Scaling identity connects human mobility and social interactions

Edited by Adrian Dobra, University of Washington, Seattle, WA, and accepted by Editorial Board Member Adrian E. Raftery April 20, 2016 (received for review January 21, 2016)
June 6, 2016
113 (26) 7047-7052

Significance

Both our mobility and communication patterns obey spatial constraints: Most of the time, our trips or communications occur over a short distance, and occasionally, we take longer trips or call a friend who lives far away. These spatial dependencies, best described as power laws, play a consequential role in broad areas ranging from how an epidemic spreads to diffusion of ideas and information. Here we established the first formal link, to our knowledge, between mobility and communication patterns by deriving a scaling relationship connecting them. The uncovered scaling theory not only allows us to derive human movements from communication volumes, or vice versa, but it also documents a new degree of regularity that helps deepen our quantitative understanding of human behavior.

Abstract

Massive datasets that capture human movements and social interactions have catalyzed rapid advances in our quantitative understanding of human behavior during the past years. One important aspect affecting both areas is the critical role space plays. Indeed, growing evidence suggests both our movements and communication patterns are associated with spatial costs that follow reproducible scaling laws, each characterized by its specific critical exponents. Although human mobility and social networks develop concomitantly as two prolific yet largely separated fields, we lack any known relationships between the critical exponents explored by them, despite the fact that they often study the same datasets. Here, by exploiting three different mobile phone datasets that capture simultaneously these two aspects, we discovered a new scaling relationship, mediated by a universal flux distribution, which links the critical exponents characterizing the spatial dependencies in human mobility and social networks. Therefore, the widely studied scaling laws uncovered in these two areas are not independent but connected through a deeper underlying reality.

Continue Reading

Acknowledgments

This work was supported by the College of Information, Sciences, and Technology at Pennsylvania State University; the Network Science Collaborative Technology Alliance sponsored by the US Army Research Laboratory under Agreement W911NF-09-2-0053; and the Defense Threat Reduction Agency Awards WMD BRBAA07-J-2-0035 and BRBAA08-Per4-C-2-0033. P.D. is supported by the National Fund for Scientific Research (FNRS) and by the Research Department of the Communauté francaise de Belgique (Large Graph Concerted Research Action).

Supporting Information

Supporting Information (PDF)
Supporting Information

References

1
D Brockmann, L Hufnagel, T Geisel, The scaling laws of human travel. Nature 439, 462–465 (2006).
2
MC González, CA Hidalgo, AL Barabási, Understanding individual human mobility patterns. Nature 453, 779–782 (2008).
3
C Song, Z Qu, N Blumm, AL Barabási, Limits of predictability in human mobility. Science 327, 1018–1021 (2010).
4
C Song, T Koren, P Wang, A Barabási, Modelling the scaling properties of human mobility. Nat Phys 6, 818–823 (2010).
5
D Wang, D Pedreschi, C Song, F Giannotti, A-L Barabasi, Human mobility, social ties, and link prediction. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Association for Computing Machinery, New York), pp. 1100–1108 (2011).
6
F Simini, MC González, A Maritan, A-L Barabási, A universal model for mobility and migration patterns. Nature 484, 96–100 (2012).
7
Y-A de Montjoye, CA Hidalgo, M Verleysen, VD Blondel, Unique in the Crowd: The privacy bounds of human mobility. Sci Rep 3, 1376 (2013).
8
DJ Watts Six Degrees: The Science of a Connected Age (WW Norton, New York, 2004).
9
A-L Barabási Linked: The New Science of Networks (Perseus, Cambridge, MA, 2002).
10
D Lazer, et al., Life in the network: The coming age of computational social science. Science 323, 721 (2009).
11
VD Blondel, et al., Data for development: The d4d challenge on mobile phone data. arXiv:1210.0137. (2012).
12
VD Blondel, A Decuyper, G Krings, A survey of results on mobile phone datasets analysis. arXiv:1502.03406. (2015).
13
S Wasserman, K Faust Social Network Analysis: Methods and Applications (Cambridge Univ Press, Cambridge, UK) Vol 8 (1994).
14
S Milgram, The small world problem. Psychol Today 1, 61–67 (1967).
15
SP Borgatti, A Mehra, DJ Brass, G Labianca, Network analysis in the social sciences. Science 323, 892–895 (2009).
16
MS Granovetter, The strength of weak ties. Am J Sociol 78, 1360–1380 (1973).
17
JS Coleman, Social capital in the creation of human capital. Am J Sociol 94, S95–S120 (1988).
18
N Lin, K Cook, RS Burt, Social capital: Theory and research. Sociology and Economics: Controversy and Integration Series (Aldine de Gruyter, New York), pp. 31–56 (2001).
19
F Fukuyama Trust: The Social Virtues and the Creation of Prosperity (Free Press, New York) Vol 457 (1996).
20
M Barthélemy, Spatial networks. Phys Rep 499, 1–101 (2011).
21
V Colizza, A Barrat, M Barthélemy, A Vespignani, The role of the airline transportation network in the prediction and predictability of global epidemics. Proc Natl Acad Sci USA 103, 2015–2020 (2006).
22
V Colizza, R Pastor-Satorras, A Vespignani, Reaction–diffusion processes and metapopulation models in heterogeneous networks. Nat Phys 3, 276–282 (2007).
23
D Balcan, et al., Multiscale mobility networks and the spatial spreading of infectious diseases. Proc Natl Acad Sci USA 106, 21484–21489 (2009).
24
JP Bagrow, D Wang, A-L Barabási, Collective response of human populations to large-scale emergencies. PLoS One 6, e17680 (2011).
25
X Lu, L Bengtsson, P Holme, Predictability of population displacement after the 2010 Haiti earthquake. Proc Natl Acad Sci USA 109, 11576–11581 (2012).
26
L Gao, et al., Quantifying information flow during emergencies. Sci Rep 4, 3997 (2014).
27
D Liben-Nowell, J Novak, R Kumar, P Raghavan, A Tomkins, Geographic routing in social networks. Proc Natl Acad Sci USA 102, 11623–11628 (2005).
28
LH Wong, P Pattison, G Robins, A spatial model for social networks. Physica A 360, 99–120 (2006).
29
R Lambiotte, et al., Geographical dispersal of mobile communication networks. Physica A 387, 5317–5325 (2008).
30
S Scellato, A Noulas, R Lambiotte, C Mascolo, Socio-spatial properties of online location-based social networks. Proceedings of the Fifth International Conference on Weblogs and Social Media (Association for Advancement of Artificial Intelligence, Menlo Park, CA), pp. 329–336 (2011).
31
JM Kleinberg, Navigation in a small world. Nature 406, 845 (2000).
32
M Boguna, D Krioukov, KC Claffy, Navigability of complex networks. Nat Phys 5, 74–80 (2008).
33
M Boguñá, F Papadopoulos, D Krioukov, Sustaining the Internet with hyperbolic mapping. Nat Commun 1, 62 (2010).
34
LA Adamic, RM Lukose, AR Puniyani, BA Huberman, Search in power-law networks. Phys Rev E Stat Nonlin Soft Matter Phys 64, 046135 (2001).
35
L Adamic, E Adar, How to search a social network. Soc Networks 27, 187–203 (2005).
36
D Wang, et al., Information spreading in context. Proceedings of the 20th International Conference on World Wide Web (Association for Computing Machinery, New York), pp. 735–744 (2011).
37
EM Rogers Diffusion of Innovations (Simon and Schuster, New York, 2010).
38
E Cho, SA Myers, J Leskovec, Friendship and mobility: User movement in location-based social networks. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Association for Computing Machinery, New York), pp. 1082–1090 (2011).
39
A Noulas, S Scellato, R Lambiotte, M Pontil, C Mascolo, A tale of many cities: universal patterns in human urban mobility. PLoS One 7, e37027 (2012).
40
JL Toole, C Herrera-Yaqüe, CM Schneider, MC González, Coupling human mobility and social ties. J R Soc Interface 12, 20141128 (2015).
41
GK Zipf, The P1 P2/D hypothesis: On the intercity movement of persons. Am Sociol Rev 11, 677–686 (1946).
42
J-P Rodrigue, C Comtois, B Slack The Geography of Transport Systems (Routledge, New York, 2013).
43
E Bullmore, O Sporns, Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10, 186–198 (2009).
44
G Krings, F Calabrese, C Ratti, VD Blondel, Urban gravity: A model for inter-city telecommunication flows. J Stat Mech 2009, L07003 (2009).
45
S Erlander, NF Stewart The Gravity Model in Transportation Analysis: Theory and Extensions (VSP, Zeist, The Netherlands) Vol 3 (1990).
46
A Barrat, M Barthélemy, R Pastor-Satorras, A Vespignani, The architecture of complex weighted networks. Proc Natl Acad Sci USA 101, 3747–3752 (2004).
47
J Giles, Computational social science: Making the links. Nature 488, 448–450 (2012).
48
WER Team, et al., Ebola virus disease in West Africa--the first 9 months of the epidemic and forward projections. N Engl J Med; WHO Ebola Response Team 371, 1481–1495 (2014).
49
MFC Gomes, et al., Assessing the international spreading risk associated with the 2014 West African Ebola outbreak. PLoS Currents Outbreaks September 2, 2014, Edition 1. (2014).
50
J Shaman, A Karspeck, W Yang, J Tamerius, M Lipsitch, Real-time influenza forecasts during the 2012-2013 season. Nat Commun 4, 2837 (2013).
51
R Pastor-Satorras, C Castellano, P Van Mieghem, A Vespignani, Epidemic processes in complex networks. arXiv:1408.2701. (2014).
52
P Wang, MC González, CA Hidalgo, A-L Barabási, Understanding the spreading patterns of mobile phone viruses. Science 324, 1071–1076 (2009).
53
D Brockmann, D Helbing, The hidden geometry of complex, network-driven contagion phenomena. Science 342, 1337–1342 (2013).
54
R Pastor-Satorras, A Vespignani, Epidemic spreading in scale-free networks. Phys Rev Lett 86, 3200–3203 (2001).
55
M Boguñá, R Pastor-Satorras, Epidemic spreading in correlated complex networks. Phys Rev E Stat Nonlin Soft Matter Phys 66, 047104 (2002).
56
N Eagle, AS Pentland, D Lazer, Inferring friendship network structure by using mobile phone data. Proc Natl Acad Sci USA 106, 15274–15278 (2009).
57
A Clauset, C Shalizi, M Newman, Power-law distributions in empirical data. arXiv:0706.1062. (2007).
58
M Kardar Statistical Physics of Fields (Cambridge Univ Press, Cambridge, UK, 2007).
59
C Viboud, et al., Synchrony, waves, and spatial hierarchies in the spread of influenza. Science 312, 447–451 (2006).
60
L Hufnagel, D Brockmann, T Geisel, Forecast and control of epidemics in a globalized world. Proc Natl Acad Sci USA 101, 15124–15129 (2004).
61
RM Anderson, RM May Infectious Diseases of Humans (Oxford Univ Press, Oxford, UK) Vol 1 (1991).
62
J Heesterbeek Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation (Wiley, Chichester, UK) Vol 5 (2000).

Information & Authors

Information

Published in

Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 113 | No. 26
June 28, 2016
PubMed: 27274050

Classifications

Submission history

Published online: June 6, 2016
Published in issue: June 28, 2016

Keywords

  1. human mobility
  2. social interactions
  3. mobile phone data
  4. social networks
  5. spatial networks

Acknowledgments

This work was supported by the College of Information, Sciences, and Technology at Pennsylvania State University; the Network Science Collaborative Technology Alliance sponsored by the US Army Research Laboratory under Agreement W911NF-09-2-0053; and the Defense Threat Reduction Agency Awards WMD BRBAA07-J-2-0035 and BRBAA08-Per4-C-2-0033. P.D. is supported by the National Fund for Scientific Research (FNRS) and by the Research Department of the Communauté francaise de Belgique (Large Graph Concerted Research Action).

Notes

This article is a PNAS Direct Submission. A.D. is a guest editor invited by the Editorial Board.

Authors

Affiliations

Pierre Deville
Department of Applied Mathematics, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium;
Center for Complex Network Research, Department of Physics, Biology and Computer Science, Northeastern University, Boston, MA 02115;
Chaoming Song
Department of Physics, University of Miami, Coral Gables, FL 33142;
Nathan Eagle
College of Computer Science, Northeastern University, Boston, MA 02115;
Vincent D. Blondel
Department of Applied Mathematics, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium;
Albert-László Barabási
Center for Complex Network Research, Department of Physics, Biology and Computer Science, Northeastern University, Boston, MA 02115;
Center for Cancer Systems Biology, Dana Farber Cancer Institute, Boston, MA 02115;
Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115;
College of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802

Notes

1
To whom correspondence should be addressed. Email: [email protected].
Author contributions: P.D., C.S., N.E., V.D.B., A.-L.B., and D.W. designed research; P.D., C.S., and D.W. performed research; P.D., C.S., and D.W. analyzed data; and P.D., C.S., and D.W. wrote the paper.

Competing Interests

The authors declare no conflict of interest.

Metrics & Citations

Metrics

Note: The article usage is presented with a three- to four-day delay and will update daily once available. Due to ths delay, usage data will not appear immediately following publication. Citation information is sourced from Crossref Cited-by service.


Citation statements

Altmetrics

Citations

Export the article citation data by selecting a format from the list below and clicking Export.

Cited by

    Loading...

    View Options

    View options

    PDF format

    Download this article as a PDF file

    DOWNLOAD PDF

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Personal login Institutional Login

    Recommend to a librarian

    Recommend PNAS to a Librarian

    Purchase options

    Purchase this article to access the full text.

    Single Article Purchase

    Scaling identity connects human mobility and social interactions
    Proceedings of the National Academy of Sciences
    • Vol. 113
    • No. 26
    • pp. 7003-E3810

    Media

    Figures

    Tables

    Other

    Share

    Share

    Share article link

    Share on social media