Solving Common Graph-Based Machine Learning Problems
Graph machine learning (ML) approaches can be useful for a wide range of tasks, with applications ranging from drug design to recommender systems in social networks. Furthermore, given the fact that such methods are general by design (meaning that they are not tailored to a specific problem), the same algorithm can be used to solve different problems.
There are common problems that can be effectively solved using graph-based learning techniques, as they excel at capturing relationships and structures in data. In this chapter, we will mention some of the most well studied of these by providing details about how a specific algorithm, among the ones we have already learned about in Chapter 4, Unsupervised Graph Learning, and Chapter 5, Supervised Graph Learning, can be used to solve a task. After reading this chapter, you will be aware of the formal definition of many common problems you may encounter when dealing with graphs...
