Graph generative models perform well on small structured data but struggle to scale to large, complex structures. Hierarchical approaches improve scalability but often ignore node and edge features, which are critical in real-world applications, particularly for hypergraphs that model higher-order relationships. In this paper, we propose FAHNES (feature-aware (hyper)graph generation via next-scale prediction), a hierarchical framework that jointly generates topology and features for graphs and hypergraphs. FAHNES builds multi-scale representations through node coarsening and localized expansion, guided by a novel hierarchical scale encoding that controls granularity and ensures cross-scale consistency. Experiments on synthetic, 3D mesh, and graph point cloud datasets demonstrate competitive or state-of-the-art performance while uniquely scaling to featured large-scale graphs and hypergraphs. Our code is open source

翻译：图生成模型在小规模结构化数据上表现良好，但在扩展到大规模复杂结构时面临挑战。层次化方法虽然提升了可扩展性，但往往忽略节点和边的特征——这些特征在真实应用（尤其是建模高阶关系的超图）中至关重要。本文提出FAHNES（基于下一尺度预测的特征感知（超）图生成），一种联合生成图/超图拓扑结构与特征的层次化框架。FAHNES通过节点粗化与局部扩展构建多尺度表征，并受新型层次化尺度编码引导，该编码可控制粒度并确保跨尺度一致性。在合成数据集、三维网格数据及图点云数据集上的实验表明，该方法在实现竞争力或最优性能的同时，能独特地扩展至带特征的大规模图与超图。我们的代码已开源。