Reliability-Aware and Explainability-Driven Evaluation of Graph Neural Networks on Citation Networks

While Graph Neural Networks (GNNs) are widely used for citation network analysis, their reliability and interpretability remain understudied. Existing benchmarks predominantly focus on accuracy, overlooking confidence behavior and failure modes critical for real-world deployment. To address this, we introduce a reliability-aware evaluation framework for GNNs that holistically compares three prominent architectures—GCN, GAT, and GraphSAGE—on node classification and link prediction across Cora, Citeseer, and PubMed. Beyond accuracy, we integrate GNNExplainer to investigate model interpretability and uncover a critical overconfidence phenomenon: incorrect predictions show 22.1% higher confidence than correct ones, indicating severe miscalibration. Our ablation studies reveal that edgebased augmentation outperforms feature-based augmentation by +1.2% accuracy, and that single-head GAT performs comparably to multi-head on homophilic graphs, suggesting architectural redundancy. Statistical analysis confirms GAT’s superior performance (82.7% accuracy) and calibration, but all models exhibit strong reliance on edge importance (r=0.82 with confidence). These findings motivate the necessity of calibration-aware GNN evaluation and post-hoc correction techniques, offering actionable insights for architecture selection and trustworthy deployment of GNNs in scholarly applications.