Hyperbolic User Identity Linkage across Social Networks

With the growing prosperity and diversity of social networks, more and more users participate in multiple social networks to enjoy their diverse services. Users can create different user identities in different social networks, but most networks are independent of each other. Hence, a fundamental question arises: can we align user identities across social networks? Related work mostly focuses on Euclidean geometry to learn representation vectors of social networks. However, motivated by recent advances in geometry representation learning, we find that hyperbolic geometry shows the advantage of expressing network hierarchical structure, while Euclidean geometry doesn't. Thus, in this paper, we first introduce the connection between hyperbolic space and social networks. Then we propose a novel hyperbolic geometry representation learning model for user identity linkage across social networks, which is called “HUIL”. Finally, we conduct comprehensive experiments on real-world datasets and verify the superiority of HUIL for user identity linkage.