%0 Journal Article %1 Crandall28122010 %A Crandall, David J. %A Backstrom, Lars %A Cosley, Dan %A Suri, Siddharth %A Huttenlocher, Daniel %A Kleinberg, Jon %D 2010 %J Proceedings of the National Academy of Sciences %K thema:exploiting_place_features_in_link_prediction_on_location-based_social_networks %N 52 %P 22436-22441 %R 10.1073/pnas.1006155107 %T Inferring social ties from geographic coincidences %U http://www.pnas.org/content/107/52/22436.abstract %V 107 %X 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.

@article{Crandall28122010, 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.}, added-at = {2014-04-24T14:42:55.000+0200}, author = {Crandall, David J. and Backstrom, Lars and Cosley, Dan and Suri, Siddharth and Huttenlocher, Daniel and Kleinberg, Jon}, biburl = {https://www.bibsonomy.org/bibtex/20cb10f191135cbbaa6bfec41f03d222d/b.helmerich}, description = {Inferring social ties from geographic coincidences}, doi = {10.1073/pnas.1006155107}, eprint = {http://www.pnas.org/content/107/52/22436.full.pdf+html}, interhash = {8cfff5f47d8cea7644d93ae004e3eed4}, intrahash = {0cb10f191135cbbaa6bfec41f03d222d}, journal = {Proceedings of the National Academy of Sciences}, keywords = {thema:exploiting_place_features_in_link_prediction_on_location-based_social_networks}, number = 52, pages = {22436-22441}, timestamp = {2014-04-24T14:42:55.000+0200}, title = {Inferring social ties from geographic coincidences}, url = {http://www.pnas.org/content/107/52/22436.abstract}, volume = 107, year = 2010 }