Summary of Conformal Inductive Graph Neural Networks, by Soroush H. Zargarbashi et al.
Conformal Inductive Graph Neural Networks
by Soroush H. Zargarbashi, Aleksandar Bojchevski
First submitted to arxiv on: 12 Jul 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 Conformal prediction (CP) transforms model outputs into prediction sets with guaranteed coverage of true labels. CP relies on exchangeability, relaxing the i.i.d. assumption, making it suitable for transductive node-classification. However, conventional CP is not applicable in inductive settings due to message passing causing a shift in calibration scores. This paper addresses this issue for node- and edge-exchangeable graphs, recovering standard coverage guarantees without compromising statistical efficiency. The authors also prove that the guarantee holds regardless of prediction time, including upon arrival of new nodes or edges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about a way to make predictions more reliable by adding extra information around each answer. This helps ensure that the true answer will be included in the range of possible answers. The method works for certain types of data and makes it easier to use in new situations. It’s like having a safety net to catch any mistakes, so you can trust your predictions even more. |
Keywords
* Artificial intelligence * Classification
