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
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Usage Over Time
Components
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
