Data to 14 Oct 2026

GNNExplainer: Generating Explanations for Graph Neural Networks

arXiv 1903.03894 ↗First posted 10 Mar 2019cs.LG stat.MLcited by 737

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Abstract (arXiv)

Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs.GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. However, incorporating both graph structure and feature information leads to complex models, and explaining predictions made by GNNs remains unsolved. Here we propose GNNExplainer, the first general, model-agnostic approach for providing interpretable explanations for predictions of any GNN-based model on any graph-based machine learning task. Given an instance, GNNExplainer identifies a compact subgraph structure and a small subset of node features that have a crucial role in GNN's prediction. Further, GNNExplainer can generate consistent and concise explanations for an entire class of instances. We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures. Experiments on synthetic and real-world graphs show that our approach can identify important graph structures as well as node features, and outperforms baselines by 17.1% on average. GNNExplainer provides a variety of benefits, from the ability to visualize semantically relevant structures to interpretability, to giving insights into errors of faulty GNNs.

Key facts. Cited by 737 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Mar 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 84; 2022: 125; 2023: 134; 2024: 152; 2025: 128; 2026: 114.

737 papers.

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2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 302
2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 281
2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
2021Identity-aware Graph Neural Networkscited by 198
2022Discovering Invariant Rationales for Graph Neural Networkscited by 188
2022Graph neural networks for materials science and chemistrycited by 182
2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
2022Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanismcited by 171
2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
2022GOOD: A Graph Out-of-Distribution Benchmarkcited by 141
2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
2021Generative Causal Explanations for Graph Neural Networkscited by 124

38 more

2021CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networkscited by 119
2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 118
2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
2021Causal Attention for Interpretable and Generalizable Graph Classificationcited by 108
2022XAI for Transformers: Better Explanations through Conservative Propagationcited by 100
2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
2021ProtGNN: Towards Self-Explaining Graph Neural Networkscited by 96
2022Decoupling the Depth and Scope of Graph Neural Networkscited by 94
2021Trustworthy AI: A Computational Perspectivecited by 93
2021Graph Neural Networks in Network Neurosciencecited by 92
2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 89
2022Evaluating Explainability for Graph Neural Networkscited by 87
2022Debiasing Graph Neural Networks via Learning Disentangled Causal Substructurecited by 83
2021Graph Representation Learning in Biomedicinecited by 80
2022Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoningcited by 79
2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
2021Towards Self-Explainable Graph Neural Networkcited by 75
2021Robust Counterfactual Explanations on Graph Neural Networkscited by 72
2021Generalizing Graph Neural Networks on Out-Of-Distribution Graphscited by 71
2022Graph Rationalization with Environment-based Augmentationscited by 70
2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
2022Global Explainability of GNNs via Logic Combination of Learned Conceptscited by 65
2021GraphSVX: Shapley Value Explanations for Graph Neural Networkscited by 64
2024Human-inspired Episodic Memory for Infinite Context LLMscited by 62
2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 61
2022Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysiscited by 59
2022GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networkscited by 58
2022Let Invariant Rationale Discovery Inspire Graph Contrastive Learningcited by 57
2023Interpretable Neural-Symbolic Concept Reasoningcited by 57
2024A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensationcited by 57
2021E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTcited by 56
2023Time Series Contrastive Learning with Information-Aware Augmentationscited by 52
2025PyG 2.0: Scalable Learning on Real World Graphscited by 52
2021Entropy-based Logic Explanations of Neural Networkscited by 51
2022KerGNNs: Interpretable Graph Neural Networks with Graph Kernelscited by 50
2022CLEAR: Generative Counterfactual Explanations on Graphscited by 48
2023Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learningcited by 47
2021Improving Subgraph Recognition with Variational Graph Information Bottleneckcited by 45

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