Adversarially Regularized Graph Embedding for User Identity Linkage Across Social Networks

User identity linkage across social networks aims to discover the potential correspondence between users across different social platforms. However, it is non-trivial to solve the practically relevant problem due to the following challenges. 1) User identity linkage requires analyzing relationships within individual networks as well as across networks, making it essential to preserve the intra-network and inter-network relationships. 2) The existing methods mostly ignore the data distribution of latent embeddings and lack additional constraints for enhancing the robustness of representations. Towards this end, we propose an adversarially regularized graph embedding-based method (ARUIL) for user identity linkage across social networks. Specifically, we first adopt a pair of graph auto-encoders with shared weights to embed the source and target networks from the structure space to a common latent vector space. Then, we design an effective dual constraint mechanism, intra-network relationship preserving and inter-network relationship preserving, to optimize the overall framework jointly. The former constraint based on network reconstruction aims to capture the original structural features of input networks. The latter one, inspired by the contrastive learning paradigm, seeks to minimize the distance between positive samples in the latent vector space and increase the distance from negative samples. Finally, adversarial regularization is introduced to enforce latent embeddings to match a prior Gaussian distribution to improve the generalization performance of our proposed method. Extensive experiments on two real-world social network datasets demonstrate that ARUIL outperforms the state-of-the-art baselines. The ablation study also illustrates the rationality and effectiveness of our proposed method.