GCE: Confidence Calibration Error for ImprovedTrustworthiness of Graph Neural Networks
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".