Difference between revisions of "Inferring Social Ties From Geographic Coincidences"
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| + | This paper addresses the problem of inferring social ties between people based on their co-occurrence in time and space. Given that two people have been in the same geographical location at around the same time on several occasions, what is the probability that they actually know each other? Such inferences, although very intuitive, have been difficult to make precise. In this respect, the paper's contribution is in developing a general analytic framework to quantify this probability. Applying the framework to a network of Flickr users: inferring the probability of a friendship (social tie) between two Flickr users given the number of photos they took at approximately the same place and at approximately the same time, the paper discovers that even a very small number such co-occurrences between two users can result in a high probability of friendship between them. The paper presents a probabilistic model that produces a good fit to this phenomenon of distributions observed in the actual Flickr data. The findings of the paper highlight also potential privacy implications in the possibility of inferring social structures even from small quantities of data capturing individual's geographical locations over time. | ||
== Description of the method == | == Description of the method == | ||
Revision as of 22:55, 2 February 2011
Contents
Citation
D. Crandall, L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher, J. Kleinberg. Inferring Social Ties from Geographic Coincidences. Proc. National Academy of Sciences 107 (52) 22436-22441, 28 December 2010.
Online version
Summary
This paper addresses the problem of inferring social ties between people based on their co-occurrence in time and space. Given that two people have been in the same geographical location at around the same time on several occasions, what is the probability that they actually know each other? Such inferences, although very intuitive, have been difficult to make precise. In this respect, the paper's contribution is in developing a general analytic framework to quantify this probability. Applying the framework to a network of Flickr users: inferring the probability of a friendship (social tie) between two Flickr users given the number of photos they took at approximately the same place and at approximately the same time, the paper discovers that even a very small number such co-occurrences between two users can result in a high probability of friendship between them. The paper presents a probabilistic model that produces a good fit to this phenomenon of distributions observed in the actual Flickr data. The findings of the paper highlight also potential privacy implications in the possibility of inferring social structures even from small quantities of data capturing individual's geographical locations over time.
