Fast Graph Generative Model via Spectral Diffusion
- URL: http://arxiv.org/abs/2211.08892v1
- Date: Wed, 16 Nov 2022 12:56:32 GMT
- Title: Fast Graph Generative Model via Spectral Diffusion
- Authors: Tianze Luo, Zhanfeng Mo, Sinno Jialin Pan
- Abstract summary: We argue that running full-rank diffusion SDEs on the whole space hinders diffusion models from learning graph topology generation.
We propose an efficient yet effective Graph Spectral Diffusion Model (GSDM), which is driven by low-rank diffusion SDEs on the graph spectrum space.
- Score: 38.31052833073743
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generating graph-structured data is a challenging problem, which requires
learning the underlying distribution of graphs. Various models such as graph
VAE, graph GANs and graph diffusion models have been proposed to generate
meaningful and reliable graphs, among which the diffusion models have achieved
state-of-the-art performance. In this paper, we argue that running full-rank
diffusion SDEs on the whole space hinders diffusion models from learning graph
topology generation, and hence significantly deteriorates the quality of
generated graph data. To address this limitation, we propose an efficient yet
effective Graph Spectral Diffusion Model (GSDM), which is driven by low-rank
diffusion SDEs on the graph spectrum space. Our spectral diffusion model is
further proven to enjoy a substantially stronger theoretical guarantee than
standard diffusion models. Extensive experiments across various datasets
demonstrate that, our proposed GSDM turns out to be the SOTA model, by
exhibiting either significantly higher generation quality or much less
computational consumption than the baselines.
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