[2] Tianyi Zhao, Yang Hu, Linda R. Valsdottir, Tianyi Zang, and Jiajie Peng. Identifying drug-target interactions based on graph convolutional network and deep neural network. *Briefings Bioinform.*, 22(2):2141-2150, 2021.
[3] Zhiwei Guo and Heng Wang. A deep graph neural network-based mechanism for social recommendations. IEEE Trans. Ind. Informatics, 17(4):2776-2783, 2021.
[4] Kun Wang, Yuxuan Liang, Pengkun Wang, Xu Wang, Pengfei Gu, Junfeng Fang, and Yang Wang. Searching lottery tickets in graph neural networks: A dual perspective. In The Eleventh International Conference on Learning Representations, 2023.
[5] Minh N. Vu and My T. Thai. Pgm-explainer: Probabilistic graphical model explanations for graph neural networks. In NeurIPS, 2020.
[6] Wanyu Lin, Hao Lan, and Baochun Li. Generative causal explanations for graph neural networks. In ICML, volume 139, pages 6666-6679, 2021.
[7] Michael Sejr Schlichtkrull, Nicola De Cao, and Ivan Titov. Interpreting graph neural networks for NLP with differentiable edge masking. In ICLR, 2021.
[8] Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. Explainability in graph neural networks: A taxonomic survey. CoRR, abs/2012.15445, 2020.
[9] Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji. On explainability of graph neural networks via subgraph explorations. In ICML, volume 139, pages 12241-12252. PMLR, 2021.
[10] Federico Baldassarre and Hossein Azizpour. Explainability techniques for graph convolutional networks. CoRR, abs/1905.13686, 2019.
[11] Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. Gnnexplainer: Generating explanations for graph neural networks. In NeurIPS, pages 9240–9251, 2019.
[12] Leo Breiman. Random forests. Mach. Learn., 45:5-32, 2001.
[13] Giles Hooker and Lucas Mentch. Please stop permuting features: An explanation and alternatives. CoRR, abs/1905.03151, 2019.
[14] Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. Out-of-distribution generalization on graphs: A survey. CoRR, abs/2202.07987, 2022.
[15] Siqi Miao, Mia Liu, and Pan Li. Interpretable and generalizable graph learning via stochastic attention mechanism. In ICML, pages 15524-15543, 2022.
[16] Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He, and Tat-Seng Chua. Discovering invariant rationales for graph neural networks. CoRR, abs/2201.12872, 2022.
[17] Ying-Xin Wu, Xiang Wang, An Zhang, Xia Hu, Fuli Feng, Xiangnan He, and Tat-Seng Chua. Deconfounding to explanation evaluation in graph neural networks. CoRR, abs/2201.08802, 2022.
[18] Christopher Frye, Damien de Mijolla, Tom Begley, Laurence Cowton, Megan Stanley, and Ilya Feige. Shapley explainability on the data manifold. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, 2021.
[19] Thomas N. Kipf and Max Welling. Variational graph auto-encoders. CoRR, abs/1611.07308, 2016.
[20] Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, 2014.
[21] Thomas Fel, Melanie Ducoffe, David Vigouroux, Rémi Cadène, Mikael Capelle, Claire Nicodeme, and Thomas Serre. Don't lie to me! robust and efficient explainability with verified perturbation analysis. CoRR, abs/2202.07728, 2022.