GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance

Excerpt

arXiv:2605.16668v2 Announce Type: replace-cross Abstract: 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