[1] S. Ali, T. Abuhmed, S. El-Sappagh, K. Muhammad, J. M. Alonso-Moral, R. Confalonieri, R. Guidotti, J. Del Ser, N. Diaz-Rodriguez, and F. Herrera. Explainable artificial intelligence (xai): What we know and what is left to attain trustworthy artificial intelligence. Information Fusion, page 101805, 2023.
[2] M. Bajaj, L. Chu, Z. Y. Xue, J. Pei, L. Wang, P. C.-H. Lam, and Y. Zhang. Robust counterfactual explanations on graph neural networks. Advances in Neural Information Processing Systems, 34:5644-5655, 2021.
[3] F. Baldassarre and H. Azizpour. Explainability techniques for graph convolutional networks. In International Conference on Machine Learning (ICML) Workshops, 2019 Workshop on Learning and Reasoning with Graph-Structured Representations, 2019.
[4] C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton. A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1):1-43, 2012.
[5] I. Covert, S. M. Lundberg, and S.-I. Lee. Understanding global feature contributions with additive importance measures. Advances in Neural Information Processing Systems, 33:17212-17223, 2020.
[6] I. C. Covert, S. Lundberg, and S.-I. Lee. Explaining by removing: A unified framework for model explanation. The Journal of Machine Learning Research, 22(1):9477-9566, 2021.
[7] A. K. Debnath, R. L. Lopez de Compadre, G. Debnath, A. J. Shusterman, and C. Hansch. Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity. Journal of Medicinal Chemistry, 34(2):786-797, 1991.
[8] A. Duval and F. D. Malliaros. Graphsvx: Shapley value explanations for graph neural networks. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part II 21, pages 302–318. Springer, 2021.
[9] Q. Feng, N. Liu, F. Yang, R. Tang, M. Du, and X. Hu. Degree: Decomposition based explanation for graph neural networks. In International Conference on Learning Representations, 2022.
[10] M. Fey and J. E. Lenssen. Fast graph representation learning with pytorch geometric. arXiv preprint arXiv:1903.02428, 2019.
[11] T. Funke, M. Khosla, M. Rathee, and A. Anand. Zorro: Valid, sparse, and stable explanations in graph neural networks. IEEE Transactions on Knowledge and Data Engineering, 2022.
[12] Q. Huang, M. Yamada, Y. Tian, D. Singh, and Y. Chang. Graphlime: Local interpretable model explanations for graph neural networks. IEEE Transactions on Knowledge and Data Engineering, 2022.
[13] R. Kleinberg, A. Slivkins, and E. Upfal. Multi-armed bandits in metric spaces. In Proceedings of the fortieth annual ACM symposium on Theory of computing, pages 681–690, 2008.
[14] I. E. Kumar, S. Venkatasubramanian, C. Scheidegger, and S. Friedler. Problems with shapley-value-based explanations as feature importance measures. In International Conference on Machine Learning, pages 5491-5500. PMLR, 2020.
[15] W. Lin, H. Lan, and B. Li. Generative causal explanations for graph neural networks. In International Conference on Machine Learning, pages 6666-6679. PMLR, 2021.