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Found 3 papers, 2 papers with code
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
We start with an empirical study that shows that a decrease in within-class variability is also prevalent in the node-wise classification setting, however, not to the extent observed in the instance-wise case.
Randomized Schur Complement Views for Graph Contrastive Learning
We introduce a randomized topological augmentor based on Schur complements for Graph Contrastive Learning (GCL).
Ranked #2 on
Graph Classification
on PROTEINS
Neural Collapse: A Review on Modelling Principles and Generalization
Deep classifier neural networks enter the terminal phase of training (TPT) when training error reaches zero and tend to exhibit intriguing Neural Collapse (NC) properties.
