Chapter 3: Unsupervised Graph Learning
Unsupervised machine learning refers to the subset of machine learning algorithms that do not exploit any target information during training. Instead, they work on their own to find clusters, discover patterns, detect anomalies, and solve many other problems for which there is no teacher and no correct answer known a priori.
As per many other machine learning algorithms, unsupervised models have found large applications in the graph representation learning domain. Indeed, they represent an extremely useful tool for solving various downstream tasks, such as node classification and community detection, among others.
In this chapter, an overview of recent unsupervised graph embedding methods will be provided. Given a graph, the goal of these techniques is to automatically learn a latent representation of it, in which the key structural components are somehow preserved.
The following topics will be covered in this chapter:
- The unsupervised...
