A completion-aware framework for counterfactual explainability in GNNs that integrates factual explanations with missing edge prediction to improve explanation quality, robustness, and intuitiveness.
In: Proceed- ings of the Sixteenth ACM International Conference on Web Search and Data Mining
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
AlignGAD is a zero-shot generalized graph anomaly detection framework using a Global Unification Module, Clustering Module, and Node Discrepancy Scoring Module.
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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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A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction
AlignGAD is a zero-shot generalized graph anomaly detection framework using a Global Unification Module, Clustering Module, and Node Discrepancy Scoring Module.