Variational Cross-Network Embedding for Anonymized User Identity Linkage

Abstract

User identity linkage (UIL) task aims to infer the identical users between different social networks/platforms. Existing models leverage the labeled inter-linkages or high-quality user attributes to make predictions. Nevertheless, it is often difficult or even impossible to obtain such information in real-world applications. To this end, we in this paper focus on studying an Anonymized User Identity Linkage (AUIL) problem wherein neither labeled anchor users nor attributes are available. To handle such a practical and challenging task, we propose a novel and concise unsupervised embedding method, VCNE, by utilizing the network structural information. Concretely, considering the inherent properties of structural diversity in the AUIL problem, we introduce a variational cross-network embedding learning framework to jointly study the Gaussian embeddings instead of the existing deterministic embedding from the angle of vector space. The multi-facet experiments on both real-world and synthetic datasets demonstrate that VCNE not only outperforms all baselines to a large extent but also be more robust to the different-level diversities and sparsities of the networks.