https://github.com/flyingdoog/awesome-graph-explainability-papers

Papers about explainability of GNNs
https://github.com/flyingdoog/awesome-graph-explainability-papers

List: awesome-graph-explainability-papers

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Papers about explainability of GNNs

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Awesome Lists containing this project

README

        
# awesome-graph-explainability-papers

Papers about the explainability of GNNs

### Surveys

1. [Proceedings of the IEEE 24] **Trustworthy Graph Neural Networks: Aspects, Methods and Trends** [paper](https://arxiv.org/abs/2205.07424)

2. [Arixv 23] **A Survey on Explainability of Graph Neural Networks** [paper](https://arxiv.org/abs/2306.01958)

3. [ACM computing survey] **A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges** [paper](https://dl.acm.org/doi/abs/10.1145/3618105)

4. [TPAMI 22]**Explainability in graph neural networks: A taxonomic survey**. *Yuan Hao, Yu Haiyang, Gui Shurui, Ji Shuiwang*. [paper](https://arxiv.org/pdf/2012.15445.pdf)

5. [Arxiv 22]**A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics** [paper](https://arxiv.org/pdf/2207.12599.pdf)

6. [Arxiv 22] **A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection** [paper](https://arxiv.org/abs/2205.10014)

7. [Big Data 2022]**A Survey of Explainable Graph Neural Networks for Cyber Malware Analysis** [paper](https://ieeexplore.ieee.org/abstract/document/10020943)

8. [Arxiv 23] **A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability**[paper](https://arxiv.org/abs/2204.08570)

9. [Arxiv 22] **Explaining the Explainers in Graph Neural Networks: a Comparative Study** [paper](https://arxiv.org/pdf/2210.15304.pdf)

10. [Book 23] **Generative Explanation for Graph Neural Network: Methods and Evaluation** [paper](http://sites.computer.org/debull/A23june/p64.pdf)

### Platforms

1. **PyTorch Geometric** [[Document]](https://pytorch-geometric.readthedocs.io/en/latest/tutorial/explain.html) [[Blog]](https://medium.com/@pytorch_geometric/graph-machine-learning-explainability-with-pyg-ff13cffc23c2)

2. **DIG: A Turnkey Library for Diving into Graph Deep Learning Research** [paper](https://www.jmlr.org/papers/v22/21-0343.html) [Code](https://github.com/divelab/DIG)

2. **GraphXAI: Evaluating Explainability for Graph Neural Networks** [paper](https://arxiv.org/abs/2208.09339v2) [Code](https://github.com/mims-harvard/graphxai)

3. **GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks** [paper](https://arxiv.org/abs/2206.09677) [Code](https://github.com/graphframex/graphframex)

4. **GNNExplainer and PGExplainer** [paper](https://openreview.net/forum?id=8JHrucviUf) [Code](https://github.com/LarsHoldijk/RE-ParameterizedExplainerForGraphNeuralNetworks)

5. **BAGEL: A Benchmark for Assessing Graph Neural Network Explanations** [[paper]](https://arxiv.org/abs/2206.13983)[Code](https://github.com/mandeep-rathee/bagel-benchmark)

### Most Influential Papers selected by [Cogdl](https://github.com/THUDM/cogdl/blob/master/gnn_papers.md#explainability

1. **Explainability in graph neural networks: A taxonomic survey**. *Yuan Hao, Yu Haiyang, Gui Shurui, Ji Shuiwang*. ARXIV 2020. [paper](https://arxiv.org/pdf/2012.15445.pdf)

2. **Gnnexplainer: Generating explanations for graph neural networks**. *Ying Rex, Bourgeois Dylan, You Jiaxuan, Zitnik Marinka, Leskovec Jure*. NeurIPS 2019. [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138248/) [code](https://github.com/RexYing/gnn-model-explainer)

3. **Explainability methods for graph convolutional neural networks**. *Pope Phillip E, Kolouri Soheil, Rostami Mohammad, Martin Charles E, Hoffmann Heiko*. CVPR 2019.[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Pope_Explainability_Methods_for_Graph_Convolutional_Neural_Networks_CVPR_2019_paper.pdf)

4. **Parameterized Explainer for Graph Neural Network**. *Luo Dongsheng, Cheng Wei, Xu Dongkuan, Yu Wenchao, Zong Bo, Chen Haifeng, Zhang Xiang*. NeurIPS 2020. [paper](https://arxiv.org/abs/2011.04573) [code](https://github.com/flyingdoog/PGExplainer)

5. **Xgnn: Towards model-level explanations of graph neural networks**. *Yuan Hao, Tang Jiliang, Hu Xia, Ji Shuiwang*. KDD 2020. [paper](https://dl.acm.org/doi/pdf/10.1145/3394486.3403085). 

6. **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](https://proceedings.neurips.cc/paper/2020/file/417fbbf2e9d5a28a855a11894b2e795a-Paper.pdf)

7. **PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks**. *Vu Minh, Thai My T.*. NeurIPS  2020.[paper](https://arxiv.org/pdf/2010.05788.pdf)

8. **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](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730613.pdf)

9. **GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media**. *Lu, Yi-Ju and Li, Cheng-Te*. ACL 2020.[paper](https://arxiv.org/pdf/2004.11648.pdf)

10. **On Explainability of Graph Neural Networks via Subgraph Explorations**. *Yuan Hao, Yu Haiyang, Wang Jie, Li Kang, Ji Shuiwang*. ICML 2021.[paper](https://arxiv.org/pdf/2102.05152.pdf)

### Year 2024

1. [ICML 24] **Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks**[[paper]](https://arxiv.org/abs/2402.02036)

2. [ICML 24] **Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks** [[paper]](https://arxiv.org/abs/2401.08627)

3. [ICML 24] **Graph Neural Network Explanations are Fragile** [[paper]](https://arxiv.org/pdf/2406.03193)

4. [ICML 24] **How Interpretable Are Interpretable Graph Neural Networks?** [[paper]](https://arxiv.org/abs/2406.07955)

5. [ICML 24] **Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation**[[paper]](https://arxiv.org/abs/2402.08845)

6. [ICML 24] **Explaining Graph Neural Networks via Structure-aware Interaction Index** [[paper]](https://icml.cc/virtual/2024/poster/34550)

7. [ICML 24] **EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time** [[paper]](https://arxiv.org/abs/2405.01762)

8. [ICLR 24] **GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks** [[paper]](https://openreview.net/forum?id=IjMUGuUmBI)

9. [ICLR 24] **GOAt: Explaining Graph Neural Networks via Graph Output Attribution** [[paper]](https://openreview.net/forum?id=2Q8TZWAHv4)

10. [ICLR 24] **Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks** [[paper]](https://openreview.net/forum?id=up6hr4hIQH)

11. [ICLR 24] **GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking** [[paper]](https://arxiv.org/abs/2310.01794)

12. [ICLR 24] **UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models** [[paper]](https://openreview.net/forum?id=0j9ZDzMPqr)

13. [TPAMI 24] **Towards Inductive and Efficient Explanations for Graph Neural Networks**[[paper]](https://ieeexplore.ieee.org/abstract/document/10423141)

20. [TMLR 24] **InduCE: Inductive Counterfactual Explanations for Graph Neural Networks** [[paper]](https://openreview.net/forum?id=RZPN8cgqST)

21. [SIGMOD 24]**View-based Explanations for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2401.02086)

22. [ICDE 24] **Generating Robust Counterfactual Witnesses for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2404.19519)

23. [ICSE 24] **Coca: Improving and Explaining Graph Neural Network-Based Vulnerability Detection Systems**[[paper]](https://arxiv.org/abs/2401.14886)

24. [AAAI 24] **Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/30168)

25. [AAAI 24] **Stratifed GNN Explanations through Sufficient Expansion**[[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/29180)

26. [AAAI 24] **Factorized Explainer for Graph Neural Networks**[[paper]](https://arxiv.org/abs/2312.05596)

27. [AAAI 24] **Self-Interpretable Graph Learning with Sufficient and Necessary Explanations**

28. [AAAI 24] **Explainable Origin-Destination Crowd Flow Interpolation via Variational Multi-Modal Recurrent Graph Auto-Encoder** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/28796)

13. [AISTATS 24] **Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process** [[paper]](https://proceedings.mlr.press/v238/kong24a.html)

14. [WWW 24] **Game-theoretic Counterfactual Explanation for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2402.06030)

15. [WWW 24] **EXGC: Bridging Efficiency and Explainability in Graph Condensation**[[paper]](https://arxiv.org/abs/2402.05962)

16. [WWW 24] **Adversarial Mask Explainer for Graph Neural Networks** [[paper]](https://dl.acm.org/doi/abs/10.1145/3589334.3645608)

17. [WWW 24] **Globally Interpretable Graph Learning via Distribution Matching**[[paper]](https://arxiv.org/abs/2306.10447)

18. [WWW 24] **GNNShap: Scalable and Accurate GNN Explanation using Shapley Values** [[paper]](https://dl.acm.org/doi/abs/10.1145/3589334.3645599)

19. [SDM 24] **XGExplainer: Robust Evaluation-based Explanation for Graph Neural Networks**[[paper]](https://epubs.siam.org/doi/abs/10.1137/1.9781611978032.8)

28. [ISSTA 2024] **Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation** [[paper]](https://arxiv.org/abs/2404.15687)

29. [AAAI workshop] **Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease**[[paper]](https://arxiv.org/abs/2403.02786)

30. [xAI] **Global Concept Explanations for Graphs by Contrastive Learning** [[paper]](https://arxiv.org/abs/2404.16532)

31. [Arxiv 24.06] **Explainable Graph Neural Networks Under Fire** [[paper]](https://arxiv.org/abs/2406.06417)

32. [Arxiv 24.06] **Explainable AI Security: Exploring Robustness of Graph Neural Networks to Adversarial Attacks** [[paper]](https://arxiv.org/abs/2406.13920)

33. [Arxiv 24.06] **Perks and Pitfalls of Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs** [[paper]](https://arxiv.org/abs/2406.15156)

34. [Arxiv 24.06] **Exploring Higher Order Structures in Graph Explanations** [[paper]](https://arxiv.org/abs/2406.03253)

35. [Arxiv 24.05] **Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networks** [[paper]](https://arxiv.org/abs/2405.12654)

36. [Arxiv 24.05] **MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation** [[paper]](https://arxiv.org/abs/2405.12519)

37. [Arxiv 24.05] **Detecting Complex Multi-step Attacks with Explainable Graph Neural Network** [[paper]](https://arxiv.org/abs/2405.11335)

38. [Preprint 24.05] **Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices** [[paper]](https://www.preprints.org/manuscript/202405.0037/v1)

39. [Arxiv 24.04] **Superior Polymeric Gas Separation Membrane Designed by Explainable Graph Machine Learning** [[paper]](https://arxiv.org/abs/2404.10903)

40. [Arxiv 24.04] **Improving the interpretability of GNN predictions through conformal-based graph sparsification** [[paper]](https://arxiv.org/abs/2404.12356)

41. [Arxiv 24.03] **GreeDy and CoDy: Counterfactual Explainers for Dynamic Graph**[[paper]](https://arxiv.org/abs/2403.16846)

42. [Arxiv 24.03] **Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation**[[paper]](https://arxiv.org/abs/2403.17384)

43. [Arxiv 24.03] **Securing GNNs: Explanation-Based Identification of Backdoored Training Graphs**[[paper]](https://arxiv.org/abs/2403.18136)

44. [Arixv 24.03] **Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations**[[paper]](https://arxiv.org/abs/2403.07849)

45. [Arxiv 24.03] **Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation**[[paper]](https://arxiv.org/abs/2403.17384)

46. [Arxiv 24.02] **PAC Learnability under Explanation-Preserving Graph Perturbations**[[paper]](https://arxiv.org/abs/2402.05039)

47. [Arxiv 24.02] **Explainable Global Wildfire Prediction Models using Graph Neural Networks**[[paper]](https://arxiv.org/abs/2402.07152)

48. [Arxiv 24.02] **Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks**[[paper]](https://arxiv.org/abs/2402.04710)

49. [Arxiv 24.01] **SynHIN: Generating Synthetic Heterogeneous Information Network for Explainable AI**[[paper]](https://arxiv.org/abs/2401.04133)

50. [Arxiv 24.01] **On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions**[[paper]](https://arxiv.org/abs/2401.00633)

51. [ASP=DAC 24] **LIPSTICK: Corruptibility-Aware and Explainable Graph Neural Network-based Oracle-Less Attack on Logic Locking**[[paper]](https://arxiv.org/abs/2402.04235)

52. [Biorxiv 24] **Community-aware explanations in knowledge graphs with XP-GNN**[[paper]](https://www.biorxiv.org/content/10.1101/2024.01.21.576302v1.abstract)

53. [Information Procs. & Mana.] **Towards explaining graph neural networks via preserving prediction ranking and structural dependency**[[paper]](https://www.sciencedirect.com/science/article/pii/S0306457323003084)

54. [Applied Energy]  **Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production** [[paper]](https://www.sciencedirect.com/science/article/pii/S0306261923015155)

55. [PAKDD 24] **Random Mask Perturbation Based Explainable Method of Graph Neural Networks** [[paper]](https://link.springer.com/chapter/10.1007/978-981-97-2259-4_2)

56. [Computational Materials Science] **Graph isomorphism network for materials property prediction along with explainability analysis**[[paper]](https://www.sciencedirect.com/science/article/pii/S0927025623006134)

57. [NN 24] **Explanatory subgraph attacks against Graph Neural Networks**[[paper]](https://www.sciencedirect.com/science/article/pii/S0893608024000030)

58. [Neural Networks 24] **CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis** [[paper]](https://arxiv.org/abs/2301.01642)

59. [NeuroImage 24] **BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping**[[paper]](https://www.sciencedirect.com/science/article/pii/S1053811924000892)

60. [PAKDD 24] **Toward Interpretable Graph Classification via Concept-Focused Structural Correspondence** [[paper]](https://link.springer.com/chapter/10.1007/978-981-97-2650-9_2)

61. [MedRxiv 24] **An Interpretable Population Graph Network to Identify Rapid Progression of Alzheimer’s Disease Using UK Biobank**[[paper]](https://www.medrxiv.org/content/10.1101/2024.03.27.24304966v1)

62. [IEEE TDSC 24] **TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support** [[paper]](https://arxiv.org/pdf/2306.13339.pdf)

63. [IEEE Transactions] **IEEE Transactions on Computational Social Systems**[[paper]](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6570650)

64. [Journal of Physics] **Explainer on GNN-based segmentation networks**[[paper]](https://iopscience.iop.org/article/10.1088/1742-6596/2711/1/012009/meta)

65. [Energy and AI] **Electricity demand forecasting at distribution and household levels using explainable causal graph neural network** [[paper]](https://www.sciencedirect.com/science/article/pii/S266654682400034X)

### Year 2023

1. [NeurIPS 23] **Interpretable Graph Networks Formulate Universal Algebra Conjectures**[[paper]](https://arxiv.org/abs/2307.11688)

2. [NeurIPS 23] **SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanation** [[paper]](https://openreview.net/forum?id=kBBsj9KRgh)

3. [NeurIPS 23] **Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks**[[paper]](https://openreview.net/forum?id=enfx8HM4Rp)

4. [NeurIPS 23] **D4Explainer: In-distribution Explanations of Graph Neural Network via Discrete Denoising Diffusion** [[paper]](https://arxiv.org/abs/2310.19321)

5. [NeurIPS 23] **TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery** [[paper]](https://arxiv.org/abs/2310.19324)

6. [NeurIPS 23] **V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs** [[paper]](https://openreview.net/forum?id=CtXXOaxDw7)

7. [NeurIPS 23] **Towards Self-Interpretable Graph-Level Anomaly Detection** [[paper]](https://arxiv.org/abs/2310.16520)

8. [NeurIPS 23] **Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis** [[paper]](https://openreview.net/forum?id=eD534mPhAg)

9. [NeurIPS 23] **Interpretable Prototype-based Graph Information Bottleneck** [[paper]](https://arxiv.org/abs/2310.19906)

10. [ICML 23] **Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching** [[paper]](https://openreview.net/forum?id=MocsSAUKlk)

11. [ICML 23] **Relevant Walk Search for Explaining Graph Neural Networks** [[paper]](https://openreview.net/forum?id=BDYIci7bVs)

12. [ICML 23] **Towards Understanding the Generalization of Graph Neural Networks** [[paper]](https://openreview.net/pdf?id=BhMyLk0YNy)

13. [ICLR 23] **GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks** [[paper]](https://arxiv.org/pdf/2209.07924.pdf)

14. [ICLR 23] **Global Explainability of GNNs via Logic Combination of Learned Concepts** [[paper]](https://openreview.net/forum?id=OTbRTIY4YS)

15. [ICLR 23] **Explaining Temporal Graph Models through an Explorer-Navigator Framework** [[paper]](https://openreview.net/forum?id=BR_ZhvcYbGJ)

16. [ICLR 23] **DAG Matters! GFlowNets Enhanced Explainer for Graph Neural Networks** [[paper]](https://openreview.net/forum?id=jgmuRzM-sb6)

17. [ICLR 23] **Interpretable Geometric Deep Learning via Learnable Randomness Injection** [[paper]](https://arxiv.org/abs/2210.16966)

18. [ICLR 23] **A Differential Geometric View and Explainability of GNN on Evolving Graphs** [[paper]](https://openreview.net/forum?id=lRdhvzMpVYV)

19. [KDD 23] **MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation** [[paper]](https://arxiv.org/abs/2307.07832)

20. [KDD 23] **Counterfactual Learning on Heterogeneous Graphs with Greedy Perturbation** [[paper]](https://repository.kaust.edu.sa/handle/10754/693484)

21. [KDD 23] **Empower Post-hoc Graph Explanations with Information Bottleneck: A Pre-training and Fine-tuning Perspective**[[paper]](https://dl.acm.org/doi/10.1145/3580305.3599330)

22. [KDD 23] **Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining.**[[paper]](https://dl.acm.org/doi/10.1145/3580305.3599413)

23. [KDD 23] **Shift-Robust Molecular Relational Learning with Causal Substructure** [[paper]](https://dl.acm.org/doi/abs/10.1145/3580305.3599437)

24. [AAAI 23] **Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis** [[paper]](https://arxiv.org/abs/2208.10609)

25. [AAAI 23] **On the Limit of Explaining Black-box Temporal Graph Neural Networks** [[paper]](https://arxiv.org/abs/2212.00952)

26. [AAAI 23] **Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/download/26040/25812)

27. [AAAI 23] **Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery**  [[paper]](https://openreview.net/forum?id=W2OStztdMhc)

28. [VLDB 23] **HENCE-X: Toward Heterogeneity-agnostic Multi-level Explainability for Deep Graph Networks** [[paper]](https://www.vldb.org/pvldb/vol16/p2990-lv.pdf)

29. [VLDB 23] **On Data-Aware Global Explainability of Graph Neural Networks** [[paper]](https://www.vldb.org/pvldb/vol16/p3447-lv.pdf)

30. [AISTATS 23] **Distill n' Explain: explaining graph neural networks using simple surrogates** [[Paper]](https://arxiv.org/abs/2303.10139)

31. [AISTATS 23] **Probing Graph Representations** [[paper]](https://proceedings.mlr.press/v206/akhondzadeh23a/akhondzadeh23a.pdf)

32. [ICDE 23] **INGREX: An Interactive Explanation Framework for Graph Neural Networks**[[paper]](https://arxiv.org/pdf/2211.01548.pdf)

33. [ICDE 23] **Jointly Attacking Graph Neural Network and its Explanations** [[paper]](https://arxiv.org/pdf/2108.03388.pdf)

34. [WWW 23]**PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction** [[paper]](https://arxiv.org/pdf/2302.12465.pdf)

35. [ICDM 23] **Limitations of Perturbation-based Explanation Methods for Temporal Graph Neural Networks**

36. [ICDM 23] **Interpretable Subgraph Feature Extraction for Hyperlink Prediction**[[paper]](https://www.researchgate.net/publication/378000024_Interpretable_Subgraph_Feature_Extraction_for_Hyperlink_Prediction)

37. [WSDM 23]**Interpretable Research Interest Shift Detection with Temporal Heterogeneous Graphs** [[paper]](https://dl.acm.org/doi/pdf/10.1145/3539597.3570453)

38. [WSDM 23]**Cooperative Explanations of Graph Neural Networks** [[paper]](https://dl.acm.org/doi/pdf/10.1145/3539597.3570378)

39. [WSDM 23]**Towards Faithful and Consistent Explanations for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2205.13733)

40. [WSDM 23] **Global Counterfactual Explainer for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2210.11695)

41. [CIKM 23] **Explainable Spatio-Temporal Graph Neural Networks** [[paper]](https://dl.acm.org/doi/abs/10.1145/3583780.3614871)

42. [CIKM 23] **DuoGAT: Dual Time-oriented Graph Attention Networks for Accurate, Efficient and Explainable Anomaly Detection on Time-series.** [[paper]](https://dl.acm.org/doi/abs/10.1145/3583780.3614857)

43. [CIKM 23] **Heterogeneous Temporal Graph Neural Network Explainer** [[paper]](https://dl.acm.org/doi/abs/10.1145/3583780.3614909)

44. [CIKM 23] **ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks**[[paper]]()

45. [CIKM 23] **KG4Ex: An Explainable Knowledge Graph-Based Approach for Exercise Recommendation** [[paper]](https://dl.acm.org/doi/10.1145/3583780.3614943)

46. [ECML-PKDD 23] **ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning** [[paper]](https://arxiv.org/abs/2307.01053)

47. [TPAMI 23] **FlowX: Towards Explainable Graph Neural Networks via Message Flows** [[paper]](https://arxiv.org/abs/2206.12987)

48. [TAI] **Prototype-based interpretable graph neural networks.** [[paper]](https://ieeexplore.ieee.org/document/9953541)

49. [TKDE 23] **Counterfactual Graph Learning for Anomaly Detection on Attributed Networks** [[paper]](https://ieeexplore.ieee.org/document/10056298)

50. [Scientific Data 23 ] **Evaluating explainability for graph neural networks** [[paper]](https://www.nature.com/articles/s41597-023-01974-x)

51. [Nature Communications 23] **Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking** [[paper]](https://www.nature.com/articles/s41467-023-38192-3)

52. [ACM Computing Surveys 23] **A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation**  [[paper]](https://arxiv.org/abs/2210.12089)

53. [TIST 23] **Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment** [[paper]](https://arxiv.org/abs/2301.02791)

54. [Openreview 23] **STExplainer: Global Explainability of GNNs via Frequent SubTree Mining** [[paper]](https://openreview.net/forum?id=HgSfV6sGIn)

55. [GLFrontiers 23] **Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts** [[paper]](https://openreview.net/forum?id=wrqAn3AJA1)

56. [Openreview 23] **Iterative Graph Neural Network Enhancement Using Explanations** [[paper]](https://openreview.net/forum?id=qp0oVaFGm0)

58. [Openreview 23] **Interpretable and Convergent Graph Neural Network Layers at Scale** [[paper]](https://openreview.net/forum?id=uYTaVRkKvz)

60. [NeurIPS 2023 Workshop XAIA] **GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Networks Explanations** [[paper]](https://openreview.net/forum?id=88MalncLgU)

61. [NeurIPS 2023 Workshop XAIA] **On the Consistency of GNN Explainability Methods** [[paper]](https://openreview.net/forum?id=tiLZkab8TP)

62. [Arxiv 23] **Evaluating Neighbor Explainability for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2311.08118)

64. [Arxiv 23] **DyExplainer: Explainable Dynamic Graph Neural Networks** [[paper]](https://arxiv.org/abs/2310.16375)

65. [Arxiv 23] **Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation**[[paper]](https://arxiv.org/abs/2312.17301)

66. [AICS 23] **A subgraph interpretation generative model for knowledge graph link prediction based on uni-relation transformation** [[paper]](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12803/1280339/A-subgraph-interpretation-generative-model-for-knowledge-graph-link-prediction/10.1117/12.3009388.short?SSO=1)

67. [GUT 23] **Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study** [[paper]](https://gut.bmj.com/content/gutjnl/early/2023/05/11/gutjnl-2023-329512.full.pdf)

68. [PR 2023] **Towards self-explainable graph convolutional neural network with frequency adaptive inception** [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S0031320323006891)

69. [MLG 2023] **Understanding how explainers work in graph neural networks** [[paper]](https://mlg-europe.github.io/papers/241.pdf)

70. [MLG 2023] **Graph Model Explainer Tool** [[paper]](https://www.mlgworkshop.org/2023/papers/MLG__KDD_2023_paper_5.pdf)

71. [Information Science 23] **Robust explanations for graph neural network with neuron explanation component** [[paper]](https://www.sciencedirect.com/science/article/pii/S0020025523013701)

72. [Recsys 23] **Explainable Graph Neural Network Recommenders; Challenges and Opportunities** [[paper]](https://dl.acm.org/doi/abs/10.1145/3604915.3608875)

73. [xAI  23] **Counterfactual Explanations for Graph Classification Through the Lenses of Density** [[paper]](https://arxiv.org/abs/2307.14849)

74. [XAI 23] **Evaluating Link Prediction Explanations for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2308.01682

75. [xAI 23] **XInsight: Revealing Model Insights for GNNs with Flow-based Explanations** [[paper]](https://arxiv.org/pdf/2306.04791.pdf)

76. [xAI 23] **Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies** [[paper]](https://arxiv.org/abs/2305.15961)

77. [xAI 23] **MEGAN: Multi Explanation Graph Attention Network** [[paper]](https://openreview.net/forum?id=H6LVUiHzYDE)

78. [XKDD 23] **Game Theoretic Explanations for Graph Neural Networks** [[paper]](http://xkdd2023.isti.cnr.it/papers/424.pdf)

79. [XKDD 23] **From Black Box to Glass Box: Evaluating Faithfulness of Process Predictions with GCNNs** [[paper]](http://xkdd2023.isti.cnr.it/papers/425.pdf)

80. [IJCNN 23] **MEGA: Explaining Graph Neural Networks with Network Motifs** [[paper]](https://doi.org/10.1109/IJCNN54540.2023.10191684)

81. [LOG Poster 23] **On the Robustness of Post-hoc GNN Explainers to Label Noise** [[paper]](https://arxiv.org/abs/2309.01706)

82. [LOG Poster 23] **How Faithful are Self-Explainable GNNs?** [[paper]](https://arxiv.org/abs/2308.15096)

83. [LOG Poster 23] **RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task** [[paper]](https://arxiv.org/abs/2307.07840)

84. [LOG Poster 23] **Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples** [[paper]](https://arxiv.org/abs/2212.02651)

85. [Bioriv 23] **Building explainable graph neural network by sparse learning for the drug-protein binding prediction** [[paper]](https://www.biorxiv.org/content/10.1101/2023.08.28.555203v1.abstract)

86. [ICAID 2023] **Explanations for Graph Neural Networks via Layer Analysis.** [[paper]](https://www.atlantis-press.com/proceedings/icaid-23/125990065)

87. [ECAI 23] **XGBD: Explanation-Guided Graph Backdoor Detection** [[paper]](https://arxiv.org/abs/2308.04406)

88. [IEEE Transactions on Consumer Electronics 23] **Human Pose Prediction Using Interpretable Graph Convolutional Network for Smart Home** [[paper]](https://arxiv.org/abs/2308.04406)

89. [KBS 23] **KE-X: Towards subgraph explanations of knowledge graph embedding based on knowledge information gain** [[paper]](http://sites.computer.org/debull/A23june/A23JUNE-CD.pdf#page=64)

90. [ICML workshop 23] **Generating Global Factual and Counterfactual Explainer for Molecule under Domain Constraints** [[paper]](https://openreview.net/forum?id=qElXYQqxQh)

91. [Thesis 23] **Developing interpretable graph neural networks for high dimensional feature spaces** [[paper]](https://pub.tik.ee.ethz.ch/students/2022-HS/BA-2022-43.pdf)

92. [Thesis 23] **Evaluation of Explainability Methods on Single-Cell Classification Tasks Using Graph Neural Networks** [[paper]](https://www.semanticscholar.org/paper/Evaluation-of-Explainability-Methods-on-Single-Cell-Singh-Kobayashi/85f4aba430387a337ec3a4b2aa39bfc7361dea1f)

93. [Arxiv 23] **On the Interplay of Subset Selection and Informed Graph Neural Networks** [[paper]](https://www.semanticscholar.org/paper/On-the-Interplay-of-Subset-Selection-and-Informed-Breustedt-Climaco/2c76331ac1676ed7fdd51b8cd744765628e0a181)

94. [ISSTA23] **Interpreters for GNN-Based Vulnerability Detection: Are We There Yet?** [[paper]](https://www.semanticscholar.org/paper/Interpreters-for-GNN-Based-Vulnerability-Detection%3A-Hu-Wang/6bb9c86483f212a631324ba9b47c344d419a428a)

95. [ICECAI23] **Improved GraphSVX for GNN Explanations Based on Cross Entropy** [[paper]](https://www.semanticscholar.org/paper/Improved-GraphSVX-for-GNN-Explanations-Based-on-Yu-Liang/b01c4f2c4d54723b590a828d4e1b4cdbfea5dad4)

96. [ICRA Workshop 23] **Towards Semantic Interpretation and Validation of Graph Attention-based Explanations** [[paper]](https://openreview.net/forum?id=ymyQeqatQqQ)

97. [Arxiv 23] **Graph Neural Network based Log Anomaly Detection and Explanation** [[paper]](https://arxiv.org/abs/2307.00527)

98. [Arxiv 23] **Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features** [[paper]](https://arxiv.org/abs/2306.00934)

99. [Thesis 23] **Interpretability of Graphical Models** [[paper]](https://search.proquest.com/openview/1e61b389a59936e319974be0e3fd1af5/1?pq-origsite=gscholar&cbl=18750&diss=y)

101. [Bioengineering 2023] **Personalized Explanations for Early Diagnosis of Alzheimer's Disease Using Explainable Graph Neural Networks with Population Graphs** [[paper]](https://www.mdpi.com/2306-5354/10/6/701)

102. [BDSC 2023] **MDC: An Interpretable GNNs Method Based on Node Motif Degree and Graph Diffusion Convolution** [[paper]] (https://link.springer.com/chapter/10.1007/978-981-99-3925-1_24)

104. [Information Science 2023] **Explainability techniques applied to road traffic forecasting using Graph Neural Network models** [[paper]](https://www.sciencedirect.com/science/article/pii/S0020025523009052)

105. [Arxiv 23] **Efficient GNN Explanation via Learning Removal-based Attribution** [[paper]](https://arxiv.org/abs/2306.05760)

106. [Arxiv 23] **Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity** [[paper]](https://arxiv.org/pdf/2306.04835.pdf)

107. [ICLR Tiny 23] **Message-passing selection: Towards interpretable GNNs for graph classification** [[paper]](https://openreview.net/forum?id=99Go96dla5y)

108. [ICLR Tiny 23] **Revisiting CounteRGAN for Counterfactual Explainability of Graphs** [[paper]](https://openreview.net/forum?id=d0m0Rl15q3g)

109. [MICCAI Workshop 23] **IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction** [[paper]](https://arxiv.org/pdf/2103.15587.pdf)

110. [Arxiv 23] **Robust Ante-hoc Graph Explainer using Bilevel Optimization** [[paper]](https://arxiv.org/abs/2305.15745)

111. [GRADES & NDA'23] **A Demonstration of Interpretability Methods for Graph Neural Networks** [[paper]](https://homes.cs.aau.dk/~Arijit/Papers/gInterpreter_GRADES_NDA23.pdf)

112. [Arxiv 23] **Self-Explainable Graph Neural Networks for Link Prediction**  [[paper]](https://arxiv.org/abs/2305.12578)

113. [ChemRxiv 23] **Interpreting Graph Neural Networks with Myerson Values for Cheminformatics Approaches** [[paper]](https://chemrxiv.org/engage/chemrxiv/article-details/6456c89707c3f0293753101d)

114. [Neural Networks 23] **Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness** [[paper]](https://arxiv.org/abs/2304.12036)

115. [ICASSP 23] **Towards a More Stable and General Subgraph Information Bottleneck** [[paper]](https://ieeexplore.ieee.org/document/10094812)

116. [ESANN 23] **Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability** [[Paper]](https://arxiv.org/abs/2304.07152)

117. [IEEE Access] **Generating Real-Time Explanations for GNNs via Multiple Specialty Learners and Online Knowledge Distillation** [[Paper]](https://ieeexplore.ieee.org/document/10107968)

118. [IEEE Access] **Providing Post-Hoc Explanation for Node Representation Learning Models Through Inductive Conformal Predictions** [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10003193&tag=1)

119. [Journal of Software 23] **A Slice-level vulnerability detection and interpretation method based on graph neural network** [[paper]](http://www.jos.org.cn/josen/article/abstract/mr008)

120. [Automation in Construction 23] **Learning from explainable data-driven tunneling graphs: A spatio-temporal graph convolutional network for clogging detection** [[paper]](https://www.sciencedirect.com/science/article/pii/S0926580523000018)

121. [Briefings in Bioinformatics] **Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism** [[paper]](https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbac534/6918752)

122. [Briefings in Bioinformatics] **Identification of vital chemical information via visualization of graph neural networks** [[paper]](https://academic.oup.com/bib/article/24/1/bbac577/6936421)

123. [Bioinformatics 23] **Explainable Multilayer Graph Neural Network for Cancer Gene Prediction** [[paper]](https://arxiv.org/pdf/2301.08831.pdf)

124. [ICLR Workshop 23] **GCI: A Graph Concept Interpretation Framework** [[paper]](https://arxiv.org/abs/2302.04899)

125. [Arxiv 23] **Structural Explanations for Graph Neural Networks using HSIC** [[paper]](https://arxiv.org/abs/2302.02139)

126. [Internet of Things 23] **XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics** [[paper]](https://arxiv.org/abs/2207.09088)

127. [JOS23] **A Generic Explaining & Locating Method for Malware Detection based on Graph Neural Networks** [[paper]](https://www.jos.org.cn/josen/article/abstract/7123)

### Year 2022

1. [NeurIPS 22] **GStarX:Explaining Graph-level Predictions with Communication Structure-Aware Cooperative Games** [[paper]](https://openreview.net/pdf?id=Qry8exovcNA)

2. [NeurIPS 22] **Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure** [[paper]](https://arxiv.org/abs/2209.14107)

3. [NeurIPS 22] **Task-Agnostic Graph Neural Explanations** [[paper]](https://openreview.net/pdf?id=NQrx8EYMboO)

4. [NeurIPS 22] **CLEAR: Generative Counterfactual Explanations on Graphs**[[paper]](https://arxiv.org/abs/2210.08443)

5. [ICML 22] **Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism** [[paper]](https://arxiv.org/abs/2201.12987v1)

6. [ICLR 22] **DEGREE: Decomposition Based Explanation for Graph Neural Networks** [[paper]](https://openreview.net/pdf?id=Ve0Wth3ptT_)

7. [ICLR 22] **Explainable GNN-Based Models over Knowledge Graphs** [[paper]](https://openreview.net/attachment?id=CrCvGNHAIrz&name=pdf)

8. [ICLR 22] **Discovering Invariant Rationales for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2201.12872)

9. [KDD 22] **On Structural Explanation of Bias in Graph Neural Networks** [[paper]](https://arxiv.org/abs/2206.12104)

10. [KDD 22] **Causal Attention for Interpretable and Generalizable Graph Classification** [[paper]](https://arxiv.org/abs/2112.15089)

10. [CVPR 22] **OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks** [[paper]](https://wanyu-lin.github.io/assets/publications/wanyu-cvpr2022.pdf)

81. [CVPR 22] **Improving Subgraph Recognition with Variational Graph Information Bottleneck** [[paper]](https://arxiv.org/abs/2112.09899)

12. [AISTATS 22] **Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods** [[paper]](https://arxiv.org/abs/2106.09078)

13. [AISTATS 22] **CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks**  [[paper]](https://arxiv.org/abs/2102.03322)

14. [TPAMI 22] **Differentially Private Graph Neural Networks for Whole-Graph Classification** [[paper]](https://arxiv.org/abs/2212.03806)

15. [TPAMI 22] **Reinforced Causal Explainer for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2204.11028)

17. [VLDB 22] **xFraud: Explainable Fraud Transaction Detection on Heterogeneous Graphs** [[paper]](https://arxiv.org/pdf/2011.12193.pdf)

18. [LOG 22]**GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2206.09677)

19. [LOG 22] **Towards Training GNNs using Explanation Directed Message Passing** [[paper]](https://arxiv.org/abs/2211.16731)

20. [The Webconf 22] **Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning** [[paper]](https://arxiv.org/abs/2202.08816)

21. [AAAI 22] **Prototype-Based Explanations for Graph Neural Networks** [[paper]](https://www.aaai.org/AAAI22Papers/SA-00396-ShinY.pdf)

36. [AAAI 22] **KerGNNs: Interpretable Graph Neural Networks with Graph Kernels**[[paper]](https://arxiv.org/pdf/2201.00491.pdf)

37. [AAAI 22] **ProtGNN: Towards Self-Explaining Graph Neural Networks** [[paper]](https://arxiv.org/abs/2112.00911)

23. [IEEE Big Data 22] **Trade less Accuracy for Fairness and Trade-off Explanation for GNN** [[paper]](https://ieeexplore.ieee.org/abstract/document/10020318)

28. [CIKM 22] **GRETEL: A unified framework for Graph Counterfactual Explanation Evaluation**  [[paper]](https://arxiv.org/abs/2206.02957)

29. [CIKM 22] **GRETEL: Graph Counterfactual Explanation Evaluation Framework**[[paper]](https://dl.acm.org/doi/abs/10.1145/3511808.3557608)

30. [CIKM 22] **A Model-Centric Explainer for Graph Neural Network based Node Classification** [[paper]](https://dl.acm.org/doi/10.1145/3511808.3557535)

31. [IJCAI 22] **What Does My GNN Really Capture? On Exploring Internal GNN Representations** [[paper]](https://hal.archives-ouvertes.fr/hal-03700710/)

32. [ECML PKDD 22] **Improving the quality of rule-based GNN explanations** [[paper]](https://kdd.isti.cnr.it/xkdd2022/papers/XKDD_2022_paper_2436.pdf)

33. [MICCAI 22] **Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis** [[paper]](https://arxiv.org/abs/2207.00813)

34. [MICCAI 22] **Sparse Interpretation of Graph Convolutional Networks for Multi-modal Diagnosis of Alzheimer’s Disease** [[paper]](https://link.springer.com/chapter/10.1007/978-3-031-16452-1_45)

38. [EuroS&P 22] **Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis** [[paper]](https://ieeexplore.ieee.org/abstract/document/9797387?casa_token=1AvRK3S4eJQAAAAA:8PXcOA8iU1ketRMdu6YVMBMcfZKjF7MIVujPpHTpjdc2O9r1cvUg8usfRiOYZ5Fe-MKJi4Y)

39. [INFOCOM 22] **Interpretability Evaluation of Botnet Detection Model based on Graph Neural Network** [[paper]](https://ieeexplore.ieee.org/document/9798287)

40. [GLOBECOM 22] **Shapley Explainer - An Interpretation Method for GNNs Used in SDN** [[paper]](https://ieeexplore.ieee.org/abstract/document/10001460)

41. [GLOBECOM 22] **An Explainer for Temporal Graph Neural Networks** [[paper]]([https://arxiv.org/pdf/2209.00807.pdf])

42. [TKDE 22] **Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks** [[paper]](https://arxiv.org/abs/2105.08621)

43. [TNNLS 22] **Interpretable Graph Reservoir Computing With the Temporal Pattern Attention**  [[paper]](https://ieeexplore.ieee.org/abstract/document/10003110)

44. [TNNLS22] **A Meta-Learning Approach for Training Explainable Graph Neural Networks** [[paper]](https://ieeexplore.ieee.org/abstract/document/9772740)

45. [TNNLS 22] **Explaining Deep Graph Networks via Input Perturbation** [[paper]](https://pubmed.ncbi.nlm.nih.gov/35446771/)

63. [TNNLS 22] **A Meta-Learning Approach for Training Explainable Graph Neural Network** [[paper]](https://arxiv.org/pdf/2109.09426.pdf)

47. [DMKD 22] **On GNN explanability with activation patterns**  [[paper]](https://hal.archives-ouvertes.fr/hal-03367714/file/hal.pdf)

48. [KBS 22] **EGNN: Constructing explainable graph neural networks via knowledge distillation** [[paper]](https://www.sciencedirect.com/science/article/pii/S0950705122001289?via%3Dihub)

49. [XKDD 22] **GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations**  [[paper]](https://arxiv.org/abs/2208.04222)

50. [AI 22] **Are Graph Neural Network Explainers Robust to Graph Noises?** [[paper]](https://link.springer.com/chapter/10.1007/978-3-031-22695-3_12)

52. [BRACIS 22] **ConveXplainer for Graph Neural Networks** [[paper]](https://link.springer.com/chapter/10.1007/978-3-031-21689-3_41)

53. [GLB 22] **An Explainable AI Library for Benchmarking Graph Explainers** [[paper]](https://graph-learning-benchmarks.github.io/assets/papers/glb2022/An_Explainable_AI_Library_for_Benchmarking_Graph_Explainers.pdf)

54. [DASFAA 22] **On Global Explainability of Graph Neural Networks** [[paper]](https://link.springer.com/chapter/10.1007/978-3-031-00123-9_52)

55. [ISBI 22] **Interpretable Graph Convolutional Network Of Multi-Modality Brain Imaging For Alzheimer’s Disease Diagnosis** [[paper]](https://ieeexplore.ieee.org/abstract/document/9761449?casa_token=w3IlSZNlKwcAAAAA:Xvh04eK29bZtbkRq5Eg3jUZURS3qs1k3AA1bhnnN2kKWmIjBnh7alAiy98zBgsHFtvFQqV0IYA)

56. [Bioinformatics] **GNN-SubNet: disease subnetwork detection with explainable Graph Neural Networks** [[paper]](https://academic.oup.com/bioinformatics/article/38/Supplement_2/ii120/6702000?login=false)

57. [Medical Imaging 2022]  **Phenotype guided interpretable graph convolutional network analysis of fMRI data reveals changing brain connectivity during adolescence** [[paper]](https://www.semanticscholar.org/paper/Phenotype-guided-interpretable-graph-convolutional-Orlichenko-Qu/d05adc7c772780be4b99a169441696017d49c6ed)

83. [NeuroComputing 22] **Perturb more, trap more: Understanding behaviors of graph neural networks** [[paper]](https://www.sciencedirect.com/science/article/pii/S0925231222004404?casa_token=6KLu9elyyLMAAAAA:hM0eGpfSnLxF0V8fZJdoDE3hkalzK2yccBJl3X9KN-Btu_xDSZmmbORIfkYdK5rgjTr7MReeFxc)

84. [DSN 22] **CFGExplainer: Explaining Graph Neural Network-Based Malware Classification from Control Flow Graphs** [[paper]](http://www.cs.binghamton.edu/~ghyan/papers/dsn22.pdf)

118. [IEEE Access 22] **Providing Node-level Local Explanation for node2vec through Reinforcement Learning** [[paper]](https://mlog-workshop.github.io/papers/MLoG%20Providing%20Node-level%20Local%20Explanation%20for%20node2vec%20through%20Reinforcement%20Learning.pdf)

119. [Patterns 22] **Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction** [[paper]](https://arxiv.org/pdf/2107.04119.pdf)

120. [Arxiv 22] **GRAPHSHAP: Motif-based Explanations for Black-box Graph Classifiers** [[paper]](https://arxiv.org/abs/2202.08815)

121. [IEEE Access 22] **Providing Post-Hoc Explanation for Node Representation Learning Models Through Inductive Conformal Predictions** [[paper]](https://ieeexplore.ieee.org/abstract/document/10003193)

122. [IEEE 22] **Explaining Graph Neural Networks With Topology-Aware Node Selection: Application in Air Quality Inference**  [[paper]](https://ieeexplore.ieee.org/document/9801665)

123. [BioRxiv 22] **GNN-SubNet: disease subnetwork detection with explainable Graph Neural Networks** [[paper]](https://www.biorxiv.org/content/10.1101/2022.01.12.475995v1)

124. [IEEE Robotics and Automation Letters 22] **Efficient and Interpretable Robot Manipulation with Graph Neural Networks** [[paper]](https://arxiv.org/pdf/2102.13177.pdf)

125. [Arxiv 22] **Deconfounding to Explanation Evaluation in Graph Neural Networks** [[paper]](https://arxiv.org/abs/2201.08802)

126. [ICCPR 22] **GANExplainer: GAN-based Graph Neural Networks Explainer**  [[paper]](https://arxiv.org/abs/2301.00012)

127. [Arxiv 22] **On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach** [[paper]](https://arxiv.org/abs/2212.07056)

129. [Arxiv 22] **Exploring Explainability Methods for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2211.01770)

130. [Arxiv 22] **PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2210.17159)

132. [Arxiv 22] **Toward Multiple Specialty Learners for Explaining GNNs via Online Knowledge Distillation** [[paper]](https://arxiv.org/abs/2210.11094)

134. [Openreview 23] **TGP: Explainable Temporal Graph Neural Networks for Personalized Recommendation** [[paper]](https://openreview.net/forum?id=EGobBwPc1J-)

136. [Openreview 23] **On Regularization for Explaining Graph Neural Networks: An Information Theory Perspective** [[paper]](https://openreview.net/forum?id=5rX7M4wa2R_)

137. [Arxiv 22] **L2XGNN: Learning to Explain Graph Neural Networks** [[paper]](https://arxiv.org/pdf/2209.14402.pdf)

138. [Arxiv 22] **Towards Prototype-Based Self-Explainable Graph Neural Network** [[paper]](https://arxiv.org/abs/2210.01974)

139. [Arxiv 22] **PGX: A Multi-level GNN Explanation Framework Based on Separate Knowledge Distillation Processes** [[paper]](https://arxiv.org/abs/2208.03075)

141. [Arxiv 22] **Explainability in subgraphs-enhanced Graph Neural Networks** [[paper]](https://arxiv.org/abs/2209.07926)

142. [Arxiv 22] **Defending Against Backdoor Attack on Graph Neural Network by Explainability** [[paper]](https://arxiv.org/pdf/2209.02902.pdf)

144. [Arxiv 22] **Explaining Dynamic Graph Neural Networks via Relevance Back-propagation** [[paper]](https://arxiv.org/abs/2207.11175)

145. [Arxiv 22] **EiX-GNN : Concept-level eigencentrality explainer for graph neural networks** [[paper]](https://arxiv.org/abs/2206.03491)

146. [Arxiv 22] **MotifExplainer: a Motif-based Graph Neural Network Explainer**  [[paper]](https://arxiv.org/abs/2202.00519)

147. [Arxiv 22] **Faithful Explanations for Deep Graph Models** [[paper]](https://arxiv.org/abs/2205.11850)

148. [Arxiv 22] **Towards Explanation for Unsupervised Graph-Level Representation Learning** [[paper]](https://arxiv.org/abs/2205.09934)

149. [Arxiv 22] **BAGEL: A Benchmark for Assessing Graph Neural Network Explanations** [[paper]](https://arxiv.org/abs/2206.13983)

150. [Arxiv 22] **BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck** [[paper]](https://arxiv.org/abs/2205.03612)

151. [Arxiv 22] **A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability** [[paper]](https://arxiv.org/abs/2204.08570)

152. [Arxiv 22] **Explainability in Graph Neural Networks: An Experimental Survey** [[paper]](https://arxiv.org/abs/2203.09258)

153. [IEEE TSIPN 22] **Explainability and Graph Learning from Social Interactions** [[paper]](https://arxiv.org/pdf/2203.07494.pdf)

154. [Arxiv 22] **Cognitive Explainers of Graph Neural Networks Based on Medical Concepts** [[paper]](https://arxiv.org/abs/2201.07798)

### Year 2021

1. [NeurIPS 21] **SALKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning** [[paper]](https://arxiv.org/pdf/2104.08793.pdf)

2. [NeurIPS 2021] **Reinforcement Learning Enhanced Explainer for Graph Neural Networks** [[paper]](http://recmind.cn/papers/explainer_nips21.pdf)

3. [NeurIPS 2021] **Towards Multi-Grained Explainability for Graph Neural Networks** [[paper]](http://staff.ustc.edu.cn/~hexn/papers/nips21-explain-gnn.pdf)

21. [NeurIPS 2021] **Robust Counterfactual Explanations on Graph Neural Networks** [[paper]](https://arxiv.org/abs/2107.04086)

22. [ICML 2021] **On Explainability of Graph Neural Networks via Subgraph Explorations**[[paper]](https://arxiv.org/abs/2102.05152)

32. [ICML 2021] **Generative Causal Explanations for Graph Neural Networks**[[paper]](https://arxiv.org/abs/2104.06643)

33. [ICML 2021] **Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity**[[paper]](https://arxiv.org/abs/2105.04854)

34. [ICML 2021] **Automated Graph Representation Learning with Hyperparameter Importance Explanation**[[paper]](http://proceedings.mlr.press/v139/wang21f/wang21f.pdf)

26. [ICLR 2021] **Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking**[[paper]](https://arxiv.org/abs/2010.00577)

52. [ICLR 2021] **Graph Information Bottleneck for Subgraph Recognition** [[paper]](https://arxiv.org/pdf/2010.05563.pdf)

53. [KDD 2021] **When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods**[[paper]](https://dl.acm.org/doi/abs/10.1145/3447548.3467283)

54. [KDD 2021] **Counterfactual Graphs for Explainable Classification of Brain Networks** [[paper]](https://arxiv.org/abs/2106.08640)

27. [CVPR 2021] **Quantifying Explainers of Graph Neural Networks in Computational Pathology**.[[paper]](https://arxiv.org/pdf/2011.12646.pdf)

40. [NAACL 2021] **Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network**. [[paper]](https://aclanthology.org/2021.naacl-main.156.pdf)

28. [AAAI 2021] **Motif-Driven Contrastive Learning of Graph Representations** [[paper]](https://arxiv.org/pdf/2012.12533.pdf)

56. [TPAMI 21] **Higher-Order Explanations of Graph Neural Networks via Relevant Walks** [[paper]](https://ieeexplore.ieee.org/document/9547794)

57. [WWW 2021] **Interpreting and Unifying Graph Neural Networks with An Optimization Framework** [[paper]](https://arxiv.org/abs/2101.11859)

59. [Genome medicine 21] **Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer** [[paper]](https://www.semanticscholar.org/paper/Explaining-decisions-of-Graph-Convolutional-Neural-Chereda-Bleckmann/49a4e339182b2b304304c8837b09ce3e0951a616)

60. [IJCKG 21] **Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules** [[paper]](https://arxiv.org/abs/2112.08589)

61. [RuleML+RR 21] **Combining Sub-Symbolic and Symbolic Methods for Explainability** [[paper]](https://arxiv.org/abs/2112.01844)

62. [PAKDD 21] **SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction** [[paper]](https://arxiv.org/abs/2102.04627)

63. [J. Chem. Inf. Model] **Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment** [[paper]](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.0c01344)

64. [BioRxiv 21] **APRILE: Exploring the Molecular Mechanisms of Drug Side Effects with Explainable Graph Neural Networks** [[paper]](https://www.biorxiv.org/content/10.1101/2021.07.02.450937v2.abstract)

65. [ISM 21] **Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks** [[paper]](https://arxiv.org/pdf/2111.00722.pdf)

67. [Arxiv 21] **Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows** [[paper]](https://arxiv.org/abs/2112.09895)

68. [Arxiv 21] **SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods** [[paper]](https://arxiv.org/pdf/2106.08532.pdf)

69. [Arxiv 21] **Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation** [[paper]](https://arxiv.org/pdf/2103.13944.pdf)

70. [Arxiv 21] **Learnt Sparsification for Interpretable Graph Neural Networks** [[paper]](https://arxiv.org/pdf/2106.12920.pdf)

72. [ICML workshop 21] **GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks** [[paper]](https://arxiv.org/pdf/2107.11889.pdf)

74. [ICML workshop 21] **Reliable Graph Neural Network Explanations Through Adversarial Training** [[paper]](https://arxiv.org/pdf/2106.13427.pdf)

75. [ICML workshop 21] **Reimagining GNN Explanations with ideas from Tabular Data** [[paper]](https://arxiv.org/pdf/2106.12665.pdf)

76. [ICML workshop 21] **Towards Automated Evaluation of Explanations in Graph Neural Networks** [[paper]](https://arxiv.org/pdf/2106.11864.pdf)

79. [ICDM 2021] **GNES: Learning to Explain Graph Neural Networks** [[paper]](https://cs.emory.edu/~lzhao41/materials/papers/GNES.pdf)

80. [ICDM 2021] **GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs** [[paper]](https://arxiv.org/abs/2110.05598)

82. [ICDM 2021] **Multi-objective Explanations of GNN Predictions** [[paper]](https://arxiv.org/abs/2111.14651)

83. [CIKM 2021] **Towards Self-Explainable Graph Neural Network** [[paper]](https://arxiv.org/abs/2108.12055)

84. [ECML PKDD 2021] **GraphSVX: Shapley Value Explanations for Graph Neural Networks** [[paper]](https://arxiv.org/abs/2104.10482)

85. [WiseML 2021] **Explainability-based Backdoor Attacks Against Graph Neural Networks** [[paper]](https://dl.acm.org/doi/pdf/10.1145/3468218.3469046)

86. [IJCNN 21] **MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks**  [[paper]](https://arxiv.org/pdf/2104.08060.pdf)

87. [ICCSA 2021] **Understanding Drug Abuse Social Network Using Weighted Graph Neural Networks Explainer** [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-030-86970-0_5)

88. [NeSy 21] **A New Concept for Explaining Graph Neural Networks** [[paper]](http://ceur-ws.org/Vol-2986/paper1.pdf)

89. [Information Fusion 21] **Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI** [[paper]](https://www.sciencedirect.com/science/article/pii/S1566253521000142?via%3Dihub)

90. [Patterns 21] **hcga: Highly Comparative Graph Analysis for network phenotyping** [[paper]](https://www.biorxiv.org/content/10.1101/2020.09.25.312926v2)

### Year 2020 and Before

1. [NeurIPS 2020] **Parameterized Explainer for Graph Neural Network**.[[paper]](https://arxiv.org/abs/2011.04573)

2. [NeurIPS 2020] **PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks** [[paper]](https://arxiv.org/pdf/2010.05788.pdf)

3. [KDD 2020] **XGNN: Towards Model-Level Explanations of Graph Neural Networks** [[paper]](https://dl.acm.org/doi/10.1145/3394486.3403085)

4. [ACL 2020]**GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media**. [paper](https://arxiv.org/pdf/2004.11648.pdf)

5. [Arxiv 2020] **Graph Neural Networks Including Sparse Interpretability** [[paper]](https://arxiv.org/abs/2007.00119)

6. [NeurIPS Workshop 20] **Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks** [[paper]](https://arxiv.org/abs/2012.03723)

7. [ICML workstop 2020] **Contrastive Graph Neural Network Explanation** [[paper]](https://arxiv.org/pdf/2010.13663.pdf)

8. [ICML workstop 2020] **Towards Explainable Graph Representations in Digital Pathology** [[paper]](https://arxiv.org/pdf/2007.00311.pdf)

9. [NeurIPS workshop 2020] **Explaining Deep Graph Networks with Molecular Counterfactuals** [[paper]](https://arxiv.org/pdf/2011.05134.pdf)

10. [DataMod 2020] **Exploring Graph-Based Neural Networks for Automatic Brain Tumor Segmentation"** [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-030-70650-0_2)

12. [OpenReview 20] **A Framework For Differentiable Discovery Of Graph Algorithms** [[paper]](https://openreview.net/pdf?id=ueiBFzt7CiK)

13. [OpenReview 20] **Causal Screening to Interpret Graph Neural Networks** [[paper]](https://openreview.net/pdf?id=nzKv5vxZfge)

14. [Arxiv 20] **Understanding Graph Neural Networks from Graph Signal Denoising Perspectives** [[paper]](https://arxiv.org/pdf/2006.04386.pdf)

15. [Arxiv 20] **Understanding the Message Passing in Graph Neural Networks via Power Iteration** [[paper]](https://arxiv.org/pdf/2006.00144.pdf)

16. [Arxiv 20] **xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links**  [[paper]](https://arxiv.org/pdf/2006.00144.pdf)

17. [IJCNN 20] **GCN-LRP explanation: exploring latent attention of graph convolutional networks**] [[paper]](https://ieeexplore.ieee.org/abstract/document/9207639)

18. [CD-MAKE 20] **Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification** [[paper]](https://arxiv.org/abs/2002.00514) 

19. [ICDM 19] **Scalable Explanation of Inferences on Large Graphs**[[paper]](https://arxiv.org/abs/1908.06482)