Expressiveness and Generalisation in Graph Neural Networks
The expressive power of graph neural networks (GNNs) has been widely analysed through their connection to the 1-dimensional Weisfeiler–Leman (1-WL) algorithm, a key tool for addressing the graph isomorphism problem. While this link has deepened our understanding of how GNNs represent complex structures, it provides limited insight into their generalisation—specifically, their ability to accurately predict on unseen data. In this talk, we delve into the relationship between GNNs’ expressive power and their generalisation capabilities, offering a unified perspective that bridges these two critical aspects of GNN performance.
Speaker
Floris Geerts is professor in ADREM, UAntwerp.
Time and Place
Wednesday 12/03/2025 at 13:45pm in M.A.143
Registration
Participation is free, but registration is compulsory. Make sure to fill in this form.