Generalized Graph Variational Autoencoders: Bounded Divergences Control Posterior Collapse

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 Renyi-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 o 1, so the VGAE is the q=1 arm of our own model rather than a separate baseline, and any measured difference is attributable to a single scalar. Our analysis identifies boundedness, not the order, as the operative property: for q<1 the Tsallis divergence is bounded above by 1/(1-q), independently of the latent width, whereas the KL and the Renyi divergence of the same order are unbounded. On ten graphs spanning three synthetic families, a social network, three citation networks, a connectome, a power grid and a road network, q moves the retained posterior information by up to 49imes relative to the VGAE, while the Renyi arm at the same order stays within 1.02-1.30imes of it on all six larger real graphs (isolating the bound as the cause). The retained information is usable: probing the frozen embedding for node class, a label absent from the objective, gives GGVA up to +0.14 macro-F1 over the VGAE on CiteSeer, with the Renyi control again tracking the VGAE. We also report what the design was built to expose: none of this reaches held-out link-prediction accuracy on any of the six larger real graphs, and boundedness delays posterior collapse rather than preventing it.

Source: arXiv cs.AI | 2026-09-25