Integrated anchor and social link predictions across multiple social networks

摘要

In recent years, various online social networks offering specific services have gained great popularity and success. To enjoy more online social services, some users can be involved in multiple social networks simultaneously. A challenging problem in social network studies is to identify the common users across networks to gain better understanding of user behavior. This is referred to as the anchor link prediction problem. Meanwhile, across these partially aligned social networks, users can be connected by different kinds of links, e.g., social links among users in one single network and anchor links between accounts of the shared users in different networks. Many different link prediction methods have been proposed so far to predict each type of links separately. In this paper, we want to predict the formation of social links among users in the target network as well as anchor links aligning the target network with other external social networks. The problem is formally defined as the “collective link identification” problem. Predicting the formation of links in social networks with traditional link prediction methods, e.g., classification-based methods, can be very challenging. The reason is that, from the network, we can only obtain the formed links (i.e., positive links) but no information about the links that will never be formed (i.e., negative links). To solve the collective link identification problem, a unified link prediction framework, collective link fusion (CLF) is proposed in this paper, which consists of two phases: step (1) collective link prediction of anchor and social links with positive and unlabeled learning techniques, and step (2) propagation of predicted links across the partially aligned “probabilistic networks” with collective random walk. Extensive experiments conducted on two real-world partially aligned networks demonstrate that CLF can perform very well in predicting social and anchor links concurrently.