Chapter 4: Supervised Graph Learning
Supervised learning (SL) most probably represents the majority of practical machine learning (ML) tasks. Thanks to more and more active and effective data collection activities, it is very common nowadays to deal with labeled datasets.
This is also true for graph data, where labels can be assigned to nodes, communities, or even to an entire structure. The task, then, is to learn a mapping function between the input and the label (also known as a target or an annotation).
For example, given a graph representing a social network, we might be asked to guess which user (node) will close their account. We can learn this predictive function by training graph ML on retrospective data, where each user is labeled as "faithful" or "quitter" based on whether they closed their account after a few months.
In this chapter, we will explore the concept of SL and how it can be applied on graphs. Therefore, we will also be providing an...