These are personal notes on everything about GDL, from Graph Theory to GCN; they're part of my curricular internship program at University of Bologna and should be the ones leading to my bachelor thesis. Most of these are summaries/smaller copy-paste versions of papers/articles, with direct citations and links.
To explore such topics and to experiment with implementing graph-based-learning NNs is my main focus, while providing all the resources from whom I've studied. By doing this I'm trying to define a simple and clear roadmap for anyone approaching GDL.
There will also be a number of jupyter notebooks: the examples are taken from these very useful PyTorch introductory articles from IAML (which are also available in italian) which helped kickstart my skills in PyTorch.