What does 2022 hold for Geometric & Graph ML?

Michael Bronstein and Petar Veličković released a huge post summarizing the state of Graph ML in 2021 and predicting possible breakthroughs in 2022. Even more, the authors conducted a large-scale community study and interviewed many prominent researchers discussing 11 key aspects:

- Rising importance of geometry in ML
- Message passing GNNs are still dominating
- Differential equations power new GNN architectures
- Old ideas from signal processing, neuroscience, and physics strike back
- Modeling complex systems with higher-order structures
- Reasoning, axiomatisation, and generalisation are still challenging
- Graphs in Reinforcement Learning
- AlphaFold 2 is a paradigm shift in structural biology
- Progress in graph transformer architectures
- Drug discovery with Geometric and Graph ML
- Quantum ML + graph-based methods

And 130 references 😉