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：我们扩展了图形变迁网络方法, 用于在图形数据中深层学习图形数据, 在相邻节点方面达到更高的顺序。 为了在图表中构建节点的表示, 除了节点及其相邻节点的特征外, 我们还在计算中加入了更远的节点。 在尝试一些公开的引用图数据集时, 我们显示这个更高级的访问邻居以优于原始模型的方式获得回报, 特别是当我们拥有数量有限的用于模型培训的标签数据点时 。