Existing graph generative models often face a critical trade-off between sample quality and generation speed. We introduce Autoregressive Noisy Filtration Modeling (ANFM), a flexible autoregressive framework that addresses both challenges. ANFM leverages filtration, a concept from topological data analysis, to transform graphs into short sequences of subgraphs. We identify exposure bias as a potential hurdle in autoregressive graph generation and propose noise augmentation and reinforcement learning as effective mitigation strategies, which allow ANFM to learn both edge addition and deletion operations. This unique capability enables ANFM to correct errors during generation by modeling non-monotonic graph sequences. Our results show that ANFM matches state-of-the-art diffusion models in quality while offering over 100 times faster inference, making it a promising approach for high-throughput graph generation. The source code is publicly available at https://github.com/BorgwardtLab/anfm .

翻译：现有图生成模型常面临样本质量与生成速度之间的关键权衡。本文提出自回归噪声滤波建模（ANFM），一种灵活的自回归框架，旨在同时应对这两项挑战。ANFM利用拓扑数据分析中的滤波概念，将图转化为短序列的子图结构。我们指出自回归图生成中可能存在的曝光偏差问题，并提出噪声增强与强化学习作为有效的缓解策略，使ANFM能够同时学习边的添加与删除操作。这一独特能力使得ANFM能够通过建模非单调图序列来修正生成过程中的错误。实验结果表明，ANFM在质量上媲美最先进的扩散模型，同时提供超过100倍的推理加速，为高通量图生成提供了极具前景的解决方案。源代码已公开于 https://github.com/BorgwardtLab/anfm。