Summary of Uncertainty in Graph Neural Networks: a Survey, by Fangxin Wang et al.
Uncertainty in Graph Neural Networks: A Survey
by Fangxin Wang, Yuqing Liu, Kay Liu, Yibo Wang, Sourav Medya, Philip S. Yu
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 paper presents a comprehensive survey of Graph Neural Networks (GNNs) from an uncertainty perspective. The authors highlight the importance of quantifying and utilizing predictive uncertainty in GNNs, which can stem from data randomness and model training errors. They review existing graph uncertainty theory and methods, as well as corresponding downstream tasks, to bridge the gap between theory and practice. This work aims to connect different GNN communities and provide insights into promising directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how Graph Neural Networks (GNNs) can be better used by understanding their uncertainties. Right now, GNNs are widely used in many real-world applications, but they can make mistakes because of the uncertainty involved. This makes it hard to trust their predictions. The authors want to help solve this problem by looking at how uncertainty is handled in GNNs and what methods exist to deal with it. They also hope to bring together people working on GNNs and show them where future research should focus. |
Keywords
* Artificial intelligence * Gnn
