Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge

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This paper describes the winning entry to the IJCNN 2011 Social Network Challenge run by 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 predictionthe latter is required to achieve good performance on the portion of the test set not de-anonymizedfor example by training the predictor on the de-anonymized portion of the test set, and combining probabilistic predictions from de-anonymization and link prediction. © 2011 IEEE.




Narayanan, A., Shi, E., & Rubinstein, B. I. P. (2011). Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge. In Proceedings of the International Joint Conference on Neural Networks (pp. 1825–1834).

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