{"id":160937,"date":"2011-02-22T00:00:00","date_gmt":"2011-02-22T00:00:00","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/msr-research-item\/link-prediction-by-de-anonymization-how-we-won-the-kaggle-social-network-challenge\/"},"modified":"2018-10-16T20:52:54","modified_gmt":"2018-10-17T03:52:54","slug":"link-prediction-by-de-anonymization-how-we-won-the-kaggle-social-network-challenge","status":"publish","type":"msr-research-item","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/link-prediction-by-de-anonymization-how-we-won-the-kaggle-social-network-challenge\/","title":{"rendered":"Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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.<\/p>\n<p>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&#8212;the latter is required to achieve good performance on the portion of the test set not de-anonymized&#8212;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.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"IEEE","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"1825-1834","msr_page_range_start":"1825","msr_page_range_end":"1834","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the 2011 International Joint Conference on Neural Networks 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