Contents


Link Prediction by De-anonymization:
How We Won the Kaggle Social Network Challenge

Arvind Narayanan

Department of Computer Science, Stanford University

Elaine Shi

Computer Science Division, UC Berkeley;
PARC

Benjamin I. P. Rubinstein

Microsoft Research, Silicon Valley

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.

Arvind Narayanan 2011-03-07