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Make Autoregressive Great Again: Diffusion-Free Graph Generation with Next-Scale Prediction

Autoregressive models are popular generative models due to their speed and properties. However, they require an explicit sequence order, which contradicts the unordered nature of graphs. In contrast, diffusion models maintain permutation invariance and enable one-shot generation but require up to thousands of denoising steps and additional features, leading to high computational costs. Inspired by recent breakthroughs in image generation-especially the success of visual autoregressive methods-we propose MAG, a novel diffusion-free graph generation framework based on next-scale prediction. By leveraging a hierarchy of latent representations, the model progressively generates scales of the entire graph without the need for explicit node ordering. Extensive experiments on both generic and molecular graph datasets demonstrate that MAG delivers competitive performance compared to state-of-the-art methods, achieving up to three orders of magnitude in speedup during inference.

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