arXiv Computer Vision
U-CECE: A Universal Multi-Resolution Framework for Conceptual Counterfactual Explanations
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A Comparative Study of Counterfactual Explainers for Graph Neural Networks Enabling Multiple Types of Graph Edit
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Beyond Soft Masks: Hard-Perturbation Mixup Explainer for Robust GNN Explainability
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arXiv Machine Learning
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arXiv Machine Learning
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LLM Explainability with Counterfactual Chains and Causal Graphs
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RAW-Explainer: Post-hoc Explanations of Path Reasoning Graph Neural Networks for Knowledge Graph Completion
RAW-Explainer is a new explainability method designed for path reasoning graph neural networks used in knowledge graph completion. It produces connected, concise, and interpretable subgraph explanations for link prediction by leveraging heterogeneous information and a random walk objective. The approach also addresses distribution shift when evaluating small explanatory subgraphs with a robust evaluator, achieving a balance between explanation quality and computational efficiency on standard benchmarks.