Graph Embeddings
Laplacian EigenMap
Introduced by Niyogi in Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Node Classification | 17 | 6.07% |
| Graph Representation Learning | 14 | 5.00% |
| Graph Learning | 13 | 4.64% |
| Link Prediction | 12 | 4.29% |
| Graph Classification | 11 | 3.93% |
| Graph Regression | 9 | 3.21% |
| Drug Discovery | 8 | 2.86% |
| Molecular Property Prediction | 6 | 2.14% |
| Property Prediction | 6 | 2.14% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
