GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
We introduce GraViti, a transformer-based graph-level variational autoencoder that encodes entire graphs into single, fixed-dimensional latent vectors rather than per-node embeddings, yielding a graph-level latent space that supports smooth interpolation, property-guided search, and other downstream tasks beyond the reach of node-level approaches. GraViti achieves state-of-the-art reconstruction accuracy on large molecular graph datasets while offering competitive single-step generative performance. Our central finding concerns where permutation invariance is needed in such a model, and where it is not. The encoder remains permutation-invariant throughout, ensuring the model generalizes t...
| Authors | Roman Bresson, Konstantinos Divriotis, Johannes F. Lutzeyer et al. |
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| Primary category | cs.LG |
| Published | 15 May 2026 |
| Last updated at source | 5 October 2026 |
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