Graph Embeddings
VERtex Similarity Embeddings
Introduced by Tsitsulin et al. in VERSE: Versatile Graph Embeddings from Similarity MeasuresVERtex Similarity Embeddings (VERSE) is a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network.
Source: Tsitsulin et al.
Image source: Tsitsulin et al.
Source: VERSE: Versatile Graph Embeddings from Similarity Measures
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Link Prediction | 4 | 6.78% |
| Anatomy | 3 | 5.08% |
| Translation | 3 | 5.08% |
| Computed Tomography (CT) | 3 | 5.08% |
| Node Classification | 3 | 5.08% |
| Benchmarking | 2 | 3.39% |
| Retrieval | 2 | 3.39% |
| Sentence | 2 | 3.39% |
| Decision Making | 2 | 3.39% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
