AD-Link: An Adaptive Approach for User Identity Linkage

User identity linkage (UIL) refers to linking accounts of the same user across different online social platforms. The state-of-the-art UIL methods usually perform account matching using user account's features derived from the profile attributes, content and relationships. They are however static and do not adapt well to fast-changing online social data due to: (a) new content and activities generated by users; as well as (b) new platform functions introduced to users. In particular, the importance of features used in UIL methods may change over time and new important user features may be introduced. In this paper, we proposed AD-Link, a new UIL method which (i) learns and assigns weights to the user features used for user identity linkage and (ii) handles new user features introduced by new user-generated data. We evaluated AD-Link on real-world datasets from three popular online social platforms, namely, Twitter, Facebook and Foursquare. The results show that AD-Link outperforms the state-of-the-art UIL methods.