实体对齐模型中的图注意力网络局限性：共享权重矩阵与静态性

Most early entity alignment models use graph convolutional networks or graph attention networks, which are more effective, to structurally model entity relationship triplets. However, when applied to entity alignment tasks, the limitations of traditional static graph attention networks mainly manifest in the aspects of shared weight matrices and static nature.