Scaling Novel Graph Generation via Lightweight Structure-Guided Autoregressive Models 事件

Scaling Novel Graph Generation via Lightweight Structure-Guided Autoregressive Models arXiv:2606.04287v1 Announce Type: cross Abstract: Generating realistic and diverse graphs is a key problem in machine learning, with applications in molecular discovery, circuit design, cybersecurity, and beyond. However, current graph generative models remain limited by scalability and novelty. Diffusion-based methods often require costly full-adjacency operations and long denoising chains, while many autoreg