#gnn-explainability News & Analysis

2 articles tagged with #gnn-explainability. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AINeutralarXiv – CS AI · Jun 236/10

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A Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks

Researchers propose a novel counterfactual explainability framework for graph neural networks that improves model transparency by combining factual explainability methods with link prediction techniques. The model-agnostic approach enables both edge addition and removal to generate higher-quality, more intuitive explanations for GNN predictions on graph classification tasks.

AINeutralarXiv – CS AI · Jun 56/10

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Beyond Soft Masks: Hard-Perturbation Mixup Explainer for Robust GNN Explainability

Researchers propose HPME, a novel framework for explaining Graph Neural Network decisions using hard-perturbation mixup strategies instead of soft masks. The method addresses out-of-distribution issues in GNN explainability by extracting discrete subgraphs and employing structure-level replacement, achieving improved explanation fidelity across synthetic and real-world datasets.