[1] Murat Seckin Ayhan and Philipp Berens. Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks. In Medical Imaging with Deep Learning, 2018.
[2] Filippo Maria Bianchi, Daniele Grattarola, and Cesare Alippi. Spectral clustering with graph neural networks for graph pooling. In Procs. of ICML, 2020.
[3] Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. Graph neural networks for social recommendation. In Procs. of WWW, 2019.
[4] Yujie Fan, Mingxuan Ju, Chuxu Zhang, and Yanfang Ye. Heterogeneous temporal graph neural network. In Procs. of SDM, 2022.
[5] Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang. Graph random neural networks for semi-supervised learning on graphs. Procs. of NeurIPS, 2020.
[6] Nikhil Goyal, Harsh Vardhan Jain, and Sayan Ranu. Graphgen: A scalable approach to domain-agnostic labeled graph generation. In Procs. of WWW, 2020.
[7] Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, and Nitesh V Chawla. Few-shot graph learning for molecular property prediction. In Procs. of WWW, 2021.
[8] Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V Chawla, Neil Shah, and Tong Zhao. Linkless link prediction via relational distillation. In Procs. of ICML, 2023.
[9] William L Hamilton, Rex Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Procs. of NeurIPS, 2017.
[10] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Procs. of NeurIPS, 2020.
[11] Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. Strategies for pre-training graph neural networks. In Procs. of ICLR, 2019.
[12] Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. In Procs. of NeurIPS, 2020.
[13] Weihua Hu, Kaidi Cao, Kexin Huang, Edward W Huang, Karthik Subbian, and Jure Leskovec. Tuneup: A training strategy for improving generalization of graph neural networks. arXiv, 2022.
[14] Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun. Gpt-gnn: Generative pre-training of graph neural networks. In Procs. of KDD, 2020.
[15] Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, and Neil Shah. Empowering graph representation learning with test-time graph transformation. In Procs. of ICLR, 2023.
[16] Jaehyeong Jo, Seul Lee, and Sung Ju Hwang. Score-based generative modeling of graphs via the system of stochastic differential equations. In Procs. of ICML, 2022.
[17] Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Yanfang Ye, and Liang Zhao. Adaptive kernel graph neural network. In Procs. of AAAI, 2022.
[18] Mingxuan Ju, Wenhao Yu, Tong Zhao, Chuxu Zhang, and Yanfang Ye. Grape: Knowledge graph enhanced passage reader for open-domain question answering. In Findings of EMNLP, 2022.
[19] Mingxuan Ju, Yujie Fan, Chuxu Zhang, and Yanfang Ye. Let graph be the go board: gradient-free node injection attack for graph neural networks via reinforcement learning. In Procs. of AAAI, 2023.