Summary
In this chapter, we have learned how supervised ML can be effectively applied on graphs to solve real problems such as node and graph classification. In particular, we first analyzed how graph and node properties can be directly used as features to train classic ML algorithms. We have seen shallow methods and simple approaches to learning node, edge, or graph representations for only a finite set of input data. We then learned how regularization techniques can be used during the learning phase in order to create more robust models that tend to generalize better. Finally, we have seen how GNNs can be applied to solve supervised ML problems on graphs.
But what can these algorithms be useful for? In the next chapter, we will explore common problems that can be solved using graph-based ML techniques.
