实体对齐中的图注意力网络局限性：共享权重矩阵和静态性

Most early entity alignment models use graph convolutional networks or more effective graph attention networks to structurally model the relationship triples of entities. However, when applied to entity alignment tasks, the limitations of traditional static graph attention networks mainly lie in the aspects of weight matrix sharing and static nature.