Cometh: A continuous-time discrete-state graph diffusion model
Keywords: Graph generation, discrete diffusion, molecule generation
TL;DR: We introduce a continuous-time discrete-state diffusion model for graph generation. It outperforms its discrete-time counterpart on synthetic and molecular benchmarks.
Abstract: Discrete-state denoising diffusion models led to state-of-the-art performance in graph generation, especially in the molecular domain. Recently, they have been transposed to continuous time, allowing more flexibility in the reverse process and a better trade-off between sampling efficiency and quality. Here, to leverage the benefits of both approaches, we propose Cometh, a continuous-time discrete-state graph diffusion model, tailored to the specificities of graph data. In addition, we also successfully replaced the set of structural encodings previously used in the discrete graph diffusion model with a single random-walk-based encoding, providing a simple and principled way to boost the model's expressive power. Empirically, we show that integrating continuous time leads to significant improvements across various metrics over state-of-the-art discrete-state diffusion models on a large set of molecular and non-molecular benchmark datasets. In terms of VUN samples, Cometh obtains a near-perfect performance of $99.5$% on the planar graph dataset and outperforms DiGress by $12.6$% on the large GuacaMol dataset.
Supplementary Material: zip
Primary Area: generative models
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Submission Number: 11387
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