Summary of Uncertainty Quantification on Graph Learning: a Survey, by Chao Chen et al.
Uncertainty Quantification on Graph Learning: A Survey
by Chao Chen, Chenghua Guo, Rui Xu, Xiangwen Liao, Xi Zhang, Sihong Xie, Hui Xiong, Philip Yu
First submitted to arxiv on: 23 Apr 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 The proposed survey comprehensively examines existing works on uncertainty quantification (UQ) techniques tailored to graphical models. The focus is on key aspects such as foundational knowledge, sources, representation, handling, and measurement of uncertainty in probabilistic graphical models (PGMs) and graph neural networks (GNNs). The work categorizes recent research into two primary areas: uncertainty representation and uncertainty handling. By providing a comprehensive overview of the current landscape, including established methodologies and emerging trends, the survey aims to bridge gaps in understanding and highlight key challenges and opportunities in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This survey looks at how we can better understand and deal with uncertainty when using graphical models, which are really good at things like social networks and online recommendation systems. Graphical models are great, but they often struggle with real-world complexities and random data. To help with this, researchers have been working on special techniques called uncertainty quantification (UQ). This survey looks at all the different ways people have tried to do UQ in graphical models. |
