Including Node Features with Vanilla Neural Networks

So far, the only type of information we’ve considered is the graph topology. However, graph datasets tend to be richer than a mere set of connections: nodes and edges can also have features to represent scores, colors, words, and so on. Including this additional information in our input data is essential to produce the best embeddings possible. In fact, this is something natural in machine learning: node and edge features have the same structure as a tabular (non-graph) dataset. This means that traditional techniques can be applied to this data, such as neural networks.

In this chapter, we will introduce two new graph datasets: Cora and Facebook Page-Page. We will see how Vanilla Neural Networks perform on node features only by considering them as tabular datasets. We will then experiment to include topological information in our neural networks. This will give us our first GNN architecture: a simple model that considers...