Models based on graphs are quite important for a lot of tasks in NLP. There is an overview from Michael Bronstein about what he is expecting for upcoming year for the Graph ML field:
1. Geometry becomes increasingly important in ML.
2. Message passing is still the dominant paradigm in GNNs.
3. Differential equations give rise to new GNN architectures.
4. Old ideas from Signal Processing, Neuroscience, and Physics get a new life.
5. Modeling complex systems requires going beyond graphs.
6. Reasoning, axiomatisation, and generalisation are still big open questions in Graph ML.
7. Graphs become increasingly popular in Reinforcement Learning, but probably still have a way to go.
8. AlphaFold 2 is a triumph of Geometric ML and a paradigm shift in structural biology.
9. Drug discovery and design benefits from GNNs and their confluence with Transformers.
10. AI-first drug discovery is increasingly using Geometric and Graph ML.
11. Quantum ML benefits from graph-based methods.
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