The unsupervised graph embedding roadmap

Graphs are complex mathematical structures defined in a non-Euclidean space. Roughly speaking, this means that it is not always easy to define what is close to what; it might also be hard to say what close even means. Imagine a social network graph: two users can be respectively connected and yet share very different features—one might be interested in fashion and clothes, while the other might be interested in sports and videogames. Can we consider them as "close"?

For this reason, unsupervised machine learning algorithms have found large applications in graph analysis. Unsupervised machine learning is the class of machine learning algorithms that can be trained without the need for manually annotated data. Most of those models indeed make use of only information in the adjacency matrix and the node features, without any knowledge of the downstream machine learning task.

How is this possible? One of the most used solutions...