Summary of Diffusion-based Graph Generative Methods, by Hongyang Chen et al.
Diffusion-based Graph Generative Methods
by Hongyang Chen, Can Xu, Lingyu Zheng, Qiang Zhang, Xuemin Lin
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive review of diffusion-based graph generative methods, which have shown significant advances in generating graphs. The authors focus on three mainstream paradigms: denoising diffusion probabilistic models, score-based generative models, and stochastic differential equations. They also explore the latest applications of these models on graphs and discuss their limitations and future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper reviews how computers can create new graphs that are similar to real-life graphs. It talks about three ways this is done: denoising diffusion probabilistic models, score-based generative models, and stochastic differential equations. The authors also show how these methods are used in graph generation tasks and what they’re good for. They even point out some areas where more work is needed. |
Keywords
* Artificial intelligence * Diffusion
