PAAE: A UNIFIED FRAMEWORK FOR PREDICTING ANCHOR LINKS WITH ADVERSARIAL EMBEDDING

The goal of predicting anchor links is to align accounts from multiple networks by whether they are held by the same natural person. Network structure is the key information for predicting anchor links. Exploring the intrinsic attributes of the network structure is an important way to align anchor users across social networks. Existing methods use a representation learning approach to embed network vertices into low dimension vectors space. But these methods suffer from lack of additional constraints for enhancing the robustness of the embedding vectors when aligning anchor nodes across networks with large structural differences. To offer a robust method, we propose a novel adversarial representation learning approach to align users, called PAAE(predicting anchor links with adversarial embedding), which employs an adversarial regularization to capture the robust embedding vectors and maps anchor users with an alignment autoencoders. PAAE can solve both the network embedding problem and the user alignment problem simultaneously under a unified optimization framework. Through extensive experiments on real social network datasets, we demonstrate that PAAE significantly outperforms the state-of-the-art methods.