An approach of anchor link prediction using graph attention mechanism - Van-Vang Le; Phuong Nguyen Huy Pham; Tran Kim Toai; Vaclav Snasel (Bulletin of Electrical Engineering and Informatics, 2022)
Nowadays social networks such as Twitter, LinkedIn, and Facebook are a popular and necessary platform. It is considered a miniature of an actual social network because of its advantages in connecting and sharing information between users. The analysis of data on online social networks has become a field that has attracted a lot of attention from the research community and anchor link prediction is one of the main research directions in this field. Depending on demand, a user can simultaneously participate in many different online social networks, anchor link prediction is a kind of task that determines the identity of a user on many different social networks. In this article, we proposed an algorithm that determines missing/future anchor links between users from two different online social networks. Our algorithm utilizes the graph attention technique to represent the source and target network into the low-dimension embedding spaces, we then apply the canonical correlation analysis to recline their embeddings into same latent spaces for final prediction.