Conference Proceeding Article
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 linkage problem — (I) the platform multiplicity and (II) online 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.
Digital Communications and Networking | OS and Networks
Data Management and Analytics
Proceedings of 22nd ACM SIGKDD international conference on knowledge discovery and data mining
MU, Xin; ZHU, Feida; LIM, Ee-Peng; XIAO, Jing; WANG, Jianzong; and ZHOU, Zhi-Hua.
User Identity Linkage by Latent User Space Modelling. (2016). Proceedings of 22nd ACM SIGKDD international conference on knowledge discovery and data mining. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3185
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