@conference {19035,
	title = {Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge},
	year = {2011},
	month = {2011},
	pages = {1825 - 1834},
	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.},
	keywords = {deanonymization, Flickr social photo sharing Website, graph theory, IJCNN 2011 social network challenge, Kaggle social network challenge, learning (artificial intelligence), machine learning, realworld link prediction, Simulated annealing, simulated annealing-based weighted graph matching algorithm, social networking (online)},
	author = {Narayanan, A. and Elaine Shi and Rubinstein, B.I.P.}
}
