Nicolas Keriven

Unconditional graph generation can now capture complex node heterogeneity and higher-order structures by jointly diffusing graph edges and latent node sociability, bypassing the Erd\H{o}s--R\'enyi graph limitation.

Discrete graph diffusion models, believed to generate novel graphs, are actually memorizing training data, with this bias appearing only when conditioned on corrupted training examples.

CoRe-GNN achieves competitive accuracy on long-range graph tasks while dramatically improving memory efficiency through a novel dual-message passing approach.

Identifying universal behaviors in autoencoders could enable the creation of efficient image compression models that rival their complex counterparts without sacrificing performance.