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.
A survey on explain- ability of graph neural networks
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LLM agents make collective belief dynamics programmable, with simulations showing coordinated agents induce stable belief shifts, and four structural properties that complicate detection and defense.
xAI-Drop introduces an explainability-based topological dropping regularizer for GNNs that outperforms state-of-the-art dropping methods in accuracy and explanation quality on real-world datasets.
Benchmark study of ten GNN explainers on eight architectures and six datasets that isolates usable components and issues practical recommendations.
Systematic review of 65 GNN studies on resting-state fMRI finds high classification performance but low reproducibility of disorder-specific biomarkers across papers, with few transdiagnostic signals.
citing papers explorer
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A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloring
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.
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LLM Agents Make Collective Belief Dynamics Programmable: Challenges and Research Directions
LLM agents make collective belief dynamics programmable, with simulations showing coordinated agents induce stable belief shifts, and four structural properties that complicate detection and defense.
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xAI-Drop: Don't Use What You Cannot Explain
xAI-Drop introduces an explainability-based topological dropping regularizer for GNNs that outperforms state-of-the-art dropping methods in accuracy and explanation quality on real-world datasets.
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Explaining the Explainers in Graph Neural Networks: a Comparative Study
Benchmark study of ten GNN explainers on eight architectures and six datasets that isolates usable components and issues practical recommendations.
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Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review
Systematic review of 65 GNN studies on resting-state fMRI finds high classification performance but low reproducibility of disorder-specific biomarkers across papers, with few transdiagnostic signals.