  We present an attention-based spatial graph convolution (AGC) for graph
neural networks (GNNs). Existing AGCs focus on only using node-wise features
and utilizing one type of attention function when calculating attention
weights. Instead, we propose two methods to improve the representational power
of AGCs by utilizing 1) structural information in a high-dimensional space and
2) multiple attention functions when calculating their weights. The first
method computes a local structure representation of a graph in a
high-dimensional space. The second method utilizes multiple attention functions
simultaneously in one AGC. Both approaches can be combined. We also propose a
GNN for the classification of point clouds and that for the prediction of point
labels in a point cloud based on the proposed AGC. According to experiments,
the proposed GNNs perform better than existing methods. Our codes open at
https://github.com/liyang-tuat/SFAGC.
