[5] Linda Martin, Melissa Hutchens, Conrad Hawkins, and Alaina Radnov. How much do clinical trials cost. Nat Rev Drug Discov, 16(6):381-382, 2017.
[6] Debleena Paul, Gaurav Sanap, Snehal Shenoy, Dnyaneshwar Kalyane, Kiran Kalia, and Rakesh K Tekade. Artificial intelligence in drug discovery and development. Drug discovery today, 26(1):80, 2021.
[7] Duxin Sun, Wei Gao, Hongxiang Hu, and Simon Zhou. Why  $90\%$  of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica B, 2022.
[8] Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. Graph neural networks for social recommendation. In The world wide web conference, pages 417-426, 2019.
[9] Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. Disentangled graph convolutional networks. In International conference on machine learning, pages 4212-4221. PMLR, 2019.
[10] Yiding Yang, Zunlei Feng, Mingli Song, and Xinchao Wang. Factorizable graph convolutional networks. Advances in Neural Information Processing Systems, 33:20286-20296, 2020.
[11] Ansong Li, Zhiyong Cheng, Fan Liu, Zan Gao, Weili Guan, and Yuxin Peng. Disentangled graph neural networks for session-based recommendation. arXiv preprint arXiv:2201.03482, 2022.
[12] Thilini Cooray and Ngai-Man Cheung. Graph-wise common latent factor extraction for unsupervised graph representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6420-6428, 2022.
[13] Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, and Suhang Wang. Decoupled self-supervised learning for non-homophilous graphs. arXiv preprint arXiv:2206.03601, 2022.
[14] Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, and Wenwu Zhu. Disentangled contrastive learning on graphs. Advances in Neural Information Processing Systems, 34:21872-21884, 2021.
[15] Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. Hierarchical graph representation learning with differentiable pooling. Advances in neural information processing systems, 31, 2018.
[16] Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems, 32, 2019.
[17] Xiangxiang Chu, Tianbao Zhou, Bo Zhang, and Jixiang Li. Fair darts: Eliminating unfair advantages in differentiable architecture search. In European conference on computer vision, pages 465-480. Springer, 2020.
[18] Xiangxiang Chu, Bo Zhang, and Ruijun Xu. Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12239-12248, 2021.
[19] Stephen Cha, Taehyeon Kim, Hayeon Lee, and Se-Young Yun. Supernet in neural architecture search: A taxonomic survey. arXiv preprint arXiv:2204.03916, 2022.
[20] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
[21] Petar Velicković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. In International Conference on Learning Representations, 2018.
[22] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018.
[23] Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. Neural architecture search: A survey. The Journal of Machine Learning Research, 20(1):1997-2017, 2019.
[24] Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean. Efficient neural architecture search via parameters sharing. In International conference on machine learning, pages 4095-4104. PMLR, 2018.
[25] Hanxiao Liu, Karen Simonyan, and Yiming Yang. Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055, 2018.