[2604.00236] Hierarchical Discrete Flow Matching for Graph Generation

Computer Science > Machine Learning arXiv:2604.00236 (cs) [Submitted on 31 Mar 2026] Title:Hierarchical Discrete Flow Matching for Graph Generation Authors:Yoann Boget, Pablo Strasser, Alexandros Kalousis View a PDF of the paper titled Hierarchical Discrete Flow Matching for Graph Generation, by Yoann Boget and 2 other authors View PDF Abstract: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. Comments: Subjects: Machine Learning (cs.LG) Cite as: arXiv:2604.00236 [cs.LG]   (or arXiv:2604.00236v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2604.00236 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yoann Boget [view email] [v1] Tue, 31 Mar 2026 20:58:12 UTC (9,975 KB) Full-text links: Access Paper: View a PDF of the paper titled Hierarchical Discrete Flow Matching ...