Summary
In this chapter, we refreshed some basic machine learning concepts and discovered how they can be applied to graphs. We defined basic graph machine learning terminology with a particular focus on graph representation learning. A taxonomy of the main graph machine learning algorithms was presented in order to clarify what differentiates the various ranges of solutions developed over the years. Finally, practical examples were provided to begin understanding how the theory can be applied to practical problems.
In the next chapter, we will revise the main graph-based machine learning algorithms. We will analyze their behavior and see how they can be used in practice.
