mlsanityData to 7 Oct 2026

Explainability in Graph Neural Networks: An Experimental Survey

arXiv 2203.09258 ↗First posted 17 Mar 2022cs.LG cs.AIcited by 16

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Abstract (arXiv)

Graph neural networks (GNNs) have been extensively developed for graph representation learning in various application domains. However, similar to all other neural networks models, GNNs suffer from the black-box problem as people cannot understand the mechanism underlying them. To solve this problem, several GNN explainability methods have been proposed to explain the decisions made by GNNs. In this survey, we give an overview of the state-of-the-art GNN explainability methods and how they are evaluated. Furthermore, we propose a new evaluation metric and conduct thorough experiments to compare GNN explainability methods on real world datasets. We also suggest future directions for GNN explainability.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 38 of its 68 references that are in the map. First posted on arXiv 17 Mar 2022.

Builds on

38 of 68 references.

References · 38

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26 more
  1. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  2. 2014Striving for Simplicity: The All Convolutional Netcited by 983
  3. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  4. 2020Simple and Deep Graph Convolutional Networkscited by 857
  5. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  6. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 731
  7. 2019Graph Neural Networks for Social Recommendationcited by 711
  8. 2019Attention is not not Explanationcited by 629
  9. 2020Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?cited by 590
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  22. 2020Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskingcited by 152
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 3; 2025: 4; 2026: 1.

16 papers.

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  1. 2022GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networkscited by 57
  2. 2022Explaining the Explainers in Graph Neural Networks: a Comparative Studycited by 34
  3. 2023Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenancecited by 30
  4. 2022A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protectioncited by 20
  5. 2023Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Reviewcited by 20
  6. 2024PowerGraph: A power grid benchmark dataset for graph neural networkscited by 19
  7. 2025No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasetscited by 16
  8. 2023GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanationscited by 9
  9. 2023Exploring Causal Learning through Graph Neural Networks: An In-depth Reviewcited by 8
  10. 2023Evaluating Neighbor Explainability for Graph Neural Networkscited by 1
  11. 2024Veni, Vidi, Vici: Solving the Myriad of Challenges before Knowledge Graph Learningcited by 0
  12. 2024PAGE: Parametric Generative Explainer for Graph Neural Networkcited by 0
4 more
  1. 2025GIN-Graph: A Generative Interpretation Network for Model-Level Explanation of Graph Neural Networkscited by 0
  2. 2025Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarkingcited by 0
  3. 2025PruneGCRN: Minimizing and explaining spatio-temporal problems through node pruningcited by 0
  4. 2026Parallelizing Node-Level Explainability in Graph Neural Networkscited by 0