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2016

Variational Graph Auto-Encoders

Kipf, Thomas N., Welling, Max

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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.

Variational Graph Auto-Encoders · Around

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