Unifying Graph Neural Networks Through a Common Layer Equation
The paper proposes a seven-part layer equation meant to make GNN architectures comparable in one shared framework.
The equation separates where information propagates from what messages are carried. The authors map canonical layers and more than 200 architectures into that design space across message passing, attention, spectral, global, relational, higher-order, and geometric variants. They also use the decomposition to state component-level limits around dependency support and global mixing. HF Daily Papers' note
The equation separates where information propagates from what messages are carried. The authors map canonical layers and more than 200 architectures into that design space across message passing, attention, spectral, global, relational, higher-order, and geometric variants. They also use the decomposition to state component-level limits around dependency support and global mixing. HF Daily Papers' note
score 4