Latent Flow Matching for Molecular Graph Generation

Excerpt

arXiv:2610.06468v1 Announce Type: cross Abstract: Modern graph generative models typically operate directly in the discrete graph space, explicitly generating node and edge variables, which can become costly as graphs grow. In this paper, we perform generation explicitly on latent representations of entire graphs obtained from a pretrained Variational Autoencoder with high reconstruction fidelity. The generated representations, obtained through flow matching, are then decoded only at the final s