[25] Barto Libor and Kozik Marcin. Absorbing Subalgebras, Cyclic Terms, and the Constraint Satisfaction Problem. Logical Methods in Computer Science, Volume 8, Issue 1, 2012.
[26] Donald W Loveland. Automated theorem proving: A logical basis. Elsevier, 2016.
[27] Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. Parameterized explainer for graph neural network. Advances in neural information processing systems, 33:19620-19631, 2020.
[28] Lucie Charlotte Magister, Dmitry Kazhdan, Vikash Singh, and Pietro Lio. Gcexplainer: Human-in-the-loop concept-based explanations for graph neural networks. arXiv preprint arXiv:2107.11889, 2021.
[29] Lucie Charlotte Magister, Pietro Barbiero, Dmitry Kazhdan, Federico Siciliano, Gabriele Ciravegna, Fabrizio Silvestri, Mateja Jamnik, and Pietro Lio. Encoding concepts in graph neural networks. arXiv preprint arXiv:2207.13586, 2022.
[30] Siqi Miao, Mia Liu, and Pan Li. Interpretable and generalizable graph learning via stochastic attention mechanism. In International Conference on Machine Learning, pages 15524-15543. PMLR, 2022.
[31] J. B. Nation. Varieties whose congruences satisfy certain lattice identities. Algebra Universalis, 1974.
[32] Miroslav Olsák. The local loop lemma. Algebra universalis, 2020.
[33] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703, 2019.
[34] Fabian Pedregosa, Gáel Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of machine learning research, 12:2825-2830, 2011.
[35] Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann. Explainability methods for graph convolutional neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10772-10781, 2019.
[36] Marius-Constantin Popescu, Valentina Balas, Liliana Perescu-Popescu, and Nikos Mastorakis. Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems, 8, 07 2009.
[37] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. "why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135-1144, 2016.
[38] Cynthia Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence, 1(5):206-215, 2019.
[39] Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model. IEEE transactions on neural networks, 20(1):61-80, 2008.
[40] Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034, 2013.
[41] Petar Velicković, Rex Ying, Matilde Padovano, Raia Hadsell, and Charles Blundell. Neural execution of graph algorithms. In International Conference on Learning Representations, 2019.
[42] Minh Vu and My T Thai. Pgm-explainer: Probabilistic graphical model explanations for graph neural networks. Advances in neural information processing systems, 33:12225-12235, 2020.
[43] Philip M Whitman. Free lattices. Annals of Mathematics, pages 325-330, 1941.