We extend the graph convolutional network method for deep learning on graph data to higher order in terms of neighboring nodes. In order to construct representations for a node in a graph, in addition to the features of the node and its immediate neighboring nodes, we also include more distant nodes in the calculations. In experimenting with a number of publicly available citation graph datasets, we show that this higher order neighbor visiting pays off by outperforming the original model especially when we have a limited number of available labeled data points for the training of the model.

翻译：我们将图卷积网络方法在深度学习图数据上的应用扩展至更高阶的邻接节点范围。为构建图中节点的表示，除了节点自身及其直接相邻节点的特征外，我们在计算中还纳入更远距离的节点。通过在多个公开的引文图数据集上进行实验，我们证明这种高阶邻居访问策略能够带来性能提升，尤其在有限标注数据点用于模型训练时，其表现显著优于原始模型。