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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.

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Cite this as

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

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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
More Authors	Ethan A. Chi Jillian Tang
Homepage	https://github.com/bjing2016/sgvae