随着社交网络和在线平台的快速发展，身份关联问题成为安全审计和推荐系统中的重要研究课题。为有效解决身份关联问题，提出一种基于多层图注意力网络和对比学习相结合的方法。通过引入图注意力机制，模型能够更精确地捕捉用户之间的复杂关系，同时，对比学习的应用进一步提升了节点嵌入的判别能力和鲁棒性，模型中还融合一种联合优化策略，使得图注意力机制和对比学习能够协同工作，从而增强了模型的整体性能。实验结果表明：所提出的eCGAT-UIL模型在3个不同数据集上均可以表现出优异的性能，显著优于其他现有基线模型。

With the rapid development of social networks and online platforms, the issue of user identity linkage has become a critical research topic in security auditing and recommendation systems. To effectively address the problem of user identity linkage, a method combining multi-layer graph attention networks (GAT) and contrastive learning was proposed. By incorporating the graph attention mechanism, the model was able to capture complex relationships between users more accurately. Additionally, the application of contrastive learning further enhanced the discriminative power and robustness of the node embeddings. A joint optimization strategy was also integrated into the model, enabling the graph attention mechanism and contrastive learning to work synergistically, thereby improving the overall performance of the model. Experimental results demonstrate that the proposed eCGAT-UIL model achieves superior performance across three different datasets, significantly outperforming other existing baseline models.