Tutorial: Exploring the practical and theoretical landscape of expressive graph neural networks

Organizers: Fabrizio Frasca, Beatrice Bevilacqua, and Haggai Maron

Abstract: In an effort to overcome the expressiveness limitations of Graph Neural Networks (GNNs), a multitude of novel architectures has been recently proposed, aiming to balance expressive power, computational complexity, and domain-specific empiri- cal performance. Several directions and methods are involved in this recent surge, ranging from Graph Theory and Topology to Group Theory and theoretical Com- puter Science. As a result, researchers who wish to work on this critical topic are exposed to an unsystematic collection of seemingly independent approaches whose relations remain poorly understood. In an effort to address this issue, the pro- posed tutorial reviews the most prominent expressive GNNs, categorises them into different families, and draws interesting connections between them. This is accom- plished through a series of practical coding sessions and an organic overview of the literature landscape. We aim to convey the importance of studying the expressive power of GNNs and make this field more accessible to our community, especially practitioners and newcomers.

[sy]

Dear LOG, could you please release the Colab notebook link in the tutorial? It will help a lot, thanks!

[chinmay.prabhakar]

Thank you so much for uploading the video. Can you please publish the slides as well?