mlsanityData to 14 Oct 2026

A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics

arXiv 2207.12599 ↗First posted 26 Jul 2022cs.LG cs.AIcited by 26

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Abstract (arXiv)

Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data. At the same time, the predictions made by these models are often hard to interpret. In that regard, many efforts have been made to explain the prediction mechanisms of these models from perspectives such as GNNExplainer, XGNN and PGExplainer. Although such works present systematic frameworks to interpret GNNs, a holistic review for explainable GNNs is unavailable. In this survey, we present a comprehensive review of explainability techniques developed for GNNs. We focus on explainable graph neural networks and categorize them based on the use of explainable methods. We further provide the common performance metrics for GNNs explanations and point out several future research directions.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Builds on 30 of its 43 references that are in the map. First posted on arXiv 26 Jul 2022.

Builds on

30 of 43 references.

References · 30

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  4. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  5. 2020Parameterized Explainer for Graph Neural Networkcited by 426
  6. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 404
  7. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
  8. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 281
  9. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 254
  10. 2020XGNN: Towards Model-Level Explanations of Graph Neural Networkscited by 211
  11. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 211
  12. 2020GraphLIME: Local Interpretable Model Explanations for Graph Neural Networkscited by 209
18 more
  1. 2019Explainability Techniques for Graph Convolutional Networkscited by 161
  2. 2020Interpretability of machine learning based prediction models in healthcarecited by 75
  3. 2021Robust Counterfactual Explanations on Graph Neural Networkscited by 72
  4. 2019DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systemscited by 59
  5. 2020Explanation-Guided Training for Cross-Domain Few-Shot Classificationcited by 58
  6. 2022GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networkscited by 58
  7. 2020RelEx: A Model-Agnostic Relational Model Explainercited by 53
  8. 2020BayLIME: Bayesian Local Interpretable Model-Agnostic Explanationscited by 51
  9. 2018Person Re-identification with Deep Similarity-Guided Graph Neural Networkcited by 49
  10. 2020Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mappingcited by 49
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  12. 2020Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisionscited by 34
  13. 2022Reinforced Causal Explainer for Graph Neural Networkscited by 34
  14. 2017SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Propertiescited by 30
  15. 2019Towards Interpretable Sparse Graph Representation Learning with Laplacian Poolingcited by 20
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  17. 2019Incorporating Biological Knowledge with Factor Graph Neural Network for Interpretable Deep Learningcited by 5
  18. 2020InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 10; 2024: 8; 2025: 2; 2026: 1.

26 papers.

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  1. 2022Explaining the Explainers in Graph Neural Networks: a Comparative Studycited by 35
  2. 2023A Survey on Explainability of Graph Neural Networkscited by 29
  3. 2023MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentationcited by 18
  4. 2023Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networkscited by 17
  5. 2023RegExplainer: Generating Explanations for Graph Neural Networks in Regression Taskscited by 9
  6. 2024Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networkscited by 8
  7. 2024LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generationcited by 8
  8. 2022Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networkscited by 7
  9. 2023Categorical Foundations of Explainable AI: A Unifying Theorycited by 7
  10. 2023Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's studycited by 6
  11. 2024Explainable Graph Neural Networks Under Firecited by 5
  12. 2022Machine Learning Partners in Criminal Networkscited by 4
14 more
  1. 2023Generative Explanations for Graph Neural Network: Methods and Evaluationscited by 4
  2. 2024Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networkscited by 4
  3. 2025From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networkscited by 4
  4. 2023Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AIcited by 3
  5. 2024Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networkscited by 3
  6. 2022GANExplainer: GAN-based Graph Neural Networks Explainercited by 2
  7. 2023Unstructured and structured data: Can we have the best of both worlds with large language models?cited by 2
  8. 2024Generating Human Understandable Explanations for Node Embeddingscited by 2
  9. 2025Absolute Evaluation Measures for Machine Learning: A Surveycited by 2
  10. 2024Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learningcited by 1
  11. 2022The Path to Autonomous Learnerscited by 0
  12. 2023ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networkscited by 0
  13. 2024SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanationcited by 0
  14. 2026HiSE: A Lightweight Hierarchical Semantic Explainer for Heterogeneous Graph Neural Networkscited by 0