Adrián Arnaiz-Rodríguez and Ameya Velingker’s tutorial on Graph Learning (link) started with an introduction to graph learning, including a historical perspective.
They then covered basics of graph neural networks (GNNs) and trade-offs of GNNs vs graph transformers, as well as an overview of expressivity and generalisability (for more on this, see their poster in session 6 on Thursday) .
They introduced three core challenges faced by GNNs: under-reaching, over-smoothing, and over-squashing, and trade-offs between them controlled by GNN depth and sparsity techniques.
The tutorial finished with a panel discussion the future directions of graph learning. The tutorial site can be found here.