TransLink: User Identity Linkage across Heterogeneous Social Networks via Translating Embeddings

Nowadays people tend to create accounts with multiple social networks (SNs) to enjoy a variety of social network services. User identity linkage (UM) aims to identify those multiple accounts belonging to a same person. UIL is of great importance to user behavior understanding and prediction, information dissemination, viral marketing, wellness diagnosis, etc. Most of existing solutions typically rely on the embedding of either user's attributes or behaviors into a latent vector space, and establish anchor link based on vector distance. However, these efforts still face challenges originated from the heterogeneity of SNs, incompleteness of user information and lack of enough known anchor links. In this paper, we investigate the UIL problem by presenting a translation-modeling approach TransLink. It jointly embeds both users and interactive behaviors of various SNs into a unified low-dimensional representation space according to a set of known anchor links. More specifically, we primarily study three typical SNs, c.g, Twitter, Foursquare and Instagram. Before embedding, we abstract schcmas of three SNs and extract interaction metapaths for each SN. By doing this we can efficiently address the first two challenges. Furthermore, iterative linkage can ensure linkage performance by using a very small set of known anchor links. Experiment results on two real-world datasets demonstrate the superiority of TransLink over the state-of-the-art approaches.