Uncertainty Quantification on Graph Learning: A Survey
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915935408881664 |
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| author | Chen, Chao Guo, Chenghua Xu, Rui Chen, Jiujiu Liao, Xiangwen Zhang, Xi Xie, Sihong Xiong, Hui Yu, Philip |
| author_facet | Chen, Chao Guo, Chenghua Xu, Rui Chen, Jiujiu Liao, Xiangwen Zhang, Xi Xie, Sihong Xiong, Hui Yu, Philip |
| contents | Graphical models have demonstrated their exceptional capabilities across numerous applications. However, their performance, confidence, and trustworthiness are often limited by the inherent randomness in data generation and the lack of knowledge to accurately model real-world complexities. There has been increased interest in developing uncertainty quantification (UQ) techniques tailored to graphical models. In this survey, we systematically examine existing works on UQ for graphical models. This survey distinguishes itself from most existing UQ surveys by specifically concentrating on graphical models, including graph neural networks and graph foundation models. We organize the literature along two complementary dimensions: uncertainty representation and uncertainty handling. By synthesizing both established methodologies and emerging trends, we aim to bridge gaps in understanding key challenges and opportunities in UQ for graphical models, inspiring researchers on graphical models or uncertainty quantification to make further advancements at the cross of the two fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14642 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncertainty Quantification on Graph Learning: A Survey Chen, Chao Guo, Chenghua Xu, Rui Chen, Jiujiu Liao, Xiangwen Zhang, Xi Xie, Sihong Xiong, Hui Yu, Philip Machine Learning Graphical models have demonstrated their exceptional capabilities across numerous applications. However, their performance, confidence, and trustworthiness are often limited by the inherent randomness in data generation and the lack of knowledge to accurately model real-world complexities. There has been increased interest in developing uncertainty quantification (UQ) techniques tailored to graphical models. In this survey, we systematically examine existing works on UQ for graphical models. This survey distinguishes itself from most existing UQ surveys by specifically concentrating on graphical models, including graph neural networks and graph foundation models. We organize the literature along two complementary dimensions: uncertainty representation and uncertainty handling. By synthesizing both established methodologies and emerging trends, we aim to bridge gaps in understanding key challenges and opportunities in UQ for graphical models, inspiring researchers on graphical models or uncertainty quantification to make further advancements at the cross of the two fields. |
| title | Uncertainty Quantification on Graph Learning: A Survey |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.14642 |