Shallow embedding methods
As we already described in Chapter 3, Unsupervised Graph Learning, shallow embedding methods are a subset of graph embedding methods that learn node, edge, or graph representation for only a finite set of input data. They cannot be applied to other instances different from the ones used to train the model. Before starting our discussion, it is important to define how supervised and unsupervised shallow embedding algorithms differ.
The main difference between unsupervised and supervised embedding methods essentially lies in the task they attempt to solve. Indeed, if unsupervised shallow embedding algorithms try to learn a good graph, node, or edge representation in order to build well-defined clusters, the supervised algorithms try to find the best solution for a prediction task such as node, label, or graph classification.
In this section, we will explain in detail some of those supervised shallow embedding algorithms. Moreover, we will enrich...
