Graph Models
Dual Graph Convolutional Networks
A dual graph convolutional neural network jointly considers the two essential assumptions of semi-supervised learning: (1) local consistency and (2) global consistency. Accordingly, two convolutional neural networks are devised to embed the local-consistency-based and global-consistency-based knowledge, respectively.
Description and image from: Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Aspect-Based Sentiment Analysis (ABSA) | 1 | 33.33% |
| Dependency Parsing | 1 | 33.33% |
| Sentiment Analysis | 1 | 33.33% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
