A new blueprint for learning on graphs

Graph Neural Networks beyond Weisfeiler-Lehman and vanilla Message Passing

Physics-inspired continuous learning models on graphs allow to overcome the limitations of traditional GNNs

Michael Bronstein
Towards Data Science
29 min readMar 3, 2022

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The message-passing paradigm has been the “battle horse” of deep learning on graphs for several years, making graph neural networks a big success in a

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DeepMind Professor of AI @Oxford. Serial startupper. ML for graphs, biochemistry, drug design, and animal communication.