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)
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.
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).
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Published online: June 6, 2016
Published in issue: June 28, 2016
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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.
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The authors declare no conflict of interest.
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