Papers › Variational Graph Auto-Encoders
Variational Graph Auto-Encoders
Thomas N. Kipf, Max Welling
We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.
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Code
Syntology Ran 7 of 13 code samples harvested from 4 repositories linked to this paper; 6 have no recorded run. Of those that ran: 3 ran · honoured contract; 3 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
13 samples harvested; 7 ran; 3 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 0 of the 13 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Clustering | Citeseer | GAE | ACC | 40.8 | #8 of 9 | Archive leaderboard | report |
| Graph Clustering | Cora | GAE | ACC | 59.6 | #8 of 9 | Archive leaderboard | report |
| Graph Clustering | Pubmed | VGAE | ACC | 65.48 | #6 of 7 | Archive leaderboard | report |
| Link Prediction | Citeseer | Variational graph auto-encoders | ACC | 91.4 | #13 of 13 | Archive leaderboard | report |
| Link Prediction | Cora | Variational graph auto-encoders | ACC | 92.0 | #13 of 13 | Archive leaderboard | report |
| Link Prediction | Pubmed | Variational graph auto-encoders | ACC | 97.1 | #13 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: VGAE
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