Summary of Gagsl: Global-augmented Graph Structure Learning Via Graph Information Bottleneck, by Shuangjie Li et al.
GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck
by Shuangjie Li, Jiangqing Song, Baoming Zhang, Gaoli Ruan, Junyuan Xie, Chongjun Wang
First submitted to arxiv on: 7 Nov 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 proposed Global-augmented Graph Structure Learning (GaGSL) method aims to develop a clean graph structure that balances performance and robustness for semi-supervised node classification tasks. Building on the Graph Information Bottleneck (GIB) principle, GaGSL first obtains augmented features and structures through global feature and structure augmentation. These are then input into a structure estimator with different parameters for optimization and re-definition of the graph structure. The redefined structures are combined to form the final graph structure. GIB is used to guide the optimization of the graph structure to obtain the minimum sufficient graph structure. Experimental evaluations across various datasets demonstrate GaGSL’s superior performance and robustness compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GaGSL is a new way to learn a clean graph structure for node classification tasks. It uses an idea called Graph Information Bottleneck (GIB) to find the best structure. The method first adds extra information to the nodes and edges, then uses this new data to redefine the graph structure. This process helps remove noise and make the graph more robust. GaGSL works well across different datasets and is better than other methods at both performance and robustness. |
Keywords
» Artificial intelligence » Classification » Optimization » Semi supervised
