[25] Meng Liu, Hongyang Gao, and Shuiwang Ji. Towards deeper graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pages 338-348, 2020. 7, 10
[26] Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo, and Chuan Shi. Compact graph structure learning via mutual information compression. In Proceedings of the ACM Web Conference 2022, pages 1601-1610, 2022. 2, 4, 16
[27] Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, and Qing He. Pick and choose: a gnn-based imbalanced learning approach for fraud detection. In Proceedings of the Web Conference 2021, pages 3168-3177, 2021. 1
[28] Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, and Shirui Pan. Towards unsupervised deep graph structure learning. In Proceedings of the ACM Web Conference 2022, pages 1392-1403, 2022. 2, 4, 16
[29] Yuanfu Lu, Chuan Shi, Linmei Hu, and Zhiyuan Liu. Relation structure-aware heterogeneous information network embedding. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4456-4463, 2019. 2, 4, 14
[30] Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, and Xiang Zhang. Learning to drop: Robust graph neural network via topological denoising. In Proceedings of the 14th ACM international conference on web search and data mining, pages 779-787, 2021. 2, 16
[31] Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. Tudataset: A collection of benchmark datasets for learning with graphs. arXiv preprint arXiv:2007.08663, 2020.4
[32] Mark EJ Newman. Mixing patterns in networks. Physical review E, 67(2):026126, 2003. 18
[33] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019. 5
[34] Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. Geom-gcn: Geometric graph convolutional networks. arXiv preprint arXiv:2002.05287, 2020. 2, 4, 14, 17
[35] Ke Sun, Zhouchen Lin, and Zhanxing Zhu. Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 5892-5899, 2020. 10
[36] Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, and S Yu Philip. Graph structure learning with variational information bottleneck. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 4165-4174, 2022. 2, 4, 16, 17
[37] Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang. Social influence analysis in large-scale networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 807-816, 2009. 2, 14
[38] Minjie Yu Wang. Deep graph library: Towards efficient and scalable deep learning on graphs. In ICLR workshop on representation learning on graphs and manifolds, 2019. 5
[39] Ruijia Wang, Shuai Mou, Xiao Wang, Wanpeng Xiao, Qi Ju, Chuan Shi, and Xing Xie. Graph structure estimation neural networks. In Proceedings of the Web Conference 2021, pages 342-353, 2021. 2, 4, 16
[40] Oliver Wieder, Stefan Kohlbacher, Méline Kuenemann, Arthur Garon, Pierre Ducrot, Thomas Seidel, and Thierry Langer. A compact review of molecular property prediction with graph neural networks. *Drug Discovery Today: Technologies*, 37:1-12, 2020. 1
[41] Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu. Adversarial examples on graph data: Deep insights into attack and defense. arXiv preprint arXiv:1903.01610, 2019. 8
[42] Lirong Wu, Haitao Lin, Zihan Liu, Zicheng Liu, Yufei Huang, and Stan Z Li. Homophily-enhanced self-supervision for graph structure learning: Insights and directions. IEEE Transactions on Neural Networks and Learning Systems, 2023. 2, 4, 16
[43] Qitian Wu, Wentao Zhao, Zenan Li, David P Wipf, and Junchi Yan. Nodeformer: A scalable graph structure learning transformer for node classification. Advances in Neural Information Processing Systems, 35:27387-27401, 2022. 2, 4, 16