[40] Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard. Digress: Discrete denoising diffusion for graph generation. In Procs. of ICLR, 2022.
[41] Guotai Wang, Wenqi Li, Michael Aertsen, Jan Deprest, Sébastien Ourselin, and Tom Vercauteren. *Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks*. Neurocomputing, 2019.
[42] Qitian Wu, Hengrui Zhang, Junchi Yan, and David Wipf. Handling distribution shifts on graphs: An invariance perspective. In Procs. of ICLR, 2022.
[43] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? In Procs. of ICLR, 2018.
[44] Zhilin Yang, William Cohen, and Ruslan Salakhudinov. Revisiting semi-supervised learning with graph embeddings. In Procs. of ICML, 2016.
[45] Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. Graph convolutional neural networks for web-scale recommender systems. In Procs. of SIGKDD, 2018.
[46] Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec. Graphrn: Generating realistic graphs with deep auto-regressive models. In Procs. of ICML, 2018.
[47] Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph contrastive learning with augmentations. In Procs. of NeurIPS, 2020.
[48] Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. Graph contrastive learning automated. In Procs. of ICML, 2021.
[49] Sukwon Yun, Kibum Kim, Kanghoon Yoon, and Chanyoung Park. Lte4g: Long-tail experts for graph neural networks. In Procs. of CIKM, 2022.
[50] Muhan Zhang and Yixin Chen. Link prediction based on graph neural networks. In Procs. of NeurIPS, 2018.
[51] Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee. Motif-based graph self-supervised learning for molecular property prediction. Procs. of NeurIPS, 2021.
[52] Tong Zhao, Tianwen Jiang, Neil Shah, and Meng Jiang. A synergistic approach for graph anomaly detection with pattern mining and feature learning. IEEE Transactions on Neural Networks and Learning Systems, 2021.
[53] Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, and Meng Jiang. Learning from counterfactual links for link prediction. In Procs. of ICML, 2022.
[54] Tong Zhao, Wei Jin, Yozen Liu, Yingheng Wang, Gang Liu, Stephan Gunnemann, Neil Shah, and Meng Jiang. Graph data augmentation for graph machine learning: A survey. IEEE DEBULL, 2023.
[55] Wenqing Zheng, Edward W Huang, Nikhil Rao, Sumeet Katariya, Zhangyang Wang, and Karthik Subbian. Cold brew: Distilling graph node representations with incomplete or missing neighborhoods. In Procs. of ICLR, 2021.
[56] Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, and Jiawei Han. Transfer learning of graph neural networks with ego-graph information maximization. In Procs. of NeurIPS, 2021.
[57] Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. Deep Graph Contrastive Representation Learning. In ICML Workshop on Graph Representation Learning and Beyond, 2020.
[58] Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, and Shu Wu. A survey on deep graph generation: Methods and applications. In Procs. of LOG, 2022.