Methods › Graphs › Graph Embeddings › DBGAN
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation Learning
DBGAN
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
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 archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Deblurring by Realistic Blurring 4 Apr 2020 · 1 repository · arXiv:2004.01860
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Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation Learning 4 Dec 2019 · 1 repository · arXiv:1912.01899
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Deblurring | 1 |
| Generative Adversarial Network | 1 |
| Graph Representation Learning | 1 |
| Image Deblurring | 1 |
| Representation Learning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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