Measuring What Matters: A Unified Evaluation Framework for GNN Explainability | Paper Citation Record

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Unresolved cited work

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Inductive representation learning on large graphs

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Gnnexplainer: Generating explanations for graph neural networks.Advances in neural information processing systems, 32, 2019

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Graphlime: Local interpretable model explanations for graph neural networks.IEEE Transactions on Knowledge and Data Engineering, 35(7):6968–6972, 2022

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability On explainability of graph neural networks via subgraph explorations

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Explainability, quantified: Benchmarking xai techniques

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Bagel: A benchmark for assessing graph neural network explanations

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This paper cites Evaluating explainability for graph neural networks.Scientific Data, 10(1):144, 2023.

Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Evaluating explainability for graph neural networks.Scientific Data, 10(1):144, 2023

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This paper cites A true-to-the-model axiomatic benchmark for graph-based explainers.Trans.

Measuring What Matters: A Unified Evaluation Framework for GNN Explainability A true-to-the-model axiomatic benchmark for graph-based explainers.Trans

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Graphxai: a survey of graph neural networks (gnns) for explainable ai (xai).Neural Computing and Applications, 37(17):10949–11000, 2025

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This paper cites The graph neural network model.IEEE transactions on neural net- works, 20(1):61–80, 2008.

Measuring What Matters: A Unified Evaluation Framework for GNN Explainability The graph neural network model.IEEE transactions on neural net- works, 20(1):61–80, 2008

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Semi-supervised classification with graph convo- lutional networks

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Graph attention networks

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability How powerful are graph neural networks? InInternational Conference on Learning Representations, 2018

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Spatial graph convolutional networks

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability A survey on self-supervised graph foundation models: Knowledge-based perspective

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Learning important features through propagating activation differences

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Axiomatic attribution for deep networks

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Striving for simplicity: The all convolutional net

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Visualizing and understanding convolutional networks

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.PloS one, 10(7):e0130140, 2015

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Higher-order explanations of graph neural networks via relevant walks.IEEE transactions on pattern analysis and ma- chine intelligence, 44(11):7581–7596, 2021

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Parameterized explainer for graph neural network.Advances in neural information processing systems, 33:19620–19631, 2020

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Interpreting graph neural networks for nlp with differentiable edge masking

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Explaining identity-aware graph classifiers through the language of motifs

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Cf-gnnexplainer: Counterfactual explanations for graph neural networks

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Clear: Generative counterfactual explanations on graphs.Advances in neural information processing systems, 35:25895–25907, 2022

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Protgnn: To- wards self-explaining graph neural networks

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability GNNX-BENCH: Unravelling the utility of perturbation-based GNN explainers through in-depth benchmarking

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability Cure-bench.https://kaggle.com/competitions/cure-bench, 2025

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability The movielens datasets: History and context.Acm transactions on interactive intelligent systems (tiis), 5(4):1–19, 2015

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability binomial

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Measuring What Matters: A Unified Evaluation Framework for GNN Explainability This approach requires onlyMexplanation generations instead of2N, significantly reducing computational overhead for production monitoring

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