Graph Representation Learning
Graph sampling based inductive learning method
Introduced by Zeng et al. in GraphSAINT: Graph Sampling Based Inductive Learning MethodScalable method to train large scale GNN models via sampling small subgraphs.
Source: GraphSAINT: Graph Sampling Based Inductive Learning MethodPapers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Node Classification | 4 | 30.77% |
| graph partitioning | 1 | 7.69% |
| Reinforcement Learning (RL) | 1 | 7.69% |
| Graph Learning | 1 | 7.69% |
| whole slide images | 1 | 7.69% |
| Link Prediction | 1 | 7.69% |
| Graph Attention | 1 | 7.69% |
| Graph Embedding | 1 | 7.69% |
| Graph Representation Learning | 1 | 7.69% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
