[9] Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, and Philip S Yu. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pages 315-324, 2020.
[10] Jian Du, Shanghang Zhang, Guanhang Wu, José MF Moura, and Soummya Kar. Topology adaptive graph convolutional networks. arXiv preprint arXiv:1710.10370, 2017.
[11] Alex Fout, Jonathon Byrd, Basir Shariat, and Asa Ben-Hur. Protein interface prediction using graph convolutional networks. Advances in neural information processing systems, 30, 2017.
[12] Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Gunnemann. Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997, 2018.
[13] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in neural information processing systems, 30, 2017.
[14] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[15] Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard TB Ma, Hongzhi Chen, and Ming-Chang Yang. Measuring and improving the use of graph information in graph neural networks. In International Conference on Learning Representations, 2019.
[16] Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, 33:22118-22133, 2020.
[17] Brian Karrer and Mark EJ Newman. Stochastic blockmodels and community structure in networks. Physical review E, 83(1):016107, 2011.
[18] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[19] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
[20] Qimai Li, Zhichao Han, and Xiao-Ming Wu. Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018.
[21] Yanming Li, Bin Nan, and Ji Zhu. Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure. Biometrics, 71(2):354-363, 2015.
[22] Langzhang Liang, Zenglin Xu, Zixing Song, Irwin King, and Jieping Ye. Resnorm: Tackling long-tailed degree distribution issue in graph neural networks via normalization. arXiv preprint arXiv:2206.08181, 2022.
[23] Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser Nam Lim. Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods. Advances in Neural Information Processing Systems, 34:20887-20902, 2021.
[24] Renming Liu, Semih Cantürk, Frederik Wenkel, Sarah McGuire, Xinyi Wang, Anna Little, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, et al. Taxonomy of benchmarks in graph representation learning. In Learning on Graphs Conference, pages 6-1. PMLR, 2022.
[25] Xiao Liu, Lijun Zhang, and Hui Guan. Uplifting message passing neural network with graph original information, 2023.
[26] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.