Research map / Graph learning / Graph algorithms and clustering
Random Laplacian Features for Learning with Hyperbolic Space
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use HyLa features instead of the HGCN approach with SGC | Disease node classification · Disease | test accuracy (%): 74.5±0.9 -> 86.8±2.1 | Helped |
| Use HyLa features instead of HGCN | Airport node classification · Airport | test accuracy (%): 90.6±0.2 -> 95.2±0.5 | Helped |
| Use HyLa features instead of HGCN | Pubmed node classification · Pubmed | test accuracy (%): 80.3± 0.3 -> 80.3± 0.9 | No clear effect |
| Use HyLa features instead of GAT | Cora node classification · Cora | test accuracy (%): 83.0 ± 0.7 -> 82.5± 0.5 | Hurt |
| Use HyLa features with a linear graph model instead of sampling-based graph baselines | Reddit community prediction · Reddit | test F1: 93.0 -> 94.5; 93.7 -> 94.5; 95.0 -> 94.5 | Mixed |
| Use HyLa features instead of TextSGC features | Transductive text classification · R8 | test accuracy (%): 97.2±0.1 -> 96.9± 0.4 | Hurt |
| Use HyLa features instead of TextSGC features | Transductive text classification · R52 | test accuracy (%): 94.0± 0.2 -> 94.1± 0.3 | Helped |
| Use HyLa features instead of TextSGC features | Transductive text classification · Ohsumed | test accuracy (%): 68.5±0.3 -> 67.3± 0.5 | Hurt |
| Use HyLa features instead of TextGCN | Transductive text classification · MR | test accuracy (%): 76.7±0.2 -> 76.2± 0.3 | Hurt |
| Use HyLa features instead of RFF features | Inductive text classification · R8 | test accuracy (%): 97.0± 0.4 -> 97.4± 0.2 | Helped |
| Use HyLa features instead of RFF features | Inductive text classification · R52 | test accuracy (%): 92.2± 0.2 -> 93.5± 0.2 | Helped |
| Use HyLa features instead of RFF features | Inductive text classification · Ohsumed | test accuracy (%): 61.6± 0.3 -> 64.9± 0.3 | Helped |
Closest papers by meaning
- Hyperbolic Embeddings of Supervised Models (NeurIPS 2024 · similarity 0.91)
- Hyperbolic Graph Convolutional Neural Networks (NeurIPS 2019 · similarity 0.91)
- Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design (CVPR 2022 · similarity 0.91)
- Weighted Embeddings for Low-Dimensional Graph Representation (AAAI 2025 · similarity 0.91)
- Residual Hyperbolic Graph Convolution Networks (AAAI 2024 · similarity 0.89)
- Fast Hyperboloid Decision Tree Algorithms (ICLR 2024 · similarity 0.88)
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