GCE: Confidence Calibration Error for ImprovedTrustworthiness of Graph Neural Networks
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Bibliographic record
Abstract
The popularity of Graph Neural Networks (GNNs) in recent years underscores the need for further exploration of GNNs' trustworthiness. The confidence reported by GNN models in their predictions is an important aspect of trustworthiness, particularly in safety-critical domains such as healthcare. Recent proposals have identified that, unlike other deep learning models, GNNs exhibit under-confidence in their predictions. In this research, we propose Graph Confidence Error (GCE), a loss function to calibrate GNN model confidence during training. We compute the loss by quantifying the contribution of each data point to the model's confidence error and then use this value as a weight parameter in the loss function. We experimentally evaluated our approach for (1) three node classification tasks, including one heterogeneous and two homogeneous graphs, and (2) two graph classification tasks. The evaluation results demonstrate the reduction of the model's anticipated calibration error while preserving its overall performance. The code to GCE is publicly available at this URL https://github.com/samavi/pubs/tree/main/GCE. <https://github.com/samavi/pubs/tree/main/GCE.>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it