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Diffusion-Based Graph Generative Methods
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DOI：10.1109/TKDE.2024.3466301.png)
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摘要
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Being the most cutting-edge generative methods, diffusion methods have shown great advances in wide generation tasks. Among them, graph generation attracts significant research attention for its broad application in real life. In our survey, we systematically and comprehensively review on diffusion-based graph generative methods. We first make a review on three mainstream paradigms of diffusion methods, which are denoising diffusion probabilistic models, score-based genrative models, and stochastic differential equations. Then we further categorize and introduce the latest applications of diffusion models on graphs. In the end, we point out some limitations of current studies and future directions of future explorations.
Keyword:
diffusion models
graph neural networks
Generative methods
molecule generation
molecule generation
motion generation
motion generation
molecule generation
motion generation
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
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