The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Inferring social ties from geographic coincidences
Open accessAbstract
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.
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(1988) · Cited by 16,582
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,059
Gorski, K. M., Hivon, E., Banday, A. J., Wandelt, B. D., Hansen, F. K., Reinecke, M., & Bartelmann, M. (2005) · The Astrophysical Journal · doi:10.1086/427976 · Cited by 5,958
Robust De-anonymization of Large Sparse Datasets
Narayanan, A., & Shmatikov, V. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.33 · Cited by 2,372
The scaling laws of human travel
Brockmann, D., Hufnagel, L., & Geisel, T. (2006) · Nature · doi:10.1038/nature04292 · Cited by 2,205
Inferring friendship network structure by using mobile phone data
Eagle, N., Pentland, A. (., & Lazer, D. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0900282106 · Cited by 1,856
De-anonymizing Social Networks
Narayanan, A., & Shmatikov, V. (2009) · 2009 30th IEEE Symposium on Security and Privacy · doi:10.1109/sp.2009.22 · Cited by 1,301
Markov Random Fields and Their Applications
Kindermann, R., & Snell, J. L. (1980) · Contemporary Mathematics · doi:10.1090/conm/001 · Cited by 1,290
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Exploiting place features in link prediction on location-based social networks
Scellato, S., Noulas, A., & Mascolo, C. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020575 · Cited by 522
Our Twitter Profiles, Our Selves: Predicting Personality with Twitter
Quercia, D., Kosinski, M., Stillwell, D., & Crowcroft, J. (2011) · 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing · doi:10.1109/passat/socialcom.2011.26 · Cited by 478
Event-based social networks: linking the online and offline social worlds
Liu, X., He, Q., Tian, Y., Lee, W.-C., McPherson, J., & Han, J. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339693 · Cited by 299
Shuai, X., Pepe, A., & Bollen, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047523 · Cited by 293
Inferring social ties across heterogenous networks
Tang, J., Lou, T., & Kleinberg, J. (2012) · Proceedings of the fifth ACM international conference on Web search and data mining · doi:10.1145/2124295.2124382 · Cited by 289
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Metadata
- Authors
- Crandall, D. J., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., & Kleinberg, J.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2010-12-08
- Volume
- 107
- Issue
- 52
- Pages
- 22436-22441
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W1995629273
- Cited by
- 473
- References
- 26
- FWCI
- 81.67
- Type
- journal article
- Language
- en
- Open access
- green
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- Crandall, D. J., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., & Kleinberg, J. (2010). Inferring social ties from geographic coincidences. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1006155107
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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