Related papers: Diffusion-based Graph Generative Methods

Diffusion-based Graph Generative Methods

URL: http://arxiv.org/abs/2401.15617v2
Date: Tue, 16 Jul 2024 12:21:30 GMT
Title: Diffusion-based Graph Generative Methods
Authors: Hongyang Chen, Can Xu, Lingyu Zheng, Qiang Zhang, Xuemin Lin,
Abstract summary: 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 models, score-based genrative models, and differential equations. In the end, we point out some limitations of current studies and future directions of future explorations.
Score: 51.04666253001781
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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