr)~(Δr)-β represents a convolution between
*population heterogeneity P(rg)~rg-βr
*Levy flight with exponent α truncated by rg
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CCNRCCNR
Return time distributionsReturn time distributions
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Mobile Phone Users
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The shape of human trajectoriesThe shape of human trajectories
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CCNRCCNR
The shape of human trajectoriesThe shape of human trajectories
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Mobile Phone Viruses
Hypponen M. Scientific American Nov. 70-77 (2006).
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Spreading mechanism for cell phone viruses
Short range infection process(similar to biological viruses, like influenza or SARS)
Long range infection process(similar to computer viruses)
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Palla, A.-L. B & Vicsek (2007).Onella et al, PNAS (2007)
Social Network (MMS virus)
González, Hidalgo and A-L.B.,
Nature 453, 779 (2008)
Human Motion(Bluetooth virus)
MMS and Bluetooth Viruses
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Spatial Spreading Patterns of Bluetooth and MMS virus
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Spatial Spreading patterns of Bluetooth and MMS viruses
Bluetooth Virus MMS Virus
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).
What is the origin of this saturation?
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Spreading mechanism for cell phone viruses
If the market share of an Operating Systemis under mc~10%, MMS
viruses cannot spread.
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0 km 300 km100 km 200 km
0 km
100
km
200
km
Mobile Phone Users
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Pu Wang Cesar Hidalgo
CollaboratorsCollaborators
Marta Gonzalez
www.BarabasiLab.com