Post #651

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 😉