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SGVAE: Sequential Graph Variational Autoencoder

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.

Data and Resources

Cite this as

Bowen Jing, Ethan A. Chi, Jillian Tang (2024). Dataset: SGVAE: Sequential Graph Variational Autoencoder. https://doi.org/10.57702/lgbryz3z

DOI retrieved: December 2, 2024

Additional Info

Field Value
Created December 2, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.1912.07800
Author Bowen Jing
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Ethan A. Chi
Jillian Tang
Homepage https://github.com/bjing2016/sgvae