Common graph layers

All the graph layers that we discuss in this section use some variation of the graph convolution operation described above. Contributors to graph libraries such as DGL provide prebuilt versions of many of these layers within a short time of it being proposed in an academic paper, so you will realistically never have to implement one of these. The information here is mainly for understanding how things work under the hood.

Graph convolution network

The Graph Convolution Network (GCN) is the graph convolution layer proposed by Kipf and Welling [1]. It was originally presented as a scalable approach for semi-supervised learning on graph-structured data. They describe the GCN as an operation over the node feature vectors X and the adjacency matrix A of the underlying graph and point out that this can be exceptionally powerful when the information in A is not present in the data X, such as citation links between documents in a citation network, or relations...