2024
Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning
ICML 2024poster
Although most graph neural networks (GNNs) can operate on graphs of any size, their classification performance often declines on graphs larger than those encountered during training. Existing methods insufficiently address the removal of size information from graph representations, resulting in sub-…