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Edited by Ronald L. Graham, University of California, San Diego, La Jolla, CA, and approved October 25, 2010 (received for review May 16, 2010)
Abstract
We investigate the extent to which social ties between people can be inferred from co-occurrence in time and space: Given that two people have been in approximately the same geographic locale at approximately the same time, on multiple occasions, how likely are they to know each other? Furthermore, how does this likelihood depend on the spatial and temporal proximity of the co-occurrences? Such issues arise in data originating in both online and offline domains as well as settings that capture interfaces between online and offline behavior. Here we develop a framework for quantifying the answers to such questions, and we apply this framework to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie. We then present probabilistic models showing how such large probabilities can arise from a natural model of proximity and co-occurrence in the presence of social ties. In addition to providing a method for establishing some of the first quantifiable estimates of these measures, our findings have potential privacy implications, particularly for the ways in which social structures can be inferred from public online records that capture individuals’ physical locations over time.
Footnotes
- 3To whom correspondence should be addressed. E-mail: kleinber{at}cs.cornell.edu.
Author contributions: D.J.C., L.B., D.C., S.S., D.H., and J.K. designed research; D.J.C., L.B., D.C., S.S. D.H., and J.K. performed research; D.J.C., L.B., D.H., and J.K. contributed new reagents/analytic tools; D.J.C. analyzed data; and D.J.C., D.H., and J.K. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
Inferring social ties from geographic coincidences
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