Identifying Users Across Social Media Networks for Interpretable Fine-Grained Neighborhood Matching by Adaptive GAT

The primary concern of numerous online social media network (SMN) platforms is how to provide users with effective and personalized web services. To achieve this goal, SMN platforms typically begin by collecting user preferences based on user behaviors (e.g., browsing history, posts) or user profiles. However, the effective information about a specific user on a single SMN platform is limited and monotonous, preventing a comprehensive reflection of the user's preferences. Therefore, recognizing anonymous but identical users across two SMNs to integrate their information is crucial for enhancing web services. Clearly, cross-platform research has the potential to aid in the resolution of numerous problems in service computing theory and applications. Therefore, in this article, we present the C ross- P latform U ser M atcher ( CPUM ) framework, which attempts to map users into a union vector space and then performs user matching based on distance metrics. In particular, we introduce a GNN-based encoder Ada ptive G raph A ttention Ne t work ( AdaGAT ) for modeling user attributes and topology jointly in the social networks to capture two typical alignment principles: topology consistency and attribute consistency. Moreover, we derive AdaGAT from the heuristic of the spectral network alignment technique FINAL, which theoretically guarantees AdaGAT's efficacy. To the best of our knowledge, AdaGAT is the first representation-based alignment model to integrate these two alignment principles synergistically. In addition, two position encoding schemes are introduced to prevent alignment confusion that commonly arises with GNN-based alignment models. Extensive experiments on real-world datasets validate the superiority of the proposed framework.