Summary of Unifying Invariant and Variant Features For Graph Out-of-distribution Via Probability Of Necessity and Sufficiency, by Xuexin Chen et al.
Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency
by Xuexin Chen, Ruichu Cai, Kaitao Zheng, Zhifan Jiang, Zhengting Huang, Zhifeng Hao, Zijian Li
First submitted to arxiv on: 21 Jul 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 A novel approach is proposed to tackle Graph Out-of-Distribution (OOD) by exploiting Probability of Necessity and Sufficiency (PNS) to extract invariant substructures. The method, called Sufficiency and Necessity Inspired Graph Learning (SNIGL), ensembles an invariant subgraph classifier with a domain variant subgraph classifier for generalization enhancement. Experimental results demonstrate the effectiveness of SNIGL on six public benchmarks, outperforming state-of-the-art techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research paper, scientists developed a new way to help machines learn from biased data and apply what they’ve learned to new situations. They came up with an idea called Sufficiency and Necessity Inspired Graph Learning (SNIGL) that uses two types of graphs: one that stays the same no matter how the data is changed, and another that changes depending on the labels. By combining these two types of graphs, they were able to make their machine learning model work better in real-world scenarios. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Probability
