Controllable GNN Explanations via Multi-Metric Preference Selection

arXiv:2609.32436 · cs.AI, cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.AI, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Fidelity — 82× in this paper · explained in 2 episodes
• Interpretability — 68× in this paper · explained in 15 episodes
• Explainability — 12× in this paper · explained in 3 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Sparsity — 5× in this paper · explained in 4 episodes
• Core — 4× in this paper · explained in 2 episodes
• GNNs — 4× in this paper · explained in 2 episodes
• Two-Stage Optimization — 4× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Multi-objective Optimization — 3× in this paper · explained in 4 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adjacency Matrix — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Graph Neural Networks (GNNs) — 2× in this paper · explained in 7 episodes
• Graph Neural Tangent Kernel — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• APEX — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• In International Conference on Learning Representations — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Mask Generation — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Shapley values — 1× in this paper · explained in 2 episodes

Sources

• On the Robustness of Interpretability Methods
• Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks
• Graph Neural Networks Including Sparse Interpretability
• DIG: A Turnkey Library for Diving into Graph Deep Learning Research
• TUDataset: A collection of benchmark datasets for learning with graphs
• Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
• GNNExplainer: Generating Explanations for Graph Neural Networks
• Neural Subgraph Matching
• MotifExplainer: a Motif-based Graph Neural Network Explainer

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