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SGVAE: Sequential Graph Variational Autoencoder
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Paper
SGVAE: Sequential Graph Variational Autoencoder
2019
Overview
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.
Publisher
Cornell University Library, arXiv.org
Subject
