A Novel Framework with Information Fusion and Neighborhood Enhancement for User Identity Linkage
This addresses the user identity linkage problem for cross-network data mining, representing an incremental improvement over prior methods.
The paper tackles the problem of linking user identities across social networks by proposing INFUNE, a framework that fuses heterogeneous information and enhances neighborhood embeddings, which significantly outperforms existing state-of-the-art methods on real-world data.
User identity linkage across social networks is an essential problem for cross-network data mining. Since network structure, profile and content information describe different aspects of users, it is critical to learn effective user representations that integrate heterogeneous information. This paper proposes a novel framework with INformation FUsion and Neighborhood Enhancement (INFUNE) for user identity linkage. The information fusion component adopts a group of encoders and decoders to fuse heterogeneous information and generate discriminative node embeddings for preliminary matching. Then, these embeddings are fed to the neighborhood enhancement component, a novel graph neural network, to produce adaptive neighborhood embeddings that reflect the overlapping degree of neighborhoods of varying candidate user pairs. The importance of node embeddings and neighborhood embeddings are weighted for final prediction. The proposed method is evaluated on real-world social network data. The experimental results show that INFUNE significantly outperforms existing state-of-the-art methods.