Abstract
The opaque reasoning of Graph Neural Networks induces a lack of human trust. Existing graph network explainers attempt to address this issue by providing post-hoc explanations, however, they fail to make the model itself more interpretable. To fill this gap, we introduce the Concept Distillation Module, the first differentiable concept-distillation approach for graph networks. The proposed approach is a layer that can be plugged into any graph network to make it explainable by design, by first distilling graph concepts from the latent space and then using these to solve the task. Our results demonstrate that this approach allows graph networks to: (i) attain model accuracy comparable with their equivalent vanilla versions, (ii) distill meaningful concepts achieving 4.8% higher concept completeness and 36.5% lower purity scores on average, (iii) provide high-quality concept-based logic explanations for their prediction, and (iv) support effective interventions at test time: these can increase human trust as well as improve model performance.
L.C. Magister and P. Barbiero—Equal Contribution.
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References
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Alvarez Melis, D., Jaakkola, T.: Towards robust interpretability with self-explaining neural networks. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Azzolin, S., Longa, A., Barbiero, P., Lio, P., Passerini, A.: Global explainability of GNNs via logic combination of learned concepts. In: The First Learning on Graphs Conference (2022)
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Barbiero, P., Ciravegna, G., Giannini, F., Lió, P., Gori, M., Melacci, S.: Entropy-based logic explanations of neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 6046–6054 (2022)
Battaglia, P., Pascanu, R., Lai, M., Jimenez Rezende, D., et al.: Interaction networks for learning about objects, relations and physics. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth (1984)
Chen, Z., Bei, Y., Rudin, C.: Concept whitening for interpretable image recognition. Nat. Mach. Intell. 2(12), 772–782 (2020)
Ciravegna, G., Barbiero, P., Giannini, F., Gori, M., Lió, P., Maggini, M., Melacci, S.: Logic explained networks. Artif. Intell. 314, 103822 (2023)
Davies, A., et al.: Advancing mathematics by guiding human intuition with AI. Nature 600(7887), 70–74 (2021)
Durán, J.M., Jongsma, K.R.: Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. J. Med. Ethics 47(5), 329–335 (2021)
EUGDPR: GDPR. General data protection regulation (2017)
Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch Geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)
Forgy, E.W.: Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 21, 768–769 (1965)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (2012)
Ghorbani, A., Abid, A., Zou, J.: Interpretation of neural networks is fragile. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3681–3688 (2019)
Ghorbani, A., Wexler, J., Zou, J.Y., Kim, B.: Towards automatic concept-based explanations. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263–1272. PMLR (2017)
Kazhdan, D., Dimanov, B., Jamnik, M., Liò, P.: MEME: generating RNN model explanations via model extraction. arXiv preprint arXiv:2012.06954 (2020)
Kazhdan, D., Dimanov, B., Jamnik, M., Liò, P., Weller, A.: Now you see me (CME): concept-based model extraction. arXiv preprint arXiv:2010.13233 (2020)
Khasahmadi, A.H., Hassani, K., Moradi, P., Lee, L., Morris, Q.: Memory-based graph networks. In: International Conference on Learning Representations (2020)
Kim, B., et al.: Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). In: International Conference on Machine Learning, pp. 2668–2677. PMLR (2018)
Kindermans, P.-J., et al.: The (un)reliability of saliency methods. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Müller, K.-R. (eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700, pp. 267–280. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28954-6_14
Koh, P.W., Nguyen, T., Tang, Y.S., Mussmann, S., Pierson, E., Kim, B., Liang, P.: Concept bottleneck models. In: International Conference on Machine Learning, pp. 5338–5348. PMLR (2020)
Lo Piano, S.: Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward. Humanit. Soc. Sci. Commun. 7(1), 1–7 (2020)
Luo, D., Cheng, W., Xu, D., Yu, W., Zong, B., Chen, H., Zhang, X.: Parameterized explainer for graph neural network. In: Advances in Neural Information Processing Systems, vol. 33, pp. 19620–19631 (2020)
Magister, L.C., Kazhdan, D., Singh, V., Liò, P.: GCExplainer: human-in-the-loop concept-based explanations for graph neural networks. arXiv preprint arXiv:2107.11889 (2021)
Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956)
Morris, C., Kriege, N.M., Bause, F., Kersting, K., Mutzel, P., Neumann, M.: TUDataset: a collection of benchmark datasets for learning with graphs. arXiv preprint arXiv:2007.08663 (2020)
Pal, A., Eksombatchai, C., Zhou, Y., Zhao, B., Rosenberg, C., Leskovec, J.: PinnerSage: multi-modal user embedding framework for recommendations at pinterest. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2311–2320 (2020)
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206–215 (2019)
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61–80 (2008)
Shen, M.W.: Trust in AI: interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient. arXiv preprint arXiv:2202.05302 (2022)
Stokes, J.M., et al.: A deep learning approach to antibiotic discovery. Cell 180(4), 688–702 (2020)
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)
Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv. JL Tech. 31, 841 (2017)
Wang, M.Y.: Deep graph library: Towards efficient and scalable deep learning on graphs. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)
Yeh, C.K., Kim, B., Arik, S., Li, C.L., Pfister, T., Ravikumar, P.: On completeness-aware concept-based explanations in deep neural networks. In: Advances in Neural Information Processing Systems, vol. 33, pp. 20554–20565 (2020)
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)
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018)
Zhang, Z., Liu, Q., Wang, H., Lu, C., Lee, C.: ProtGNN: towards self-explaining graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 9127–9135 (2022)
Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: a survey on methods and metrics. Electronics 10(5), 593 (2021)
Acknowledgements
This work was partially supported by projects FAIR (PE0000013) and SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU and by ERC Starting Grant No. 802554 (SPECGEO) and PRIN 2020 project n.2020TA3K9N “LEGO.AI”. Supported also by the ERC Advanced Grant 788893 AMDROMA, EC H2020RIA project “SoBigData++” (871042), PNRR MUR project IR0000013-SoBigData.it.
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Magister, L.C. et al. (2023). Concept Distillation in Graph Neural Networks. In: Longo, L. (eds) Explainable Artificial Intelligence. xAI 2023. Communications in Computer and Information Science, vol 1903. Springer, Cham. https://doi.org/10.1007/978-3-031-44070-0_12
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