The power of graph neural networks

Graph neural networks (GNNs) have become a prominent technique for graph learning tasks such as vertex and graph classification, link prediction and graph regression. It was recently shown that classical GNNs have limited expressive power. This resulted in the proposal of a plenitude of new - more expressive - graph learning architectures. In this course we will present a systematic investigation in the expressive power of these different architectures. We here use techniques from areas such as graph algorithms, logic and query languages. The goal is to introduce various ways of boosting the expressive power of GNNs and to provide techniques to estimate the expressive power of GNNs. The conceptual part of the course is complemented with some practical coding sessions showing how theory and practice compare.