Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.15617 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910528042958848 |
|---|---|
| author | Chen, Hongyang Xu, Can Zheng, Lingyu Zhang, Qiang Lin, Xuemin |
| author_facet | Chen, Hongyang Xu, Can Zheng, Lingyu Zhang, Qiang Lin, Xuemin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15617 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diffusion-based Graph Generative Methods Chen, Hongyang Xu, Can Zheng, Lingyu Zhang, Qiang Lin, Xuemin Machine Learning Artificial Intelligence 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. |
| title | Diffusion-based Graph Generative Methods |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.15617 |