Predicting Anchor Links Based on a Supervised Iterative Framework with Strict Stable Matching

Nowadays, more and more people have their own accounts in different social networks, and they might use the different email addresses or phone numbers in different networks, so how to identify the same person among different social networks become a vital problem, called network alignment. Users with different accounts are called anchor users, researches showed that using some known anchor users to predict the potential anchor links for the full network is an effective way. To predict more accurate anchor links, the paper proposes a new prediction framework ISS, based on a reality of partially aligned social networks, it applies supervised learning based on social feature extraction and strict stable matching, which improve the accuracy of the prediction result, what is more, we apply an iterative framework to refine known information and maximize the prediction results. Experiments have conducted in two realworld heterogeneous social networks, Foursquare and Twitter, and it demonstrates that ISS can predict anchor links among heterogeneous social networks very well and outperform other similar prediction methods.