Predicting Social Links for New Users across Aligned Heterogeneous Social Networks

摘要

Nowadsys, many new users are keeping joining in the online social networks every day and these new users usually have very few social connections and very sparse auxiliary information in the network. Prediction social links for new users is very important. Different from conventional link prediction problems, link prediction for new users is more challenging due to the lack of information from the new users in the network. Meanwhile, in recent years, users are usually involved in multiple social networks simultaneously to enjoy the specific services offered by different social networks. The shared users of multiple networks can act as the "anchors" aligned the networks they participate in. In this paper, we propose a link prediction method called SCAN-PS (Supervised Cross Aligned Networks link prediction with Personalized Sampling), to solve the social link prediction problem for new users. SCAN-PS can use information transferred from both the existing active users in the target network and other source networks through aligned accounts. In addition, SCAN-PS could solve the cold start problem when information of these new users is total absent in the target network. Extensive experiments conducted on two real-world aligned heterogeneous social networks demonstrate that SCAN-PS can perform well in predicting social links for new users.