Graph CNNs

In Chapter 3, Unsupervised Graph Learning, we have learned the main concepts behind GNNs and graph convolutional networks (GCNs). We have also learned the difference between spectral graph convolution and spatial graph convolution. More precisely, we have further seen that GCN layers can be used to encode graphs or nodes under unsupervised settings by learning how to preserve graph properties such as node similarity.

In this chapter, we will explore such methods under supervised settings. This time, our goal is to learn graphs or node representations that can accurately predict node or graph labels. It is indeed worth noting that the encoding function remains the same. What will change is the objective!

Graph classification using GCNs

Let's consider again our PROTEINS dataset. Let's load the dataset as follows:

import pandas as pd
from stellargraph import datasets
dataset = datasets.PROTEINS()
graphs, graph_labels = dataset.load()
# necessary for converting...