[46] Qi Zhu, Natalia Ponomareva, Jiawei Han, and Bryan Perozzi. Shift-robust gnns: Overcoming the limitations of localized graph training data. NeurIPS, 34:27965–27977, 2021.
[47] Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, and Neil Shah. Empowering graph representation learning with test-time graph transformation. In ICLR, 2023.
[48] Junchi Yu, Jian Liang, and Ran He. Mind the label shift of augmentation-based graph ood generalization. In CVPR, 2023.
[49] Zenan Li, Qitian Wu, Fan Nie, and Junchi Yan. Graphde: A generative framework for debiased learning and out-of-distribution detection on graphs. In NeurIPS, 2022.
[50] Qitian Wu, Yiting Chen, Chenxiao Yang, and Junchi Yan. Energy-based out-of-distribution detection for graph neural networks. In ICLR, 2023.
[51] Yuxin Guo, Cheng Yang, Yuluo Chen, Jixi Liu, Chuan Shi, and Junping Du. A data-centric framework to endow graph neural networks with out-of-distribution detection ability. In KDD, 2023.
[52] Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert Müller. Covariate shift adaptation by importance weighted cross validation. Journal of Machine Learning Research, 8(5), 2007.
[53] Zheyan Shen, Peng Cui, Kun Kuang, Bo Li, and Peixuan Chen. Causally regularized learning with agnostic data selection bias. In ACM MM, pages 411-419, 2018.
[54] Renzhe Xu, Xingxuan Zhang, Zheyan Shen, Tong Zhang, and Peng Cui. A theoretical analysis on independence-driven importance weighting for covariate-shift generalization. In ICML, pages 24803-24829. PMLR, 2022.
[55] Tongtong Fang, Nan Lu, Gang Niu, and Masashi Sugiyama. Rethinking importance weighting for deep learning under distribution shift. NeurIPS, 33:11996-12007, 2020.
[56] Yue He, Xinwei Shen, Renzhe Xu, Tong Zhang, Yong Jiang, Wenchao Zou, and Peng Cui. Covariate-shift generalization via random sample weighting. AAAI, 2023.
[57] Emanuel Parzen. On estimation of a probability density function and mode. The annals of mathematical statistics, 33(3):1065-1076, 1962.
[58] Kurt Binder, Dieter Heermann, Lyle Roelofs, A John Mallinckrodt, and Susan McKay. Monte carlo simulation in statistical physics. Computers in Physics, 7(2):156-157, 1993.
[59] Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein. Geometric deep learning on graphs and manifolds using mixture model cnns. In CVPR, pages 5115-5124, 2017.
[60] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? In ICLR, 2019.
[61] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In ICLR, 2017.
[62] Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. Simple and deep graph convolutional networks. In ICML, pages 1725-1735. PMLR, 2020.
[63] Kiran K Thekumparampil, Chong Wang, Sewoong Oh, and Li-Jia Li. Attention-based graph neural network for semi-supervised learning. arXiv preprint arXiv:1803.03735, 2018.
[64] Christopher Morris, Nils M Krieger, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. Tudataset: A collection of benchmark datasets for learning with graphs. In ICMLW, 2020.
[65] Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, and Zheyan Shen. Heterogeneous risk minimization. In ICML, pages 6804-6814. PMLR, 2021.