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Diffusion models achieve state-of-the-art performance in generating realisticobjects and have been successfully applied to images, text, and videos. Recentwork has shown that diffusion can also be defined on graphs, including graphrepresentations of drug-like molecules. Unfortunately, it remains difficult toperform conditional generation on graphs in a way which is interpretable andcontrollable. In this work, we propose GraphGUIDE, a novel framework for graphgeneration using diffusion models, where edges in the graph are flipped or setat each discrete time step. We demonstrate GraphGUIDE on several graphdatasets, and show that it enables full control over the conditional generationof arbitrary structural properties without relying on predefined labels. Ourframework for graph diffusion can have a large impact on the interpretableconditional generation of graphs, including the generation of drug-likemolecules with desired properties in a way which is informed by experimentalevidence.

GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion