Graph Models
Learnable graph convolutional layer
Introduced by Gao et al. in Large-Scale Learnable Graph Convolutional NetworksLearnable graph convolutional layer (LGCL) automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs.
Description and image from: Large-Scale Learnable Graph Convolutional Networks
Source: Large-Scale Learnable Graph Convolutional Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Node Classification | 2 | 33.33% |
| Continual Learning | 1 | 16.67% |
| Link Prediction | 1 | 16.67% |
| 3D Shape Reconstruction | 1 | 16.67% |
| Document Classification | 1 | 16.67% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
