用结构先验增强流模型，生成更符合真实图结构的分子与合成图。
Flowette: Flow Matching with Graphette Priors for Graph Generation
- 基于图神经网络的流匹配框架，学习带属性节点与边的图表示速度场。
- 在多个基准上达领先性能，尤其在分子图生成任务中显著提升结构一致性。
- 引入图小样（graphettes）作为可调控的结构先验，支持环、星、树等模式建模。
我们研究具有重复子图模式的图生成问题。提出Flowette，一种基于连续流匹配的图生成框架，利用基于图神经网络的Transformer学习图表示的速度场，包含节点和边属性。通过基于最优传输的耦合实现拓扑感知对齐，并通过正则化确保全局结构一致性。为引入领域驱动的结构先验，提出图小样（graphettes），一种通过受控结构编辑泛化图论（graphons）的概率图结构模型，支持环、星形、树等子图模式。理论分析了框架的耦合性、不变性与结构特性，在合成数据与分子基准上进行评估，并通过受控消融实验分离结构先验、最优传输耦合与正则项的贡献。总体表现优异，在多个基准上达到先进水平，验证了结合结构先验与流式训练对复杂图分布建模的有效性。
原文摘要 · Abstract (English)
We study generative modeling of graphs with recurring subgraph motifs. We propose Flowette, a continuous flow matching framework that employs a graph neural network-based transformer to learn a velocity field over graph representations with node and edge attributes. Our model promotes topology-aware alignment through optimal transport-based coupling and encourages global structural coherence through regularisation. To incorporate domain-driven structural priors, we introduce graphettes, a new probabilistic family of graph structure models that generalize graphons via controlled structural edits for motifs such as rings, stars, and trees. We theoretically analyze the coupling, invariance, and structural properties of the framework, evaluate it on synthetic and molecular benchmarks, and isolate the contributions of the structural prior, the optimal-transport coupling, and the regularisation terms through controlled ablations. Flowette achieves competitive performance overall, attaining state-of-the-art results on several metrics across multiple benchmarks, highlighting the effectiveness of combining structural priors with flow-based training for modeling complex graph distributions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。