Generalized Graph Variational Autoencoders: Bounded Divergences Control Posterior Collapse
Quick summary
arXiv:2609.29546v1 Announce Type: cross Abstract: The variational graph autoencoder (VGAE) regularizes its posterior toward the prior with the Kullback-Leibler divergence, a choice inherited from the variational autoencoder rather than argued for. We introduce the generalized graph variational autoencoder (GGVA), which replaces that term with any member of the R\'enyi-Tsallis family of order $q$ while leaving every other part of the model untouched. Both members admit closed forms for diagonal Gaussians and both recover the KL exactly as $q \to 1$, so the VGAE is the $q=1$ arm of our own model
Key takeaways
- arXiv:2609.29546v1 Announce Type: cross Abstract: The variational graph autoencoder (VGAE) regularizes its posterior toward the prior with the Kullback-Leibler divergence, a choice inherited from the variational autoencoder rather than argued for.
- We introduce the generalized graph variational autoencoder (GGVA), which replaces that term with any member of the R\'enyi-Tsallis family of order $q$ while leaving every other part of the model untouched.
- Both members admit closed forms for diagonal Gaussians and both recover the KL exactly as $q \to 1$, so the VGAE is the $q=1$ arm of our own model
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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