Introducing Graph Convolutional Networks
The Graph Convolutional Network (GCN) architecture is the blueprint of what a GNN looks like. Introduced by Kipf and Welling in 2017 [1], it is based on the idea of creating an efficient variant of Convolutional Neural Networks (CNNs) applied to graphs. More accurately, it is an approximation of a graph convolution operation in graph signal processing. Thanks to its versatility and ease of use, the GCN has become the most popular GNN in scientific literature. More generally, it is the architecture of choice to create a solid baseline when dealing with graph data.
In this chapter, we’ll talk about the limitations of our previous vanilla GNN layer. This will help us to understand the motivation behind GCNs. We’ll detail how the GCN layer works and why it performs better than our solution. We’ll test this statement by implementing a GCN on the Cora and Facebook Page-Page datasets using PyTorch Geometric. This should improve...
