Understanding and Mitigating Under-Confidence in GNNs from the Final Layer

arXiv:2505.11335 · cs.LG · Submitted 2025-05-16 · Read on arXiv

cs.LG

Submitted: 2025-05-16

Updated: 2026-09-27

Terminology

• Calibration — 152× in this paper · explained in 7 episodes
• GNNs — 40× in this paper · explained in 2 episodes
• Accuracy — 24× in this paper · explained in 1 episode
• Regularization — 12× in this paper · explained in 3 episodes
• Temperature Scaling — 7× in this paper
• Graph Convolutional Network — 4× in this paper
• Graph Neural Networks (GNNs) — 4× in this paper · explained in 7 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Expected Calibration Error — 3× in this paper
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Knowledge Graph — 3× in this paper · explained in 7 episodes
• Objective Function — 3× in this paper · explained in 2 episodes
• Over-smoothing — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Adjacency Matrix — 2× in this paper · explained in 1 episode
• Computational overhead — 2× in this paper · explained in 2 episodes
• Heterogeneous Graph — 2× in this paper · explained in 2 episodes
• Knowledge graphs — 2× in this paper · explained in 2 episodes
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Matrix Scaling — 1× in this paper
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Spectral clustering — 1× in this paper · explained in 2 episodes

Sources

• A Survey on Graph Neural Networks for Knowledge Graph Completion
• Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
• A Note on Over-Smoothing for Graph Neural Networks
• A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
• A Survey on Oversmoothing in Graph Neural Networks
• Lost in Edits? A $\lambda$-Compass for AIGC Provenance

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