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arXiv · 1912.07800

SGVAE: Sequential Graph Variational Autoencoder

Bowen Jing·Ethan A. Chi·Jillian Tang
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Abstract

Generative models of graphs are well-known, but many existing models are limited in scalability and expressivity. We present a novel sequential graphical variational autoencoder operating directly on graphical representations of data. In our model, the encoding and decoding of a graph as is framed as a sequential deconstruction and construction process, respectively, enabling the the learning of a latent space. Experiments on a cycle dataset show promise, but highlight the need for a relaxation of the distribution over node permutations.

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Bowen Jing, Ethan A. Chi, Jillian Tang. 2019-12-17. SGVAE: Sequential Graph Variational Autoencoder. https://arxiv.org/abs/1912.07800

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