Research map / Graph learning / Graph self-supervised learning
Link Prediction with Persistent Homology: An Interactive View
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use pairwise rather than nodewise topological features (ablation) | TLC-GNN · PubMed | ROC-AUC: 96.91±0.002 -> 97.03±0.001 | Helped |
| Use pairwise rather than nodewise topological features (ablation) | TLC-GNN · Photo | ROC-AUC: 97.91±0.001 -> 98.23±0.001 | Helped |
| Use pairwise rather than nodewise topological features (ablation) | TLC-GNN · Computers | ROC-AUC: 97.03±0.001 -> 97.90±0.001 | Helped |
| Use Ollivier-Ricci curvature rather than DRNL as the distance filter (ablation) | TLC-GNN · PubMed, Photo, Computers | ROC-AUC: 96.89±0.002 -> 97.03±0.001; 97.61±0.003 -> 98.23±0.001; 97.23±0.003 -> 97.90±0.001 | Mixed |
| Add pairwise topological features to the link predictor | TLC-GNN · PPI samples 1–5 | ROC-AUC | Mixed |
| Replace matrix reduction with the graph-based faster persistence algorithm | Extended persistence computation · PubMed, Photo, Computers | running time per edge: 0.0068 -> 0.0027; 1.6557 -> 1.1176; 4.6531 -> 2.7033 | Helped |
Closest papers by meaning
- Graphcode: Learning from multiparameter persistent homology using graph neural networks (NeurIPS 2024 · similarity 0.91)
- TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction (ICML 2025 · similarity 0.90)
- Boosting Graph Pooling with Persistent Homology (NeurIPS 2024 · similarity 0.89)
- Positional Encoding meets Persistent Homology on Graphs (ICML 2025 · similarity 0.88)
- A Topological Perspective on Demystifying GNN-Based Link Prediction Performance (ICLR 2024 · similarity 0.88)
- Graph Persistence goes Spectral (NeurIPS 2025 · similarity 0.87)
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