Summary of Graph Condensation: a Survey, by Xinyi Gao et al.
Graph Condensation: A Survey
by Xinyi Gao, Junliang Yu, Tong Chen, Guanhua Ye, Wentao Zhang, Hongzhi Yin
First submitted to arxiv on: 22 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 survey of graph condensation (GC), an innovative approach to training graph neural networks (GNNs) on large graph data. GC synthesizes a compact yet representative graph, enabling GNNs trained on it to achieve performance comparable to those trained on the original large graph. The authors organize existing research into five categories: effectiveness, generalization, efficiency, fairness, and robustness. They examine various methods under each category, discussing optimization strategies and condensed graph generation. The paper also compares representative GC methods based on the proposed evaluation criteria, explores applications in various fields, and highlights open-source libraries and present challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a new way to train artificial intelligence (AI) models called Graph Condensation. It takes big graphs that are hard to work with and makes them smaller while keeping all the important information. This helps AI models learn from these big graphs faster and better. The researchers looked at many different ways to do this and compared how well they worked. They also talked about what fields could use this technology, like social media or medicine. It’s a way to make AI models smarter and more useful. |
Keywords
* Artificial intelligence * Generalization * Optimization
