A completion-aware framework for counterfactual explainability in GNNs that integrates factual explanations with missing edge prediction to improve explanation quality, robustness, and intuitiveness.
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Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.
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A Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks
A completion-aware framework for counterfactual explainability in GNNs that integrates factual explanations with missing edge prediction to improve explanation quality, robustness, and intuitiveness.
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Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.