Explainability in graph neural networks: A taxonomic survey. Yuan Hao, Yu Haiyang, Gui Shurui, Ji Shuiwang. ARXIV 2020. paper
Gnnexplainer: Generating explanations for graph neural networks. Ying Rex, Bourgeois Dylan, You Jiaxuan, Zitnik Marinka, Leskovec Jure. NeurIPS 2019. papercode
Explainability methods for graph convolutional neural networks. Pope Phillip E, Kolouri Soheil, Rostami Mohammad, Martin Charles E, Hoffmann Heiko. CVPR 2019.paper
Xgnn: Towards model-level explanations of graph neural networks. Yuan Hao, Tang Jiliang, Hu Xia, Ji Shuiwang. KDD 2020. paper.
Evaluating Attribution for Graph Neural Networks. Sanchez-Lengeling Benjamin, Wei Jennifer, Lee Brian, Reif Emily, Wang Peter, Qian Wesley, McCloskey Kevin, Colwell Lucy, Wiltschko Alexander. NeurIPS 2020.paper
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks. Vu Minh, Thai My T.. NeurIPS 2020.paper
Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks. Federico Baldassarre and Kevin Smith and Josephine Sullivan and Hossein Azizpour. ECCV 2020.paper
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media. Lu, Yi-Ju and Li, Cheng-Te. ACL 2020.paper
On Explainability of Graph Neural Networks via Subgraph Explorations. Yuan Hao, Yu Haiyang, Wang Jie, Li Kang, Ji Shuiwang. ICML 2021.paper
[Thesis 23] Interpretability of Graphical Models[paper]
[Bioengineering 2023] Personalized Explanations for Early Diagnosis of Alzheimer's Disease Using Explainable Graph Neural Networks with Population Graphs[paper]
[Information Science 2023] Explainability techniques applied to road traffic forecasting using Graph Neural Network models[paper]
[Arxiv 23] Efficient GNN Explanation via Learning Removal-based Attribution[paper]
[Arxiv 23] Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity[paper]
[ICLR Tiny 23] Message-passing selection: Towards interpretable GNNs for graph classification[paper]
[ICLR Tiny 23] Revisiting CounteRGAN for Counterfactual Explainability of Graphs[paper]
[MICCAI Workshop 23] IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction[paper]
[Arxiv 23] Robust Ante-hoc Graph Explainer using Bilevel Optimization[paper]
[GRADES & NDA'23] A Demonstration of Interpretability Methods for Graph Neural Networks[paper]
[Arxiv 23] Self-Explainable Graph Neural Networks for Link Prediction[paper]
[ChemRxiv 23] Interpreting Graph Neural Networks with Myerson Values for Cheminformatics Approaches[paper]
[Neural Networks 23] Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness[paper]
[ICASSP 23] Towards a More Stable and General Subgraph Information Bottleneck[paper]
[ESANN 23] Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability[Paper]
[IEEE Access] Generating Real-Time Explanations for GNNs via Multiple Specialty Learners and Online Knowledge Distillation[Paper]
[IEEE Access] Providing Post-Hoc Explanation for Node Representation Learning Models Through Inductive Conformal Predictions[paper]
[Journal of Software 23] A Slice-level vulnerability detection and interpretation method based on graph neural network[paper]
[Automation in Construction 23] Learning from explainable data-driven tunneling graphs: A spatio-temporal graph convolutional network for clogging detection[paper]
[Briefings in Bioinformatics] Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism[paper]
[Briefings in Bioinformatics] Identification of vital chemical information via visualization of graph neural networks[paper]
[Bioinformatics 23] Explainable Multilayer Graph Neural Network for Cancer Gene Prediction[paper]
[ICLR Workshop 23] GCI: A Graph Concept Interpretation Framework[paper]
[Arxiv 23] Structural Explanations for Graph Neural Networks using HSIC[paper]
[Internet of Things 23] XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics[paper]
[JOS23] A Generic Explaining & Locating Method for Malware Detection based on Graph Neural Networks[paper]
Year 2022
[NeurIPS 22] GStarX:Explaining Graph-level Predictions with Communication Structure-Aware Cooperative Games[paper]
[Arxiv 22] MotifExplainer: a Motif-based Graph Neural Network Explainer[paper]
[Arxiv 22] Faithful Explanations for Deep Graph Models[paper]
[Arxiv 22] Towards Explanation for Unsupervised Graph-Level Representation Learning[paper]
[Arxiv 22] BAGEL: A Benchmark for Assessing Graph Neural Network Explanations[paper]
[Arxiv 22] BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck[paper]
[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]
[IEEE TSIPN 22] Explainability and Graph Learning from Social Interactions[paper]
[Arxiv 22] Cognitive Explainers of Graph Neural Networks Based on Medical Concepts[paper]
Year 2021
[NeurIPS 21] SALKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning[paper]
[NeurIPS 2021] Reinforcement Learning Enhanced Explainer for Graph Neural Networks[paper]
[NeurIPS 2021] Towards Multi-Grained Explainability for Graph Neural Networks[paper]
[NeurIPS 2021] Robust Counterfactual Explanations on Graph Neural Networks[paper]
[ICML 2021] On Explainability of Graph Neural Networks via Subgraph Explorations[paper]
[ICML 2021] Generative Causal Explanations for Graph Neural Networks[paper]
[ICML 2021] Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity[paper]
[ICML 2021] Automated Graph Representation Learning with Hyperparameter Importance Explanation[paper]
[ICLR 2021] Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking[paper]
[ICLR 2021] Graph Information Bottleneck for Subgraph Recognition[paper]
[KDD 2021] When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods[paper]
[KDD 2021] Counterfactual Graphs for Explainable Classification of Brain Networks[paper]
[CVPR 2021] Quantifying Explainers of Graph Neural Networks in Computational Pathology.[paper]
[NAACL 2021] Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network. [paper]
[AAAI 2021] Motif-Driven Contrastive Learning of Graph Representations[paper]
[TPAMI 21] Higher-Order Explanations of Graph Neural Networks via Relevant Walks[paper]
[WWW 2021] Interpreting and Unifying Graph Neural Networks with An Optimization Framework[paper]
[Genome medicine 21] Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer[paper]
[IJCKG 21] Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules[paper]
[RuleML+RR 21] Combining Sub-Symbolic and Symbolic Methods for Explainability[paper]
[PAKDD 21] SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction[paper]
[J. Chem. Inf. Model] Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment[paper]
[BioRxiv 21] APRILE: Exploring the Molecular Mechanisms of Drug Side Effects with Explainable Graph Neural Networks[paper]
[ISM 21] Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks[paper]
[Arxiv 21] Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows[paper]
[Arxiv 21] SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods[paper]
[Arxiv 21] Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation[paper]
[Arxiv 21] Learnt Sparsification for Interpretable Graph Neural Networks[paper]