In real world domains, most graphs naturally exhibit a hierarchical structure. However, data-driven graph generation is yet to effectively capture such structures. To address this, we propose a novel approach that recursively generates community structures at multiple resolutions, with the generated structures conforming to training data distribution at each level of the hierarchy. The graphs generation is designed as a sequence of coarse-to-fine generative models allowing for parallel generation of all sub-structures, resulting in a high degree of scalability. Our method demonstrates generative performance improvement on multiple graph datasets.

翻译：在现实世界领域中，大多数图自然地展现出层级结构。然而，数据驱动的图生成方法尚未能有效捕捉此类结构。为解决这一问题，我们提出了一种新颖方法，该方法在多个分辨率上递归生成社区结构，且生成的每一层级结构均符合训练数据分布。图生成过程被设计为从粗到细的生成模型序列，允许所有子结构并行生成，从而实现了高度可扩展性。我们的方法在多个图数据集上展示了生成性能的提升。