Graph ML in 2022

The editors of the Graph ML channel proudly present the winter longread (in collaboration with Anton Tsitsulin and Anvar Kurmukov) including major research trends in 2021:

- Graph Transformers
- Equivariant GNNs
- Generative Models for Molecules
- GNNs and Combinatorial Optimization
- Subgraph GNNs
- Scalable and Deep GNNs
- Knowledge Graph Representation Learning
- Generally Cool Research with GNNs

Besides that, the post describes new datasets and challenges, new courses and books, as well as new / updated open source libraries for graph representation learning.