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, 2020-Decem(NeurIPS):1-34, 2020a.
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. Heterogeneous Graph Transformer. The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020, pages 2704-2710, 2020b. doi: 10.1145/3366423.3380027.
Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang. Adaptive sampling towards fast graph representation learning. Advances in Neural Information Processing Systems, 2018-Decem(Nips): 4558-4567, 2018.
Samir Khan and Johan Ugander. Adaptive normalization for IPW estimation. pages 1-31, 2021. URL http://arxiv.org/abs/2106.07695.
Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization, 2014. URL https://arxiv.org/abs/1412.6980.
Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings, pages 1-14, 2017.
Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, and Yuan Qi. Bandit samplers for training graph neural networks. Advances in Neural Information Processing Systems, 2020-Decem, 2020.
Xichuan Niu, Bofang Li, Chenliang Li, Rong Xiao, Haochuan Sun, Hongbo Deng, and Zhenzhong Chen. A dual heterogeneous graph attention network to improve long-tail performance for shop search in e-commerce. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '20, page 3405-3415, 2020. doi: 10.1145/3394486.3403393.
E Ohlsson. Sequentialoisson sampling. Journal of Official Statistics-Stockholm-, 14(2):149-162, 1998.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, 2019.
Zhihao Shi, Xize Liang, and Jie Wang. LMC: Fast training of GNNs via subgraph sampling with provable convergence. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=5VBBA91N6n.
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang. Deep graph library: A graph-centric, highly-performant package for graph neural networks, 2019. URL https://arxiv.org/abs/1909.01315.
Michael S Williams, Hans T Schreuder, and Gerardo H Terrazas. Poisson Sampling – The Adjusted and Unadjusted Estimator Revisited. page 12, 1998.
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. Graph convolutional neural networks for web-scale recommender systems. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 974-983, 2018. doi: 10.1145/3219819.3219890.
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. GraphSAINT: Graph sampling based inductive learning method. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=BJe8pkHFwS.