arXiv Machine Learning October 23, 2026 By Daniele Micheletti, Federica Zoe Ricci, Erik B. Sudderth

xVGAE: A Hierarchical Variational Graph Autoencoder for Exchangeable Graphs

Read the original on arXiv Machine Learning →

The paper introduces xVGAE, a hierarchical variational graph autoencoder designed for exchangeable graphs. Leveraging the Aldous-Hoover theorem, the model uses a graph-level latent variable, exchangeable node-specific latent variables, and a graphon function to learn and generate graph distributions efficiently. Experiments demonstrate that xVGAE produces high-quality graphs quickly while capturing key structural properties of the training data.

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