We present a structural attention network (SAN) for graph modeling, which is a novel approach to learn node representations based on graph attention networks (GATs), with the introduction of two improvements specially designed for graph-structured data. The transition matrix was used to differentiate the structures between the nodes. The output features of nodes in the graph are represented as the concatenation of multi-order features to differentiate the structures among multiple orders. This novel neural network is based on a graph attention network, which makes the model pay attention to the topology of the graph. Using various experiments on citation networks and a protein-protein interaction dataset, we demonstrate the benefits of structural information in graph attention mechanisms.

我们提出了一种用于图建模的结构注意力网络（SAN），这是一种基于图注意力网络（GAT）学习节点表示的新颖方法，并引入了两个专为图结构数据设计的改进。过渡矩阵用于区分节点之间的结构。图中节点的输出特征表示为多阶特征的级联，以区分多阶结构。这种新颖的神经网络基于图注意力网络，这使得模型关注图的拓扑。使用引文网络和蛋白质-蛋白质相互作用数据集上的各种实验，我们证明了图注意力机制中结构信息的好处。