A systematic method leveraging Weisfeiler-Leman coloring to mine class-discriminating motifs as proxy explanations, enabling the creation of the OpenGraphXAI benchmark suite from real-world datasets.
Explaining the Explainers in Graph Neural Networks: a Comparative Study , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3verdicts
UNVERDICTED 3representative citing papers
AIM is a new evaluation framework for explainability in GNNs that combines accuracy, instance-level, and model-level measures, applied to graph kernel networks to create an improved model xGKN.
A completion-aware framework for counterfactual explainability in GNNs that integrates factual explanations with missing edge prediction to improve explanation quality, robustness, and intuitiveness.
citing papers explorer
-
AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks
AIM is a new evaluation framework for explainability in GNNs that combines accuracy, instance-level, and model-level measures, applied to graph kernel networks to create an improved model xGKN.
-
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.