Graph Models
Gated Graph Sequence Neural Networks
Introduced by Li et al. in Gated Graph Sequence Neural NetworksGated Graph Sequence Neural Networks (GGS-NNs) is a novel graph-based neural network model. GGS-NNs modifies Graph Neural Networks (Scarselli et al., 2009) to use gated recurrent units and modern optimization techniques and then extend to output sequences.
Source: Li et al.
Image source: Li et al.
Source: Gated Graph Sequence Neural Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Graph Neural Network | 3 | 23.08% |
| Parameter Prediction | 1 | 7.69% |
| Clustering | 1 | 7.69% |
| Management | 1 | 7.69% |
| Method name prediction | 1 | 7.69% |
| Question Generation | 1 | 7.69% |
| Visual Relationship Detection | 1 | 7.69% |
| Drug Discovery | 1 | 7.69% |
| Graph Classification | 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 |
