SOURCE-LINKED INTELLIGENCE

ProtoGuide: Prototype-Driven Guidance for Class-Conditional Graph Generation

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

Discrete diffusion models are a prominent family for graph generation, but standard class-conditional mechanisms embed the class signal in the denoiser during training, tying the conditioning mechanism to the trained model. Classifier guidance avoids this coupling in continuous domains by steering a frozen model with a classifier's gradient, but discrete graph diffusion samples discrete edge states, so gradients cannot propagate through the sampled graph. We introduce ProtoGuide, a post-hoc, backbone-agnostic framework that recovers an analogous mechanism. At each reverse step the denoiser's p

Read original source ↗ Open in workspace

recordType

paper

region

Global