In recent years, Graph Neural Network (GNN) has gained increasing popularity in various domains due to its great expressive power and outstanding performance. Graph structures allow us to capture data with complex structures and relationships, and GNN provides us the opportunity to study and model this complex data representation for tasks such as classification, clustering, link prediction, and robust representation.

While the first motivation of GNN's roots traces back to 1997, it was only a few years ago (around 2017), that deep learning on graphs started to attract a lot of attention.

Since the concept is relatively new, most of the knowledge is learned through conference and journal papers, and when I started learning about GNN, I had difficulty knowing where to start and what to read, as there was no course available to structure the content. Therefore, I took it upon myself to construct this course with the objective of structuring the learning materials and providing a rapid full introductory course for GNN.

This course will provide complete introductory materials for learning Graph Neural Network. By finishing this course you get a good understanding of the topic both in theory and practice.
This means you will see both math and code.

If you want to start learning about Graph Neural Network, This is for you.

If you want to be able to implement Graph Neural Network models in PyTorch Geometric, This is for you.