Graph generation addresses the problem of generating new graphs that have a data distribution similar to real-world graphs. While previous diffusion-based graph generation methods have shown promising results, they often struggle to scale to large graphs. In this work, we propose ARROW-Diff (AutoRegressive RandOm Walk Diffusion), a novel random walk-based diffusion approach for efficient large-scale graph generation. Our method encompasses two components in an iterative process of random walk sampling and graph pruning. We demonstrate that ARROW-Diff can scale to large graphs efficiently, surpassing other baseline methods in terms of both generation time and multiple graph statistics, reflecting the high quality of the generated graphs.

翻译：图生成旨在生成与真实世界图数据分布相似的新图。尽管先前的基于扩散的图生成方法已展现出有希望的结果，但它们通常难以扩展至大规模图。在本工作中，我们提出了ARROW-Diff（自回归随机游走扩散），一种新颖的基于随机游走的扩散方法，用于高效的大规模图生成。我们的方法在随机游走采样与图剪枝的迭代过程中包含两个组件。我们证明ARROW-Diff能够高效地扩展至大规模图，在生成时间和多项图统计指标上均超越其他基线方法，这反映了所生成图的高质量。