mlsanityData to 7 Oct 2026

CIExplainer++: Generating Causal and Interpretable Explanations for Graph Neural Networks

arXiv 2606.20747 ↗First posted 17 Jun 2026cs.LG stat.OTcited by 0

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Abstract (arXiv)

Explainable Artificial Intelligence aims to make black-box models more trustworthy by presenting, in a human-understandable manner, the elements that lead to the model's output. This involves both (i) identifying components and connections with genuine causal influence on outputs and (ii) translating such structures into an interpretable representation. For the former, we introduce CIExplainer, a novel perturbation-based method grounded in causal inference for explaining Graph Neural Networks (GNNs). CIExplainer identifies the subgraph with the highest causal effects on GNN predictions using the Potential Outcome Framework. We evaluate and compare CIExplainer on various GNN architectures (GCN, GraphSAGE, GAT, GIN) and datasets. To bridge subgraph explanations with human interpretability, we further propose G2TeXplainer, a method that transforms causal subgraphs into natural language explanations that capture both feature-level and relational information.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 39 of its 54 references that are in the map. First posted on arXiv 17 Jun 2026.

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References · 39

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  6. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  7. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  8. 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
  9. 2024A Survey on LLM-as-a-Judgecited by 1,384
  10. 2020Simple and Deep Graph Convolutional Networkscited by 857
  11. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  12. 2019Graph Neural Networks for Social Recommendationcited by 711
27 more
  1. 2020Parameterized Explainer for Graph Neural Networkcited by 423
  2. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  3. 2016Not Just a Black Box: Learning Important Features Through Propagating Activation Differencescited by 295
  4. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
  5. 2021GRAND: Graph Neural Diffusioncited by 258
  6. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 210
  7. 2020GraphLIME: Local Interpretable Model Explanations for Graph Neural Networkscited by 209
  8. 2020Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskingcited by 152
  9. 2021Generative Causal Explanations for Graph Neural Networkscited by 124
  10. 2021CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networkscited by 118
  11. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  12. 2022Anti-Symmetric DGN: a stable architecture for Deep Graph Networkscited by 58
  13. 2025PyG 2.0: Scalable Learning on Real World Graphscited by 49
  14. 2020Interpretable and Efficient Heterogeneous Graph Convolutional Networkcited by 48
  15. 2022GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Gamescited by 42
  16. 2017Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classificationcited by 40
  17. 2022Reinforced Causal Explainer for Graph Neural Networkscited by 34
  18. 2022Causal Explanations and XAIcited by 30
  19. 2021HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecastingcited by 22
  20. 2023Distill n' Explain: explaining graph neural networks using simple surrogatescited by 18
  21. 2024GraphXAIN: Narratives to Explain Graph Neural Networkscited by 11
  22. 2024GOAt: Explaining Graph Neural Networks via Graph Output Attributioncited by 10
  23. 2023RegExplainer: Generating Explanations for Graph Neural Networks in Regression Taskscited by 9
  24. 2024GraphNarrator: Generating Textual Explanations for Graph Neural Networkscited by 7
  25. 2025From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Contextcited by 6
  26. 2025Generalised Probabilistic Modelling and Improved Uncertainty Estimation in Comparative LLM-as-a-judgecited by 4
  27. 2023Extreme Multilabel Classification for Specialist Doctor Recommendation with Implicit Feedback and Limited Patient Metadatacited by 2

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