GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks
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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_ | 1866913712075440128 |
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| author | Zhuang, Dingyi Jiang, Chonghe Zheng, Yunhan Wang, Shenhao Zhao, Jinhua |
| author_facet | Zhuang, Dingyi Jiang, Chonghe Zheng, Yunhan Wang, Shenhao Zhao, Jinhua |
| contents | Graph Neural Networks deliver strong classification results but often suffer from poor calibration performance, leading to overconfidence or underconfidence. This is particularly problematic in high stakes applications where accurate uncertainty estimates are essential. Existing post hoc methods, such as temperature scaling, fail to effectively utilize graph structures, while current GNN calibration methods often overlook the potential of leveraging diverse input information and model ensembles jointly. In the paper, we propose Graph Ensemble Temperature Scaling, a novel calibration framework that combines input and model ensemble strategies within a Graph Mixture of Experts archi SOTA calibration techniques, reducing expected calibration error by 25 percent across 10 GNN benchmark datasets. Additionally, GETS is computationally efficient, scalable, and capable of selecting effective input combinations for improved calibration performance. The implementation is available via Github. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09570 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks Zhuang, Dingyi Jiang, Chonghe Zheng, Yunhan Wang, Shenhao Zhao, Jinhua Machine Learning Graph Neural Networks deliver strong classification results but often suffer from poor calibration performance, leading to overconfidence or underconfidence. This is particularly problematic in high stakes applications where accurate uncertainty estimates are essential. Existing post hoc methods, such as temperature scaling, fail to effectively utilize graph structures, while current GNN calibration methods often overlook the potential of leveraging diverse input information and model ensembles jointly. In the paper, we propose Graph Ensemble Temperature Scaling, a novel calibration framework that combines input and model ensemble strategies within a Graph Mixture of Experts archi SOTA calibration techniques, reducing expected calibration error by 25 percent across 10 GNN benchmark datasets. Additionally, GETS is computationally efficient, scalable, and capable of selecting effective input combinations for improved calibration performance. The implementation is available via Github. |
| title | GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.09570 |