Skip to main content
SpringerLink
Log in

Random Mask Perturbation Based Explainable Method of Graph Neural Networks

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14647))

Included in the following conference series:

  • 182 Accesses

Abstract

Graph Neural Networks (GNNs) have garnered considerable attention due to their potential applications across multiple domains. However, enhancing their interpretability is a significant challenge for the crucial application. This paper proposes an innovative node perturbation-based method to explicate GNNs and unveil their decision-making processes. Categorized as a black-box method, it generates explanations of node importance solely through the input-output analysis of the model, obviating the necessity for internal access. The method employs fidelity as a metric for calculating the significance of perturbation masks and utilizes a sparsity threshold to filter the computation results. Furthermore, recognizing the impact of different node combinations on model prediction outcomes, we treat the mask as a random variable. By randomly sampling various masks, we compute perturbed node importance, facilitating the generation of user-friendly explanations. Comparative experiments and ablation studies conducted on both real and synthetic datasets substantiate the efficacy of our approach in interpreting GNNs. Additionally, through a case study, we visually demonstrate the method’s compelling interpretative evidence regarding model prediction outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)

    Article  Google Scholar 

  2. Baldassarre, F., Azizpour, H.:. Explainability techniques for graph convolutional networks. arXiv preprint arXiv:1905.13686 (2019)

  3. Dai, E., et al.: A comprehensive survey on trustworthy graph neural networks: privacy, robustness, fairness, and explainability. arXiv preprint arXiv:2204.08570 (2022)

  4. Feng, Q., Liu, N., Yang, F., Tang, R., Du, M., Hu, X.: Degree: decomposition based explanation for graph neural networks. arXiv preprint arXiv:2305.12895 (2023)

  5. Funke, T., Khosla, M., Rathee, M., Anand, A.: ZORRO: valid, sparse, and stable explanations in graph neural networks. IEEE Trans. Knowl. Data Eng. 35, 8687–8698 (2022)

    Google Scholar 

  6. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) 51(5), 1–42 (2018)

    Article  Google Scholar 

  7. Holdijk, L., Boon, M., Henckens, S., de Jong, L.: [re] parameterized explainer for graph neural network. In: ML Reproducibility Challenge 2020 (2021)

    Google Scholar 

  8. Huang, Q., Yamada, M., Tian, Y., Singh, D., Chang, Y.: GraphLIME: local interpretable model explanations for graph neural networks. IEEE Trans. Knowl. Data Eng. 35, 6968–6972 (2022)

    Google Scholar 

  9. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  10. Lucic, A., Ter Hoeve, M.A., Tolomei, G., De Rijke, M., Silvestri, F.: CF-GNNexplainer: counterfactual explanations for graph neural networks. In: International Conference on Artificial Intelligence and Statistics, pp. 4499–4511. PMLR (2022)

    Google Scholar 

  11. Luo, D., et al.: Parameterized explainer for graph neural network. In: Advance Neural Information Processing System, vol. 33, pp. 19620–19631 (2020)

    Google Scholar 

  12. Petsiuk, V., Das, A., Saenko, K.: Rise: randomized input sampling for explanation of black-box models. arXiv preprint arXiv:1806.07421 (2018)

  13. Pope, P.E., Kolouri, S., Rostami, M., Martin, C.E., Hoffmann, H.: Explainability methods for graph convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10772–10781 (2019)

    Google Scholar 

  14. Schnake, T., et al.: Higher-order explanations of graph neural networks via relevant walks. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 7581–7596 (2021)

    Article  Google Scholar 

  15. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)

    Google Scholar 

  16. Shun, K.T.T., Limanta, E.E., Khan, A.: An evaluation of backpropagation interpretability for graph classification with deep learning. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 561–570. IEEE (2020)

    Google Scholar 

  17. Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)

  18. Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning, pp. 3319–3328. PMLR (2017)

    Google Scholar 

  19. Vu, M., Thai, M.T.: PGM-Explainer: probabilistic graphical model explanations for graph neural networks. In: Advances in Neural Information Processing Systems, vol. 33, pp. 12225–12235 (2020)

    Google Scholar 

  20. Wang, X., Wu, Y., Zhang, A., He, X., Chua, T.S.: Causal screening to interpret graph neural networks (2020)

    Google Scholar 

  21. Xuanyuan, H., Barbiero, P., Georgiev, D., Magister, L.C., Liò, P.: Global concept based interpretability for graph neural networks via neuron analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 10675–10683 (2023)

    Google Scholar 

  22. Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: GNNExplainer: generating explanations for graph neural networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  23. Yuan, H., Tang, J., Hu, X., Ji, S.: XGNN: towards model-level explanations of graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 430–438 (2020)

    Google Scholar 

  24. Yuan, H., Haiyang, Yu., Gui, S., Ji, S.: Explainability in graph neural networks: a taxonomic survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 5782–5799 (2022)

    Google Scholar 

  25. Zhang, S., Liu, Y., Shah, N., Sun, Y.: GStarX: explaining graph neural networks with structure-aware cooperative games. In: Advance Neural Information Processing System, vol. 35, pp. 19810–19823 (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xinyue Yang, Hai Huang & Xingquan Zuo

Authors
  1. Xinyue Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hai Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xingquan Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hai Huang .

Editor information

Editors and Affiliations

  1. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  4. Duke University, Durham, NC, USA

    Jian Pei

  5. National Cheng Kung University, Tainan, Taiwan

    Jen-Wei Huang

  6. Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, X., Huang, H., Zuo, X. (2024). Random Mask Perturbation Based Explainable Method of Graph Neural Networks. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14647. Springer, Singapore. https://doi.org/10.1007/978-981-97-2259-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-2259-4_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2261-7

  • Online ISBN: 978-981-97-2259-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation