Variational Graph Auto-Encoders
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| Authors | Thomas N. Kipf, Max Welling |
| Journal/Conference Name | Methods |
| Paper Category | Artificial Intelligence |
| Paper Abstract | 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. |
| Date of publication | 2016 |
| Code Programming Language | Multiple |
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