通过分层离散流匹配，加速图生成并降低计算开销。
Hierarchical Discrete Flow Matching for Graph Generation
- 分层结构减少节点对评估数量，降低计算复杂度。
- 采用离散流匹配，显著减少去噪迭代次数。
- 适合需要高效生成大规模图的场景。
基于去噪的模型（如扩散模型和流匹配）在图生成领域取得了显著进展。然而，这类模型仍受两大根本限制：计算成本随节点数呈二次增长，生成过程需大量函数求值。本文提出一种新型分层生成框架，通过减少需评估的节点对数量，并采用离散流匹配大幅降低去噪迭代次数。实验表明，该方法更有效地捕捉图分布，同时显著缩短生成时间。
原文摘要 · Abstract (English)
Denoising-based models, including diffusion and flow matching, have led to substantial advances in graph generation. Despite this progress, such models remain constrained by two fundamental limitations: a computational cost that scales quadratically with the number of nodes and a large number of function evaluations required during generation. In this work, we introduce a novel hierarchical generative framework that reduces the number of node pairs that must be evaluated and adopts discrete flow matching to significantly decrease the number of denoising iterations. We empirically demonstrate that our approach more effectively captures graph distributions while substantially reducing generation time.
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