User Identity Linkage by Latent User Space Modelling
Xin Mu*, Nanjing University; Feida Zhu, Singapore Management Univ.; Zhi-Hua Zhou, ; Ee-Peng Lim, Singapore Management University; Jing Xiao, ; Jianzong Wang,
User identity linkage across social platforms is an important problem of great research challenge and practical value. In real applications, the task often assumes an extra degree of diﬃculty by requiring linkage across multiple platforms. While pair-wise user linkage between two platforms, which has been the focus of most existing solutions, provides reasonably convincing linkage, the result depends by nature on the order of platform pairs in execution with no theoretical guarantee on its stability. In this paper, we explore a new concept of “Latent User Space” to more naturally model the relationship between the underlying real users and their observed projections onto the varied social platforms, such that the more similar the real users, the closer their proﬁles in the latent user space. We propose two eﬀective algorithms, a batch model(ULink) and an online model(ULink-On), based on latent user space modelling. Two simple yet eﬀective optimization methods are used for optimizing objective function: the ﬁrst one based on the constrained concave-convex procedure(CCCP) and the second on accelerated proximal gradient. To our best knowledge, this is the ﬁrst work to propose a uniﬁed framework to address the following two important aspects of the multi-platform user identity link-age problem — (I) the platform multiplicity and (II) on-line data generation. We present experimental evaluations on real-world data sets for not only traditional pairwise-platform linkage but also multi-platform linkage. The results demonstrate the superiority of our proposed method over the state-of-the-art ones.
Filed under: Graph Mining and Social Networks