Variational Graph Auto-Encoders

Thomas N. Kipf; Max Welling

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arXiv · 1611.07308

Variational Graph Auto-Encoders

Thomas N. Kipf·Max Welling

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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.

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BibTeX RIS

Thomas N. Kipf, Max Welling. 2016-11-21. Variational Graph Auto-Encoders. https://arxiv.org/abs/1611.07308

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