Graph Models
ChebNet
Introduced by Defferrard et al. in Convolutional Neural Networks on Graphs with Fast Localized Spectral FilteringChebNet involves a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs.
Description from: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Source: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Node Classification | 3 | 37.50% |
| GPR | 2 | 25.00% |
| Graph Learning | 1 | 12.50% |
| Node Classification on Non-Homophilic (Heterophilic) Graphs | 1 | 12.50% |
| Skeleton Based Action Recognition | 1 | 12.50% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
