[25] J. McAuley, C. Targett, Q. Shi, and A. Van Den Hengel. Image-based recommendations on styles and substitutes. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pages 43-52, 2015.
[26] A. K. McCallum, K. Nigam, J. Rennie, and K. Seymour. Automating the construction of internet portals with machine learning. Information Retrieval, 3(2):127-163, 2000.
[27] J. Mukhoti, A. Kirsch, J. van Amersfoort, P. H. Torr, and Y. Gal. Deep deterministic uncertainty: A simple baseline. arXiv e-prints, pages arXiv-2102, 2021.
[28] G. Namata, B. London, L. Getoor, B. Huang, and U. Edu. Query-driven active surveying for collective classification. In 10th International Workshop on Mining and Learning with Graphs, volume 8, page 1, 2012.
[29] L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab, 1999.
[30] Y. Rong, W. Huang, T. Xu, and J. Huang. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In International Conference on Learning Representations, 2020.
[31] P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad. Collective classification in network data. AI magazine, 29(3):93-93, 2008.
[32] O. Shchur, M. Mumme, A. Bojchevski, and S. Gunnemann. Pitfalls of graph neural network evaluation. Relational Representation Learning Workshop, NeurIPS 2018, 2018.
[33] M. Stadler, B. Charpentier, S. Geisler, D. Zügner, and S. Gunnemann. Graph posterior network: Bayesian predictive uncertainty for node classification. Advances in Neural Information Processing Systems, 34:18033-18048, 2021.
[34] B. Sun and K. Saenko. Deep coral: Correlation alignment for deep domain adaptation. In Computer Vision-ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14, pages 443-450. Springer, 2016.
[35] Y. Sun, S. Wang, X. Tang, T.-Y. Hsieh, and V. Honavar. Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach. In Proceedings of the Web Conference 2020, pages 673-683, 2020.
[36] B. Wang, J. Lu, Z. Yan, H. Luo, T. Li, Y. Zheng, and G. Zhang. Deep uncertainty quantification: A machine learning approach for weather forecasting. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2087-2095, 2019.
[37] Y. Wang, W. Wang, Y. Liang, Y. Cai, and B. Hooi. Mixup for node and graph classification. In Proceedings of the Web Conference 2021, pages 3663-3674, 2021.
[38] Q. Wu, H. Zhang, J. Yan, and D. Wipf. Handling Distribution Shifts on Graphs: An Invariance Perspective. In International Conference on Learning Representations, 2022.
[39] H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz. mixup: Beyond empirical risk minimization. In International Conference on Learning Representations, 2018.
[40] X. Zhang and M. Zitnik. GNNGuard: Defending Graph Neural Networks Against Adversarial Attacks. Advances in Neural Information Processing Systems, 33:9263–9275, 2020.
[41] X. Zhao, F. Chen, S. Hu, and J.-H. Cho. Uncertainty Aware Semi-Supervised Learning on Graph Data. Advances in Neural Information Processing Systems, 33:12827-12836, 2020.
[42] D. Zügner and S. Gunnemann. Adversarial attacks on graph neural networks via meta learning. In International Conference on Learning Representations, 2019.