通过去匿名化进行链接预测：我们如何赢得Kaggle社交网络挑战赛
Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge
摘要：
本文描述了Kaggle.com举办的IJCNN 2011年社交网络挑战赛的获奖作品。比赛的目的是促进对现实世界链接预测的研究，数据集是通过抓取流行的社交照片共享网站Flickr获得的图表，用户身份被擦洗。通过使用我们自己的Flickr抓取对大部分竞争测试集进行去匿名化，我们能够有效地参与竞争。我们的攻击代表了去匿名化在游戏机器学习竞赛中的新应用，这表明未来竞赛应该如何运行。我们介绍了一种新的模拟退火为基础的加权图匹配算法的种子去匿名化的步骤。我们还展示了如何将去匿名化与链接预测相结合--后者需要在测试集的未去匿名化部分上实现良好的性能--例如，通过在测试集的去匿名化部分上训练预测器，并将去匿名化和链接预测的概率预测相结合。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
