Summary of Valid Conformal Prediction For Dynamic Gnns, by Ed Davis et al.
Valid Conformal Prediction for Dynamic GNNs
by Ed Davis, Ian Gallagher, Daniel John Lawson, Patrick Rubin-Delanchy
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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 proposed solution to improve the trustworthiness of graph neural networks (GNNs) is presented. The paper highlights that while GNNs have shown impressive empirical performance, they lack uncertainty quantification, making it challenging to rely on them in high-risk scenarios. To address this issue, the authors propose using unfolding, which allows existing static GNNs to output dynamic graph embeddings with exchangeability properties. This enables conformal prediction to be extended to dynamic GNNs in both transductive and semi-inductive regimes, providing a theoretical guarantee of valid conformal prediction. The paper demonstrates the empirical validity and performance gains of unfolded GNNs against standard GNN architectures on simulated and real datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are powerful tools that can analyze complex graph structures. However, they don’t tell us how sure we should be in their predictions. To fix this, researchers propose a way to make GNNs more trustworthy by using something called “unfolding.” This allows existing GNNs to work with changing graphs and provide accurate results. The study shows that this new approach works well on both made-up and real datasets, giving better results than usual GNN methods. |
Keywords
» Artificial intelligence » Gnn
