Diffusion-based Graph Generative Methods
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. The summary of existing methods metioned in this survey is in https://github.com/zhejiangzhuque/Diffusion-based-Graph-Generative-Methods.
Code (1)
Tasks
DenoisingGraph GenerationSurveyMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Generative Diffusion Models on Graphs: Methods and Applications
Diffusion models, as a novel generative paradigm, have achieved remarkable success in various image generation tasks such as image inpainting, image-to-text translation, and video generation. Graph generation is a crucia…
DenoisingGraph GenerationImage GenerationImage Inpainting+2A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material
Diffusion models have become a new SOTA generative modeling method in various fields, for which there are multiple survey works that provide an overall survey. With the number of articles on diffusion models increasing e…
ArticlesSurveyEfficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
Diffusion-based generative graph models have been proven effective in generating high-quality small graphs. However, they need to be more scalable for generating large graphs containing thousands of nodes desiring graph …
DenoisingGraph GenerationAutoregressive Diffusion Model for Graph Generation
Diffusion-based graph generative models have recently obtained promising results for graph generation. However, existing diffusion-based graph generative models are mostly one-shot generative models that apply Gaussian d…
DenoisingGraph GenerationmodelPSyDUCK: Training-Free Steganography for Latent Diffusion
Recent advances in generative AI have opened promising avenues for steganography, which can securely protect sensitive information for individuals operating in hostile environments, such as journalists, activists, and wh…
Denoising