Graph Machine Learning @ ICML 2023

Just finished a new Medium post summarizing Graph ML papers seen at ICML 2023 with some additional photos from Hawaii to make the text less boring 😉 What you can find inside:

- Graph Transformers: Sparser, Faster, and Directed
- Theory: VC dimension of GNNs, deep dive in over-squashing
- New GNN architectures: delays and half-hops
- Generative Models - Stable Diffusion for Molecules, Discrete diffusion
- Geometric Learning: Geometric WL, Clifford Algebras
- Molecules: 2D-3D pretraining, Uncertainty Estimation in MD
- Materials & Proteins: CLIP for proteins, Ewald Message Passing, Equivariant Augmentations
- Cool Applications: Algorithmic reasoning, Inductive KG completion, GNNs for mass spectra