Artificial Intelligence

    

Score-Based Graph Generative Models with Sublinear Spectral Density Estimation

Authors: Tianqi Zhu

We consider score-based generative models for graphs and propose to enhance them with a sublinear-time spectral density estimationmodule. Our method computes a compact spectral summary of the graph Laplacian via randomized Chebyshev moments, and uses thissummary to condition the latent diffusion process and its noise schedule. This yields a spectrum-aware score-based graph generativemodel that can adapt its diffusion dynamics to the structural properties of the input graphs, while avoiding expensive eigenvaluedecompositions

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[v1] 2025-12-30 03:05:02

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