GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks
Dingyi Zhuang Chonghe Jiang Yunhan Zheng Shenhao Wang Jinhua Zhao
Graph Neural Networks (GNNs) 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 (GETS), a novel calibration framework that combines input and model ensemble strategies within a Graph Mixture-of-Experts (MoE) architecture. GETS integrates diverse inputs, including logits, node features, and degree embeddings, and adaptively selects the most relevant experts for each node’s calibration procedure. Our method outperforms state-of-the-art calibration techniques, reducing expected calibration error (ECE) by $\geq$ 25% 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 at https://github.com/ZhuangDingyi/GETS/.
Uncertainty Quantification; Graph Neural Networks; Ensemble Learning; Mixture of Experts
GETS is a novel framework for calibrating GNNs, combining input and model ensemble strategies to improve uncertainty calibration across 10 benchmark datasets, offering significant performance gains while remaining efficient and scalable.