Graph Models
GraphSAGE
Introduced by Hamilton et al. in Inductive Representation Learning on Large GraphsGraphSAGE is a general inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data.
Image from: Inductive Representation Learning on Large Graphs
Source: Inductive Representation Learning on Large Graphs
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Node Classification | 20 | 13.07% |
| Link Prediction | 10 | 6.54% |
| Graph Attention | 9 | 5.88% |
| Graph Representation Learning | 8 | 5.23% |
| Graph Classification | 8 | 5.23% |
| General Classification | 8 | 5.23% |
| Graph Learning | 6 | 3.92% |
| Recommendation Systems | 6 | 3.92% |
| Classification | 5 | 3.27% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
