Continuous Latent Spaces Sampling for Graph Autoencoder
This paper proposes colaGAE, a self-supervised learning framework for graph-structured data. While graph autoencoders (GAEs) commonly use graph reconstruction as a pretext task, this simple approach often yields poor model performance. To address this issue, colaGAE employs mutual isomorphism as a p...
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| Hauptverfasser: | , , , , |
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| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
MDPI AG
2023-05-01
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| Schriftenreihe: | Applied Sciences |
| Schlagworte: | |
| Online-Zugang: | https://www.mdpi.com/2076-3417/13/11/6491 |
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