Survey, Evaluation
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Explainability in Graph Neural Networks: A Taxonomic Survey
– robustness - [Arxiv 22] A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability [paper]
- [Arxiv 22] Explainability in Graph Neural Networks: An Experimental Survey [paper]
- [AISTATS 22] Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods [paper]
- [The Webconf 22] Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning [paper]
- [KDD 2021] When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods [paper]
- [2206.09677] GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
- [2207.12599] A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics
Paper
- GraphLIME:Local Interpretable Model Explanations
- NIPS 19 GNNExplainer
- NIPS 20 PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
- NIPS 20 Parameterized Explainer for Graph Neural Network
- KDD 20 XGNN
- ICML 21 [2102.05152] On Explainability of Graph Neural Networks via Subgraph Explorations
- Factorizable Graph Convolutional Networks
- NeurlPS 20 Submission: XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks
- CVPR 19: Explainability Methods for Graph Convolutional Neural Networks
- [IJCAI 22] What Does My GNN Really Capture? On Exploring Internal GNN Representations [paper]
- [TNNLS 22] Explaining Deep Graph Networks via Input Perturbation [paper]
- [KDD 22] On Structural Explanation of Bias in Graph Neural Networks [paper]
- [AISTATS 22] CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks [paper]
- [NeuroComputing 22] Perturb more, trap more: Understanding behaviors of graph neural networks [paper]
- [DASFAA 22] On Glocal Explainability of Graph Neural Networks [paper]
- [AAAI 22] KerGNNs: Interpretable Graph Neural Networks with Graph Kernels [paper]
- [TPAMI 22] Reinforced Causal Explainer for Graph Neural Networks [paper]
- [CVPR 22] OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks [paper]
- [CVPR 22] Improving Subgraph Recognition with Variational Graph Information Bottleneck [paper]
- [Arxiv 22] GRAPHSHAP: Motif-based Explanations for Black-box Graph Classifiers [paper]
- [KBS 22] EGNN: Constructing explainable graph neural networks via knowledge distillation [paper]
- [AAAI22] ProtGNN: Towards Self-Explaining Graph Neural Networks [paper]
- [ICML 22] Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism [paper]
- [OpenReview 21] Deconfounding to Explanation Evaluation in Graph Neural Networks [paper]
- [ICLR 22] Discovering Invariant Rationales for Graph Neural Networks [paper]
- [Arxiv 22] MotifExplainer: a Motif-based Graph Neural Network Explainer [paper]
- [TPAMI 21] Higher-Order Explanations of Graph Neural Networks via Relevant Walks [paper]
- [ICML 2021] Generative Causal Explanations for Graph Neural Networks [paper]
- [ICML workshop 21] Towards Automated Evaluation of Explanations in Graph Neural Networks [paper]
- [ICLR 2021] Graph Information Bottleneck for Subgraph Recognition [paper]
- [WWW 2021] Interpreting and Unifying Graph Neural Networks with An Optimization Framework [paper]
- [ICDM 2021] GNES: Learning to Explain Graph Neural Networks [paper]
- [ICDM 2021] GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs [paper]
- [ICDM 2021] Multi-objective Explanations of GNN Predictions [paper]
- [CIKM 2021] Towards Self-Explainable Graph Neural Network [paper]
- [ECML PKDD 2021] GraphSVX: Shapley Value Explanations for Graph Neural Networks [paper]
- [NeSy 21] A New Concept for Explaining Graph Neural Networks [paper]
- [ICML workstop 2020] Contrastive Graph Neural Network Explanation [paper]
- [Arxiv 2020] Graph Neural Networks Including Sparse Interpretability [paper]
- [OpenReview 20] A Framework For Differentiable Discovery Of Graph Algorithms [paper]
- [OpenReview 20] Causal Screening to Interpret Graph Neural Networks [paper]
- [Arxiv 20] Understanding Graph Neural Networks from Graph Signal Denoising Perspectives [paper]
- [Arxiv 20] Understanding the Message Passing in Graph Neural Networks via Power Iteration [paper]
- [IJCNN 20] GCN-LRP explanation: exploring latent attention of graph convolutional networks ] [paper]
Application
- [BioRxiv 22] GNN-SubNet: disease subnetwork detection with explainable Graph Neural Networks [paper]
- [PAKDD 21] SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction [paper]
- [CVPR 2021] Quantifying Explainers of Graph Neural Networks in Computational Pathology .[paper]
- [ICML 2021] Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity [paper]
- [ICML workshop 21] GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks [paper]
- [ICML workshop 21] BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis [paper]
- [ICML workshop 21] Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction [paper]
- [KDD 2021] Counterfactual Graphs for Explainable Classification of Brain Networks [paper]
- [IJCNN 21] MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks [paper]