Graph Convolutional Networks (GCNs) have recently become the primary choice
for learning from graph-structured data, superseding hash fingerprints in
representing chemical compounds. However, GCNs lack the ability to take into
account the ordering of node neighbors, even when there is a geometric
interpretation of the graph vertices that provides an order based on their
spatial positions. To remedy this issue, we propose Spatial Graph Convolutional
Network (SGCN) which uses spatial features to efficiently learn from graphs
that can be naturally located in space. Our contribution is threefold: we
propose a GCN-inspired architecture which (i) leverages node positions, (ii) is
a proper generalization of both GCNs and Convolutional Neural Networks (CNNs),
(iii) benefits from augmentation which further improves the performance and
assures invariance with respect to the desired properties. Empirically, SGCN
outperforms state-of-the-art graph-based methods on image classification and
chemical tasks.

提出了 Spatial Graph Convolutional Network (SGCN) 来改进 Graph Convolutional Networks (GCNs)，SGCN 利用了空间特征来有效地学习从自然定位的图形中，经过实验证明，SGCN 优于现有的图形方法在图像分类和化学任务上的表现。