Anchor Link Prediction for Cross-Network Digital Forensics From Local and Global Perspectives

Anchor link prediction enhances the effectiveness of digital forensics through the identification of multiple social network users. The current methods based on deep learning are characterized by both the exaggerated similarity between adjacent nodes in the same latent space and the variation in the feature spaces caused by semantics. A novel approach is developed to fuse the semantic features of different networks in this paper. The proposed method is divided into two stages. Firstly, representation learning pays more attention to the influence of uncertainty on the equivalence of node network structure, and introduces the difference between adjacent nodes from the latent space. Secondly, a joint representation learning framework trains and exchanges the parameters depending on known anchor links. The joint representation learning framework injects fused features into the representation learning processes of different networks. The combination of enhanced discrimination and cross-network feature fusion reduces the feature space differences caused by the semantics of different social networks. This paper conducts comprehensive experiments on social networks in the real world. The outcome shows that the proposed approach is more efficient and robust compared to the existing state-of-the-art methods.