Summary of Cross-domain Graph Data Scaling: a Showcase with Diffusion Models, by Wenzhuo Tang et al.
Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models
by Wenzhuo Tang, Haitao Mao, Danial Dervovic, Ivan Brugere, Saumitra Mishra, Yuying Xie, Jiliang Tang
First submitted to arxiv on: 4 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 The paper proposes a universal graph structure augmentor called UniAug, which can learn diverse graph patterns and adaptively help downstream tasks. It’s built on a diffusion model that’s pre-trained on thousands of graphs across domains. This allows the model to capture structural patterns in graphs and improve performance when used for graph augmentation in downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computer models better at understanding different types of data, like pictures or text. Right now, these models get better as they’re trained with more data. But there’s a problem when it comes to graph data, which is a type of data that has connections between things. The paper wants to solve this problem by creating a new model that can learn from different types of graph data and help other tasks use this knowledge. |
Keywords
» Artificial intelligence » Diffusion model
