Diffusion-based graph generative methods
Hongyang Chen Can Xu Lingyu Zheng Qiang Zhang Xuemin Lin
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.