PsyLink: User Identity Linkage via Psychological Characteristic Modeling

User identity linkage aims to correlate multiple virtual identities of the same person across different social networks. It is widely used in person profile integration, potential friend recommendation, user behavior prediction, identity verification, etc. Since the data inconsistency and network heterogeneity among social networks, extracting features from profiles, contents, and network structures may lead to severe random noises. To this end, we propose PsyLink, a user identity linkage method via psychological characteristic modeling. To reduce the data noise, PsyLink extracts both internal personality characteristics and external interest characteristics to represent an individual. Furthermore, we design a neighborhood enhancement strategy to capture latent higher-order structural features through a graph contrastive learning technique. Under various parameter settings, the experimental results demonstrate that the macro-f1 of PsyLink improves 19.23% on average compared with state-of-the-art deep learning models. Combined with the psychological characteristics and graph contrastive learning, PsyLink is able to adequately learn the similarities and differences between user identities across social networks.