Integrated Anchor and Social Link Predictions across Social Networks / 2125
Jiawei Zhang, Philip S. Yu
To enjoy more social network services, users nowadays are usually involved in multiple online social media sites at the same time. Across these social networks, users can be connected by both intra-network links (i.e., social links) and inter-network links (i.e., anchor links) simultaneously. 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. To solve the collective link identification problem, a unified link prediction framework, CLF (Collective Link Fusion) is proposed in this paper, which consists of two phases: step (1) collective link prediction of anchor and social links, 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.