Research map / Graph learning / GNN architectures
Capturing Graphs with Hypo-Elliptic Diffusions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add increments of node attributes (Diff) (ablation) | G2T(A)N · G2T(A)N | accuracy (%): 80.1 -> 81.9 | Helped |
| add increments of node attributes (Diff) (ablation) | G2T(A)N · G2T(A)N | accuracy (%): 78.2 -> 78.0 | No clear effect |
| add zero point prepended to sequences (ZeroStart) (ablation) | G2T(A)N · G2T(A)N | accuracy (%): 79.5 -> 81.9 | Helped |
| add zero point prepended to sequences (ZeroStart) (ablation) | G2T(A)N · G2T(A)N | accuracy (%): 77.7 -> 78.0 | No clear effect |
| add optimization over algebra embedding (AlgOpt) (ablation) | G2T(A)N · G2T(A)N | accuracy (%): 81.6 -> 81.9 | No clear effect |
| add optimization over algebra embedding (AlgOpt) (ablation) | G2T(A)N · G2T(A)N | accuracy (%): 77.5 -> 78.0 | Helped |
| add JK-connections (ablation) | G2T(A)N · G2T(A)N | accuracy (%): 82.1 -> 81.9 | No clear effect |
| add JK-connections (ablation) | G2T(A)N · G2T(A)N | accuracy (%): 77.6 -> 78.0 | No clear effect |
| add skip-connections (ablation) | G2T(A)N · G2T(A)N | accuracy (%): 81.8 -> 81.9 | No clear effect |
| add layer normalization (ablation) | G2T(A)N · G2T(A)N | accuracy (%): 79.8 -> 78.0 | Hurt |
| add layer normalization (ablation) | G2T(A)N · G2T(A)N | accuracy (%): 81.6 -> 81.9 | No clear effect |
| use attention pooling instead of mean pooling (ablation) | G2T(A)N · G2T(A)N | accuracy (%): 82.4 -> 81.9 | No clear effect |
Closest papers by meaning
- Diffusion Improves Graph Learning (NeurIPS 2019 · similarity 0.91)
- A Generalized Neural Diffusion Framework on Graphs (AAAI 2024 · similarity 0.90)
- A Fractional Graph Laplacian Approach to Oversmoothing (NeurIPS 2023 · similarity 0.90)
- Learning to Approximate Adaptive Kernel Convolution on Graphs (AAAI 2024 · similarity 0.89)
- Hyperbolic Geometric Latent Diffusion Model for Graph Generation (ICML 2024 · similarity 0.88)
- Generating Graphs via Spectral Diffusion (ICLR 2025 · similarity 0.87)
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