Research map / Graph learning / GNN architectures
Approximately Equivariant Graph Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| decrease the coarsened patch size, weakening the imposed symmetry | G-Net · MNIST and FashionMNIST image inpainting | empirical risk | Mixed |
| use reflection symmetry on the coarsened grid instead of trivial coarsened-graph symmetry | G-Net · MNIST and FashionMNIST image inpainting | empirical risk | Helped |
| use 2 clusters rather than 9 clusters or the full permutation group | G-Net(gc) · METR-LA traffic flow prediction | MAE | Helped |
| use Relax-S16 instead of S16 | G-Net(gc+ew) · Human3.6M | MPJPE | Helped |
| use Relax-S16 instead of S16 | G-Net(gc+ew) · Human3.6M | P-MPJPE | Helped |
Closest papers by meaning
- Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters (NeurIPS 2024 · similarity 0.92)
- Improving Equivariant Networks with Probabilistic Symmetry Breaking (ICLR 2025 · similarity 0.91)
- Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models (NeurIPS 2025 · similarity 0.90)
- On the hardness of learning under symmetries (ICLR 2024 · similarity 0.90)
- Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks? (NeurIPS 2024 · similarity 0.89)
- A Characterization Theorem for Equivariant Networks with Point-wise Activations (ICLR 2024 · similarity 0.89)
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