Adversarial-Enhanced Hybrid Graph Network for User Identity Linkage

In this work, we investigate the user identity linkage task across different social media platforms based on heterogeneous multi-modal posts and social connections. This task is non-trivial due to the following two challenges. 1) As each user involves both intra multi-modal posts and inter social connections, how to accurately fulfil the user representation learning from both intra and inter perspectives constitutes the main challenge. And 2) even representations distributed on different platforms of the same identity tend to be distinct (i.e., the semantic gap problem) owing to discrepant data distribution of different platforms. Hence, how to alleviate the semantic gap problem poses another tough challenge. To this end, we propose a novel adversarial-enhanced hybrid graph network (AHG-Net), consisting of three key components: user representation extraction, hybrid user representation learning, and adversarial learning. Specifically, AHG-Net first employs advanced deep learning techniques to extract the user's intermediate representations from his/her heterogeneous multi-modal posts and social connections. Then AHG-Net unifies the intra-user representation learning and inter-user representation learning with a hybrid graph network. Finally, AHG-Net adopts adversarial learning to encourage the learned user presentations of the same identity to be similar using a semantic discriminator. Towards evaluation, we create a multi-modal user identity linkage dataset by augmenting an existing dataset with 62, 021 images collected from Twitter and Foursquare. Extensive experiments validate the superiority of the proposed network. Meanwhile, we release the dataset, codes, and parameters to facilitate the research community.