Deep learning on graphs The topic of this thesis is Deep learning on graphs. Deep learning is the subfield of machine learning studying algorithms and models constituted by several layers of functions learning in an increasingly level of abstraction the representation of a concept. “On graphs” refers to the application of this class of methodologies to data that are inherently represented as a graph structure, requiring ad hoc strategies and theoretical machinery to perform tasks of various nature over them. In particular, this work studies, from a compilative point of view, a relatively recent model of deep learning called Generative Adversarial Network (GAN).