Summary of Cometh: a Continuous-time Discrete-state Graph Diffusion Model, by Antoine Siraudin et al.
Cometh: A continuous-time discrete-state graph diffusion model
by Antoine Siraudin, Fragkiskos D. Malliaros, Christopher Morris
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel graph diffusion model, Cometh, is proposed to combine the benefits of discrete-state denoising diffusion models with continuous-time approaches. This new method, tailored to graph data, integrates a single random-walk-based encoding to boost expressiveness and leverages a better trade-off between sampling efficiency and quality. Empirical results show significant improvements over state-of-the-art discrete-state diffusion models on various benchmark datasets, including molecular and non-molecular ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cometh is a new way of generating graphs that combines two different techniques. It uses continuous time to make the process more flexible and efficient, while also using a random-walk-based encoding to help it generate more diverse graphs. This approach leads to better results than previous methods on many different types of graph datasets. |
Keywords
» Artificial intelligence » Diffusion model
