[Submitted on 3 Nov 2026]
Title:Constraint-Preserving Flow Matching for Spectral Graph Generation
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Abstract:Spectral graph generation models graphs through the eigenvalues and eigenvectors of their Laplacians, providing a compact and continuous alternative to direct graph construction. However, these eigensystems must retain the structural properties of graph Laplacians. Existing approaches preserve eigenvector orthonormality but do not enforce the Laplacian nullspace constraint, potentially producing eigensystems inconsistent with Laplacian structure. To address this, we introduce Laplacian-Constrained Stiefel Flow Matching (LaSFM), which preserves the nullspace condition alongside eigenvector orthonormality and positive eigenvalues during generation. We characterize the underlying constrained Stiefel manifold and formulate flow matching on it. We further extend LaSFM to property-guided generation, steering samples toward desired graph properties while maintaining these constraints. Experiments demonstrate improved performance on both spectral and graph-level metrics, along with better alignment with target properties.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2611.05436 [cs.LG]
(or arXiv:2611.05436v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.05436
arXiv-issued DOI via DataCite
Submission history
From: Anita Yang [view email] [v1] Tue, 3 Nov 2026 09:51:49 UTC (1,142 KB)
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