Summary of Posterior Label Smoothing For Node Classification, by Jaeseung Heo et al.
Posterior Label Smoothing for Node Classification
by Jaeseung Heo, Moonjeong Park, Dongwoo Kim
First submitted to arxiv on: 1 Jun 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 label smoothing method improves the transductive node classification task by encapsulating local context through neighborhood label distributions. The approach demonstrates effectiveness across 10 datasets using seven baseline models, with improved accuracy in most cases. By incorporating global label statistics into posterior computation, the method mitigates overfitting and enhances generalization performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper proposes a new way to improve node classification on graphs. It’s like giving a hint to the computer about what the labels should be based on the nearby nodes. This helps the computer make more accurate predictions and avoid getting too good at fitting the training data (overfitting). The method works well in most cases across 10 different datasets, which shows its usefulness. |
Keywords
» Artificial intelligence » Classification » Generalization » Overfitting
