Polynomial-time max-product algorithms for exact (neuron-level) and approximate (node-level) top-K relevant walk search in GNN-LRP explanations.
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2026 4verdicts
UNVERDICTED 4representative citing papers
α-TCAV replaces TCAV's hard indicator with a tunable smooth function to create a unified probabilistic framework with lower variance and guidance for parameter choice or Bayes-optimal scoring.
Message-passing algorithms compute GNN-LRP subgraph attributions in linear time w.r.t. network depth by exploiting the distributive property.
A perturbation-based metric for XAI quality that formalizes sufficiency and necessity, paired with an adapter trained via differentiable supervision to generate causal explanations on black-box models.
citing papers explorer
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Relevant Walk Search for Explaining Graph Neural Networks
Polynomial-time max-product algorithms for exact (neuron-level) and approximate (node-level) top-K relevant walk search in GNN-LRP explanations.
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$\alpha$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
α-TCAV replaces TCAV's hard indicator with a tunable smooth function to create a unified probabilistic framework with lower variance and guidance for parameter choice or Bayes-optimal scoring.
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Efficient Higher-order Subgraph Attribution via Message Passing
Message-passing algorithms compute GNN-LRP subgraph attributions in linear time w.r.t. network depth by exploiting the distributive property.
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Learning Quantifiable Visual Explanations Without Ground-Truth
A perturbation-based metric for XAI quality that formalizes sufficiency and necessity, paired with an adapter trained via differentiable supervision to generate causal explanations on black-box models.