Can graph neural networks understand chemistry?

🎦 Video: https://www.youtube.com/watch?v=jrVXJykB8qc

A talk by Dominique Beaini on their recent work and the 'maze analogy' for graph representation learning.

Covering papers on Principle Neighbourhood Aggregation, Directional GNNs, and Graph Transformers, this talk touches several sub-areas of recent advances in GNN architectures - WL testing and expressivity, positional encodings, anisotropy, spectral techniques, fully connected message passing, etc.