GraphRCG: Self-conditioned Graph Generation Framework

In this work, the authors propose a novel self-conditioned graph generation framework that explicitly models graph distributions and utilizes them to guide the generation process. By capturing graph distributions through representations and employing self-conditioned guidance, the framework enhances the fidelity of generated graphs.