Multi-network user identification via graph-aware embedding

Abstract

User identification is widely used in anomaly detection, recommendation system and so on. Previous approaches focus on extraction of features describing users, and the learners try to emphasize the differences between different user identities. However, one applicable user identification scenario occurs in the circumstance of social network, where features of users are not acquirable while only relationships between users are provided. In this paper, we aim at the later situation, i.e., the Network User Identification, where features of users cannot be extracted in social network applications. We consider the information limitation of the single network and focus on utilizing the multiple relationships between identities from multi-networks. Different from the existing common subspace methods in Cross-Network User Identification, we propose a more discriminative Graph-Aware Embedding (GAEM) method for modeling the relationships as well as the transformation between different social networks explicitly in one unified framework. As a consequence, we can get more accurate predictions of the user identities directly based on the learned transferring model with GAEM. The experimental evaluations on real-world data demonstrate the superiorities of our proposed method comparing to the state-of-the-art.