[22] Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, and Cho-Jui Hsieh. Evaluations and methods for explanation through robustness analysis. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, 2021.
[23] Yongduo Sui, Tianlong Chen, Pengfei Xia, Shuyao Wang, and Bin Li. Towards robust detection and segmentation using vertical and horizontal adversarial training. In 2022 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE, 2022.
[24] Daniel Zügner, Amir Akbarnejad, and Stephan Gunnemann. Adversarial attacks on neural networks for graph data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018, pages 2847-2856, 2018.
[25] Daniel Zügner and Stephan Gümnmann. Adversarial attacks on graph neural networks via meta learning. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, 2019.
[26] Jure Leskovec, Jon M. Kleinberg, and Christos Faloutsos. Graphs over time: densification laws, shrinking diameters and possible explanations. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, Illinois, USA, August 21-24, 2005, pages 177-187, 2005.
[27] Beatrice Bevilacqua, Yangze Zhou, and Bruno Ribeiro. Size-invariant graph representations for graph classification extrapolations. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, pages 837-851, 2021.
[28] Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander L. Gaunt. Constrained graph variational autoencoders for molecule design. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montreal, Canada, pages 7806-7815, 2018.
[29] Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. OOD-GNN: out-of-distribution generalized graph neural network. CoRR, abs/2112.03806, 2021.
[30] Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Quan Z. Sheng, and Hui Xiong. A comprehensive survey on graph anomaly detection with deep learning. CoRR, abs/2106.07178, 2021.
[31] Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, and Yongdong Zhang. Alleviating structural distribution shift in graph anomaly detection. In WSDM, pages 357-365. ACM, 2023.
[32] Xian Teng, Muheng Yan, Ali Mert Ertugrul, and Yu-Ru Lin. Deep into hypersphere: Robust and unsupervised anomaly discovery in dynamic networks. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, pages 2724-2730, 2018.
[33] Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, and Yongdong Zhang. Addressing heterophily in graph anomaly detection: A perspective of graph spectrum. In WWW, pages 1528-1538. ACM, 2023.
[34] Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, and Yong-Dong Zhang. Rumor detection with self-supervised learning on texts and social graph. Frontiers Comput. Sci., 17(4):174611, 2023.
[35] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In NeurIPS, 2020.
[36] Prafulla Dhariwal and Alexander Quinn Nichol. Diffusion models beat gans on image synthesis. In NeurIPS, pages 8780-8794, 2021.
[37] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In ICLR (Poster), 2017.