Random Walk Diffusion for Efficient Large-Scale Graph Generation

Tobias Bernecker, Ghalia Rehawi, Francesco Paolo Casale, Janine Knauer-Arloth, Annalisa Marsico
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Abstract

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.
高效大规模图形生成的随机漫步扩散
图形生成解决的问题是生成数据分布与现实世界图形相似的新图形。虽然之前基于扩散的图生成方法已经取得了可喜的成果,但它们往往难以扩展到大型图。在这项工作中,我们提出了 ARROW-Diff(AutoRegressiveRandOm Walk Diffusion,自动回归随机漫步扩散),这是一种基于随机漫步的新型扩散方法,可用于高效的大规模图生成。我们的方法包括随机漫步采样和图剪枝迭代过程中的两个部分。我们证明,ARROW-Diff 可以高效地扩展到大型图,在生成时间和多个图统计方面都超过了其他基线方法,反映出生成图的高质量。
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