Graph Embeddings
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation Learning
DBGAN is a method for graph representation learning. Instead of the widely used normal distribution assumption, the prior distribution of latent representation in DBGAN is estimated in a structure-aware way, which implicitly bridges the graph and feature spaces by prototype learning.
Source: Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation Learning
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Deblurring | 1 | 33.33% |
| Image Deblurring | 1 | 33.33% |
| Graph Representation Learning | 1 | 33.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
