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 | 13.33% |
| Node Classification | 3 | 10.00% |
| Computed Tomography (CT) | 2 | 6.67% |
| Style Transfer | 2 | 6.67% |
| General Classification | 2 | 6.67% |
| Music Generation | 1 | 3.33% |
| Audio Generation | 1 | 3.33% |
| Semantic Similarity | 1 | 3.33% |
| Semantic Textual Similarity | 1 | 3.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
