[18] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. Intriguing properties of neural networks. In *ICLR*, 2014.
[19] Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, and Jayaraman J. Thiagarajan. Analyzing data-centric properties for graph contrastive learning. In NeurIPS, 2022.
[20] Stephen Wright and Benjamin Recht. First-order methods for constrained optimization. In Optimization for Data Analysis. 2022.
[21] H. Kellerer, U. Pferschy, and D. Pisinger. Knapsack Problems. 2004.
[22] Battista Biggio and Fabio Roli. Wild patterns: Ten years after the rise of adversarial machine learning. In ACM SIGSAC Conference on Computer and Communications Security, 2018.
[23] Aleksandar Bojchevski and Stephan Gunnemann. Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. In ICLR, 2018.
[24] Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. Collective classification in network data. AI Magazine, 2008.
[25] Péter Mernyei and Cătălina Cangea. Wiki-cs: A wikipedia-based benchmark for graph neural networks. arXiv:2007.02901, 2020.
[26] Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. Benchmarking graph neural networks, 2022.
[27] Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, and Liudmila Prokhorenkova. A critical look at the evaluation of GNNs under heterophily: Are we really making progress? In ICLR, 2023.
[28] Yash Deshpande, Andrea Montanari, Elchanan Mossel, and Subhabrata Sen. Contextual stochastic block models. NeurIPS, 2018.
[29] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. In ICLR, 2018.
[30] 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. In NeurIPS, 2020.
[31] Felix Mujkanovic, Simon Geisler, Stephan Gunnemann, and Aleksandar Bojchevski. Are defenses for graph neural networks robust? In NeurIPS, 2022.
[32] Aleksandar Bojchevski, Johannes Klicpera, and Stephan Gunnemann. Efficient robustness certificates for discrete data: Sparsity-aware randomized smoothing for graphs, images and more. In ICML, 2020.
[33] Fernando Gama, Joan Bruna, and Alejandro Ribeiro. Stability properties of graph neural networks. IEEE Trans. Signal Process., 2020.
[34] Zhijie Deng, Yinpeng Dong, and Jun Zhu. Batch virtual adversarial training for graph convolutional networks. In arXiv:1902.09192, 2019.
[35] Fuli Feng, Xiangnan He, Jie Tang, and Tat-Seng Chua. Graph adversarial training: Dynamically regularizing based on graph structure. IEEE Transactions on Knowledge and Data Engineering, 2019.
[36] Hongwei Jin and Xinhua Zhang. Latent adversarial training of graph convolution networks. In ICML Workshop on Learning and Reasoning with Graph-Structured Data, 2019.
[37] Jinyin Chen, Xiang Lin, Hui Xiong, Yangyang Wu, Haibin Zheng, and Qi Xuan. Smoothing adversarial training for gnn. IEEE Transactions on Computational Social Systems, 2021.
[38] Jintang Li, Jiaying Peng, Liang Chen, Zibin Zheng, Tingting Liang, and Qing Ling. Spectral adversarial training for robust graph neural network. In TKDE, 2022.