Kipf & Welling strike back
Graph Neural Networks as gradient flows
Under a few simple constraints, Graph Neural Networks can be derived as gradient flows minimising a learnable energy that describes attractive and repulsive forces in the feature space. This formalism allows the interpretation of GNNs as physical systems and sheds light onto how the interaction between the graph…
Published in
18 min readOct 14, 2022
