[77] N. Yang, K. Zeng, Q. Wu, X. Jia, and J. Yan. Learning substructure invariance for out-of-distribution molecular representations. In Advances in Neural Information Processing Systems, 2022. (Cited on pages 1, 2, 4, 5, 9, 21, 22, 32 and 33)
[78] G. Yehudai, E. Fetaya, E. Meirom, G. Chechik, and H. Maron. From local structures to size generalization in graph neural networks. In International Conference on Machine Learning, pages 11975-11986, 2021. (Cited on page 21)
[79] R. Yeung. Information Theory and Network Coding. Springer New York, NY, 01 2008. ISBN 978-0-387-79233-0. (Cited on page 25)
[80] Y. You, T. Chen, Z. Wang, and Y. Shen. Graph domain adaptation via theory-grounded spectral regularization. In The Eleventh International Conference on Learning Representations, 2023. (Cited on page 21)
[81] J. Yu, T. Xu, Y. Rong, Y. Bian, J. Huang, and R. He. Graph information bottleneck for subgraph recognition. In International Conference on Learning Representations, 2021. (Cited on pages 4, 9 and 21)
[82] J. Yu, T. Xu, Y. Rong, Y. Bian, J. Huang, and R. He. Recognizing predictive substructures with subgraph information bottleneck. IEEE transactions on pattern analysis and machine intelligence, 2021. (Cited on pages 4 and 21)
[83] J. Yu, J. Liang, and R. He. Mind the label shift of augmentation-based graph OOD generalization. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. (Cited on pages 1, 4, 5 and 21)
[84] H. Yuan, H. Yu, S. Gui, and S. Ji. Explainability in graph neural networks: A taxonomic survey. arXiv preprint, arXiv:2012.15445, 2020. (Cited on pages 9 and 30)
[85] M. Zhang, N. S. Sohoni, H. R. Zhang, C. Finn, and C. Ré. Correct-n-contrast: A contrastive approach for improving robustness to spurious correlations. arXiv preprint, arXiv:2203.01517, 2022. (Cited on pages 4, 7, 8, 21 and 27)
[86] X. Zhang, L. Wang, J. Helwig, Y. Luo, C. Fu, Y. Xie, M. Liu, Y. Lin, Z. Xu, K. Yan, K. Adams, M. Weiler, X. Li, T. Fu, Y. Wang, H. Yu, Y. Xie, X. Fu, A. Strasser, S. Xu, Y. Liu, Y. Du, A. Saxton, H. Ling, H. Lawrence, H. Stärk, S. Gui, C. Edwards, N. Gao, A. Ladera, T. Wu, E. F. Hofgard, A. M. Tehrani, R. Wang, A. Daigavane, M. Bohde, J. Kurtin, Q. Huang, T. Phung, M. Xu, C. K. Joshi, S. V. Mathis, K. Azizzadenesheli, A. Fang, A. Aspuru-Guzik, E. Bekkers, M. M. Bronstein, M. Zitnik, A. Anandkumar, S. Ermon, P. Lio, R. Yu, S. Gunnemann, J. Leskovec, H. Ji, J. Sun, R. Barzilay, T. S. Jaakkola, C. W. Coley, X. Qian, X. Qian, T. E. Smidt, and S. Ji. Artificial intelligence for science in quantum, atomistic, and continuum systems. arXiv preprint, arXiv:2307.08423, 2023. (Cited on page 1)
[87] Y. Zhang, M. Gong, T. Liu, G. Niu, X. Tian, B. Han, B. Schölkopf, and K. Zhang. Adversarial robustness through the lens of causality. In International Conference on Learning Representations, 2022. (Cited on page 1)
[88] J. Zhou, B. Bevilacqua, and B. Ribeiro. An ood multi-task perspective for link prediction with new relation types and nodes. arXiv preprint, arXiv:2307.06046, 2023. (Cited on page 21)
[89] Y. Zhou, G. Kutyniok, and B. Ribeiro. OOD link prediction generalization capabilities of message-passing GNNs in larger test graphs. In Advances in Neural Information Processing Systems, 2022. (Cited on page 21)
[90] Y. Zhou, G. Kutyniok, and B. Ribeiro. OOD link prediction generalization capabilities of message-passing GNNs in larger test graphs. In Advances in Neural Information Processing Systems, 2022. (Cited on page 21)
[91] Z. Zhou, J. Yao, J. Liu, X. Guo, Q. Yao, L. He, L. Wang, B. Zheng, and B. Han. Combating bilateral edge noise for robust link prediction. In Advances in Neural Information Processing Systems, 2023. (Cited on page 21)