Using embedding features

The performed analysis is equivalent to the analysis performed in Chapter 6, Building a Machine Learning Model with Graph Features, with scikit-learn, except that here, there is no need to add another package for model training, as everything is taken care of in GDS.

However, in the preceding chapter, we learned about another way to find node features, by learning them from the graph structure itself: node embeddings. In this section, we will use node embeddings as features for our classification task.

Choosing the graph embedding algorithm to use

In Chapter 7, Automatically Extracting Features with Graph Embeddings for Machine Learning, we talked about two graph embedding algorithms included in GDS: Node2Vec and GraphSAGE. They have some differences, and one of them is the kind of information they tend to encode. While Node2Vec tends to model the node positions in the graph (nodes close to each other in the graph will have close embeddings), GraphSAGE...