Learnable 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 NetworksPaper | Code | Results | Date | Stars |
---|
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% |
Component | Type |
|
---|---|---|
🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |