Graph Convolutional Networks (GCNs) have been widely studied. The core of
GCNs is the definition of convolution operators on graphs. However, existing
Graph Convolution (GC) operators are mainly defined on adjacency matrix and
node features and generally focus on obtaining effective node embeddings which
cannot be utilized to address the graphs with (high-dimensional) edge features.
To address this problem, by leveraging tensor contraction representation and
tensor product graph diffusion theories, this paper analogously defines an
effective convolution operator on graphs with edge features which is named as
Tensor Product Graph Convolution (TPGC). The proposed TPGC aims to obtain
effective edge embeddings. It provides a complementary model to traditional
graph convolutions (GCs) to address the more general graph data analysis with
both node and edge features. Experimental results on several graph learning
tasks demonstrate the effectiveness of the proposed TPGC.

通过张量收缩表示和张量乘积图扩散理论，本文提出了一种名为 Tensor Product Graph Convolution (TPGC) 的图卷积算子，用于处理带有边特征的图数据分析，以获取有效的边嵌入表示。实验结果表明，所提出的 TPGC 对于多个图学习任务具有很好的效果。