Flowette：结合 graphette 先验的流匹配图生成方法

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Abstract: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 preserves structural fidelity through endpoint-aware and chemistry-aware 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.

Comments:	61 Pages
Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2602.23566 [cs.LG]
	(or arXiv:2602.23566v4 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2602.23566 arXiv-issued DOI via DataCite
Journal reference:	40th Conference on Neural Information Processing Systems (NeurIPS 2026)

Submission history

From: Asiri Wijesinghe [view email]
[v1] Fri, 27 Feb 2026 00:22:21 UTC (13,421 KB)
[v2] Fri, 15 May 2026 05:40:18 UTC (14,114 KB)
[v3] Mon, 18 May 2026 02:44:07 UTC (14,114 KB)
[v4] Mon, 26 Oct 2026 00:40:01 UTC (16,392 KB)