Difference between revisions of "Inferring Social Ties From Geographic Coincidences"
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The paper's second contribution is in presenting a probabilistic model that produces a good fit to the distributions observed in the actual Flickr data. The findings of the paper also highlight potential privacy implications in the possibility of inferring social structures from even a small amount of spatio-temporal co-occurrence data. | The paper's second contribution is in presenting a probabilistic model that produces a good fit to the distributions observed in the actual Flickr data. The findings of the paper also highlight potential privacy implications in the possibility of inferring social structures from even a small amount of spatio-temporal co-occurrence data. | ||
| − | == Description of the method == | + | == Description of the method == |
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| + | First, surface of earth is divided into grid-like cells, each with ''s'' x ''s'' degrees of latitude and longitude. Two people ''A'' and ''B'' co-occurred in a given cell ''C'', at a temporal range ''t'', if both ''A'' and ''B'' took photos geo-tagged within a location in cell ''C'', within ''t'' days of each other. For each pair of users, the number of distinct cells (''k'') in which they co-occurred at temporal range ''t'' is counted. The probability of friendship between two users is then computed by first constructing the social network of Flickr using all friendship links up through April 2008 and then identifying spatio-temporal co-occurrences that occurred after April 2008 - hence counting only friendships existing prior to the accumulation of evidence via co-occurrences. The probability of friendship (fraction of users that are friends) is computed and plotted as a function of ''k'' co-occurrences (indicating amount of evidence for a social tie), cell size ''s'' and temporal time ''t'' (indicating the precision of the evidence). | ||
== Datasets used == | == Datasets used == | ||
Revision as of 12:35, 3 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 regard, the paper's contribution is in developing a general analytic framework to quantify this probability.
Applying the framework to a network of Flickr users: by 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 of such co-occurrences between two users can result in a high probability of friendship between them.
The paper's second contribution is in presenting a probabilistic model that produces a good fit to the distributions observed in the actual Flickr data. The findings of the paper also highlight potential privacy implications in the possibility of inferring social structures from even a small amount of spatio-temporal co-occurrence data.
Description of the method
First, surface of earth is divided into grid-like cells, each with s x s degrees of latitude and longitude. Two people A and B co-occurred in a given cell C, at a temporal range t, if both A and B took photos geo-tagged within a location in cell C, within t days of each other. For each pair of users, the number of distinct cells (k) in which they co-occurred at temporal range t is counted. The probability of friendship between two users is then computed by first constructing the social network of Flickr using all friendship links up through April 2008 and then identifying spatio-temporal co-occurrences that occurred after April 2008 - hence counting only friendships existing prior to the accumulation of evidence via co-occurrences. The probability of friendship (fraction of users that are friends) is computed and plotted as a function of k co-occurrences (indicating amount of evidence for a social tie), cell size s and temporal time t (indicating the precision of the evidence).
