Summary of Rocp-gnn: Robust Conformal Prediction For Graph Neural Networks in Node-classification, by S. Akansha
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification
by S. Akansha
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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, Robust Conformal Prediction for GNNs (RoCP-GNN), integrates conformal prediction directly into the training process of Graph Neural Networks (GNNs) to provide robust uncertainty estimates. This method generates prediction sets valid at a user-defined confidence level, assuming exchangeability. RoCP-GNN robustly predicts outcomes with any predictive GNN model while quantifying the uncertainty in predictions within graph-based semi-supervised learning (SSL). Experimental results demonstrate a statistically significant increase in performance when using size loss with various state-of-the-art GNNs for node classification on standard graph benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to predict what kind of animal is living in a forest, based on pictures of the trees and other features. This can be tricky because some animals might look similar or have different characteristics that aren’t shown in the pictures. A team of researchers has come up with a new way to make these predictions more reliable by providing a range of possible answers instead of just one. They used a type of artificial intelligence called Graph Neural Networks (GNNs) and developed a method to show how uncertain they are about their predictions. This helps prevent making mistakes when it really matters. The team tested this approach on some real-world data and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Classification » Gnn » Semi supervised
