[44] Hui Xu, Liyao Xiang, Jiahao Yu, Anqi Cao, and Xinbing Wang. Speedup robust graph structure learning with low-rank information. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pages 2241-2250, 2021. 16
[45] Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu, and Liang Wang. Evidence-aware fake news detection with graph neural networks. In Proceedings of the ACM Web Conference 2022, pages 2501-2510, 2022. 1
[46] Pinar Yanardag and SVN Vishwanathan. Deep graph kernels. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 1365-1374, 2015. 2, 4
[47] Zhilin Yang, William Cohen, and Ruslan Salakhudinov. Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning, pages 40-48. PMLR, 2016. 2, 4, 14
[48] Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, and Yiming Yang. Graph-revised convolutional network. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III, pages 378–393. Springer, 2021. 2, 4, 15, 16
[49] Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim. Graph transformer networks. Advances in neural information processing systems, 32, 2019. 2, 4, 14, 16, 17
[50] Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, and Liang Wang. Mining latent structures for multimedia recommendation. In Proceedings of the 29th ACM International Conference on Multimedia, pages 3872-3880, 2021. 2
[51] Yanfu Zhang, Hongchang Gao, Jian Pei, and Heng Huang. Robust self-supervised structural graph neural network for social network prediction. In Proceedings of the ACM Web Conference 2022, pages 1352-1361, 2022. 1
[52] Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, and Can Wang. Hierarchical graph pooling with structure learning. arXiv preprint arXiv:1911.05954, 2019. 2, 4, 16, 17
[53] Jianan Zhao, Xiao Wang, Chuan Shi, Binbin Hu, Guojie Song, and Yanfang Ye. Heterogeneous graph structure learning for graph neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 4697-4705, 2021. 2, 4, 16, 17
[54] Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, and Yanfang Ye. Self-supervised graph structure refinement for graph neural networks. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pages 159–167, 2023. 2, 4, 16
[55] Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, and Wei Wang. Robust graph representation learning via neural sparsification. In International Conference on Machine Learning, pages 11458-11468. PMLR, 2020. 2, 8, 16
[56] Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in Neural Information Processing Systems, 33:7793-7804, 2020. 17
[57] Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, and Shu Wu. A survey on graph structure learning: Progress and opportunities. arXiv e-prints, pages arXiv-2103, 2021. 2
[58] Daniel Zügner and Stephan Gunnemann. Adversarial attacks on graph neural networks via meta learning. 2019. 8