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|Abstract:||This paper describes the winning entry to the IJCNN 2011 Social Network Challenge run by Kaggle.com. The goal of the contest was to promote research on real-world link prediction, and the dataset was a graph obtained by crawling the popular Flickr social photo sharing website, with user identities scrubbed. By de-anonymizing much of the competition test set using our own Flickr crawl, we were able to effectively game the competition. Our attack represents a new application of de-anonymization to gaming machine learning contests, suggesting changes in how future competitions should be run. We introduce a new simulated annealing-based weighted graph matching algorithm for the seeding step of de-anonymization. We also show how to combine de-anonymization with link prediction-the latter is required to achieve good performance on the portion of the test set not de-anonymized-for example by training the predictor on the de-anonymized portion of the test set, and combining probabilistic predictions from de-anonymization and link prediction.|
|Citation:||Narayanan, Arvind, Elaine Shi, and Benjamin IP Rubinstein. "Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge." The 2011 International Joint Conference on Neural Networks (2011): pp. 1825-1834. doi:10.1109/IJCNN.2011.6033446|
|Pages:||1825 - 1834|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||The 2011 International Joint Conference on Neural Networks|
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