Summary
In this chapter, we refreshed our knowledge of 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 look at the concepts of neural networks and neural networks applied to graphs. We will also present the principal frameworks for deep learning and deep learning for graphs in order to better understand the examples throughout the rest of this book.
