Abstract
Most post-hoc explainability methods for graph classification analyze the model’s internal representations rather than explicitly capturing its reasoning process. These approaches typically rely on perturbations, gradients, or optimization techniques to infer important features but do not approximate the decision-making function itself. In this paper, we propose a novel approach that directly models the GNN’s decision function using a Transparent Explainable Logic Layer (TELL). This logic-based approximation enables both instance-level and global-level explanations, offering insights into how node embeddings contribute to predictions. Unlike conventional methods, our approach derives explanations that are structurally aligned with the model’s decision process rather than being externally imposed. Through experiments on synthetic and real-world graph classification tasks, we show that our method produces faithful, sparse, and stable explanations, outperforming existing techniques.
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Notes
- 1.
Public GitHub repository: https://github.com/spideralessio/logix.
References
Agarwal, C., Queen, O., Lakkaraju, H., Zitnik, M.: Evaluating explainability for graph neural networks. Sci. Data 10(1) (2023)
Armgaan, B., Dalmia, M., Medya, S., Ranu, S.: Graphtrail: translating GNN predictions into human-interpretable logical rules. In: NeurIPS (2024)
Azzolin, S., Longa, A., Barbiero, P., Lio, P., Passerini, A.: Global explainability of gnns via logic combination of learned concepts. In: ICLR (2023)
Bongini, P., Bianchini, M., Scarselli, F.: Molecular generative graph neural networks for drug discovery. Neurocomputing 450, 242–252 (2021). Aug
Ciravegna, G., et al.: Logic explained networks. Artif. Intell. 314, 103822 (2023)
Cordella, L.P., Foggia, P., Sansone, C., Vento, M., et al.: An improved algorithm for matching large graphs. In: IAPR-TC15 (2001)
Dai, E., Wang, S.: Towards self-explainable graph neural network. In: International Conference on Information & Knowledge Management, CIKM, pp. 302–311 (2021)
Darwiche, A.: Logic for explainable ai. In: 2023 38th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), pp. 1–11. IEEE (2023)
Debnath, A.K., Lopez de Compadre, R.L., Debnath, G., Shusterman, A.J., Hansch, C.: Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. J. Med. Chem. 34(2), 786–797 (1991)
Dobson, P.D., Doig, A.J.: Distinguishing enzyme structures from non-enzymes without alignments. J. Mol. Biol. 330(4), 771–783 (2003)
Duval, A., Malliaros, F.D.: Graphsvx: shapley value explanations for graph neural networks. In: ECML PKDD, pp. 302–318 (2021)
Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., Yin, D.: Graph neural networks for social recommendation. In: WWW 2019. ACM Press (2019)
Jiang, D., et al.: Could graph neural networks learn better molecular representation for drug discovery? J. Cheminform. 13(1) (2021)
Kazius, J., McGuire, R., Bursi, R.: Derivation and validation of toxicophores for mutagenicity prediction. J. Med. Chem. 48(1), 312–320 (2004)
Lipton, Z.C.: The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31–57 (2018)
Luo, D., et al.: Parameterized explainer for graph neural network. NeurIPS 33, 19620–19631 (2020)
Martins, I.F., Teixeira, A.L., Pinheiro, L., Falcao, A.O.: A bayesian approach to in silico blood-brain barrier penetration modeling. J. Chem. Infor. Model. 52(6), 1686–1697 (jun 2012)
Ragno, A., Capobianco, R.: Impo: interpretable memory-based prototypical pooling. In: WSDM, pp. 625–632 (2025)
Ragno, A., La Rosa, B., Capobianco, R.: Prototype-based interpretable graph neural networks. IEEE Trans. Artifi. Intell., 1–11 (2022)
Ragno, A., Plantevit, M., Robardet, C., Capobianco, R.: Transparent explainable logic layers. In: ECAI. vol. 392, pp. 914–921 (2024)
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)
Schwarzenberg, R., Hübner, M., Harbecke, D., Alt, C., Hennig, L.: Layerwise relevance visualization in convolutional text graph classifiers. In: Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs, pp. 58–62 (2019)
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: International Conference on Computer Vision, ICCV, pp. 618–626 (2017)
Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: ICML, pp. 3319–3328. PMLR (2017)
Wale, N., Watson, I.A., Karypis, G.: Comparison of descriptor spaces for chemical compound retrieval and classification. KAIS 14(3), 347–375 (2007)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: ICLR (2019)
Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: Gnnexplainer: generating explanations for graph neural networks. NeurIPS 32 (2019)
Yuan, H., Yu, H., Wang, J., Li, K., Ji, S.: On explainability of graph neural networks via subgraph explorations. In: ICML, pp. 12241–12252. PMLR (2021)
Zhang, S., Liu, Y., Shah, N., Sun, Y.: Gstarx: explaining graph neural networks with structure-aware cooperative games. Adv. Neural Inform. Process. Syst. NeurIPS 35, 19810–19823 (2022)
Zhang, Z., Liu, Q., Wang, H., Lu, C., Lee, C.: ProtGNN: towards self-explaining graph neural networks. AAAI Artifi. Intell. 36(8), 9127–9135 (2022)
Acknowledgments
This work was supported by French state aid managed by the National Research Agency under the France 2030 program, with the references “WAIT4 ANR-22-PEAE-0008” and “PANDORA ANR-24-CE23-0950”.
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Ragno, A., Plantevit, M., Robardet, C. (2026). Faithful Explanations for Graph Classification Using Logic. In: Ribeiro, R.P., et al. Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2025. Lecture Notes in Computer Science(), vol 16016. Springer, Cham. https://doi.org/10.1007/978-3-032-06078-5_7
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