A Semantic-Enhancement-Based Social Network User-Alignment Algorithm

User alignment can associate multiple social network accounts of the same user. It has important research implications. However, the same user has various behaviors and friends across different social networks. This will affect the accuracy of user alignment. In this paper, we aim to improve the accuracy of user alignment by reducing the semantic gap between the same user in different social networks. Therefore, we propose a semantically enhanced social network user alignment algorithm (SENUA). The algorithm performs user alignment based on user attributes, user-generated contents (UGCs), and user check-ins. The interference of local semantic noise can be reduced by mining the user's semantic features for these three factors. In addition, we improve the algorithm's adaptability to noise by multi-view graph-data augmentation. Too much similarity of non-aligned users can have a large negative impact on the user-alignment effect. Therefore, we optimize the embedding vectors based on multi-headed graph attention networks and multi-view contrastive learning. This can enhance the similar semantic features of the aligned users. Experimental results show that SENUA has an average improvement of 6.27% over the baseline method at hit-precision30. This shows that semantic enhancement can effectively improve user alignment.

Automated summary

User alignment is important for associating multiple social network accounts of the same user. However, the varying behaviors and friends of the same user across different social networks affect the accuracy of alignment. To mitigate this, a semantically enhanced social network user alignment algorithm (SENUA) is proposed. SENUA aligns users based on attributes, user-generated contents, and check-ins, while reducing local semantic noise by mining semantic features. The algorithm's adaptability to noise is improved using multi-view graph-data augmentation. Furthermore, embedding vectors are optimized using multi-headed graph attention networks and multi-view contrastive learning to enhance aligned users' similar semantic features. Experimental results demonstrate a 6.27% average improvement in hit-precision30, indicating the effectiveness of semantic enhancement in user alignment.