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As graphs are everywhere, GNNs can be applied to various domains including recommendation systems, computer vision, natural language processing, biology, and chemistry. With the rapid growing size of real-world graphs, the need for efficient and scalable GNN training solutions has come. Consequently, many works proposing GNN systems have emerged throughout the past few years. However, there is an acute lack of overview, categorization, and comparison of such systems. We aim to fill this gap by summarizing and categorizing important methods and techniques for large-scale GNN solutions. 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In Proceedings of the International Conference on Machine Learning. 1263\u20131272."},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.122653799"},{"key":"e_1_3_2_59_2","first-page":"17","volume-title":"Proceedings of the 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201912)","author":"Gonzalez Joseph E.","year":"2012","unstructured":"Joseph E. Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson, and Carlos Guestrin. 2012. PowerGraph: Distributed graph-parallel computation on natural graphs. In Proceedings of the 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201912). 17\u201330."},{"key":"e_1_3_2_60_2","first-page":"164","article-title":"Deep feedforward networks","author":"Goodfellow Ian","year":"2016","unstructured":"Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep feedforward networks. In Deep Learning. 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Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 (2017).","journal-title":"arXiv preprint arXiv:1709.05584"},{"key":"e_1_3_2_69_2","article-title":"Distributed edge partitioning for trillion-edge graphs","author":"Hanai Masatoshi","year":"2019","unstructured":"Masatoshi Hanai, Toyotaro Suzumura, Wen Jun Tan, Elvis Liu, Georgios Theodoropoulos, and Wentong Cai. 2019. Distributed edge partitioning for trillion-edge graphs. arXiv preprint arXiv:1908.05855 (2019).","journal-title":"arXiv preprint arXiv:1908.05855"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00508"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3199523"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.2172\/10106339"},{"key":"e_1_3_2_73_2","first-page":"1223","volume-title":"Proceedings of the 26th International Conference on Neural Information Processing Systems","author":"Ho Qirong","year":"2013","unstructured":"Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A. Gibson, Greg Ganger, and Eric P. Xing. 2013. More effective distributed ML via a stale synchronous parallel parameter server. In Proceedings of the 26th International Conference on Neural Information Processing Systems. 1223\u20131231."},{"key":"e_1_3_2_74_2","first-page":"1","article-title":"Efficient distribution for deep learning on large graphs","volume":"1050","author":"Hoang Loc","year":"2021","unstructured":"Loc Hoang, Xuhao Chen, Hochan Lee, Roshan Dathathri, Gurbinder Gill, and Keshav Pingali. 2021. Efficient distribution for deep learning on large graphs. Update 1050 (2021), 1.","journal-title":"Update"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2019.00054"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_77_2","article-title":"OGB-LSC: A large-scale challenge for machine learning on graphs","author":"Hu Weihua","year":"2021","unstructured":"Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec. 2021. OGB-LSC: A large-scale challenge for machine learning on graphs. arXiv preprint arXiv:2103.09430 (2021).","journal-title":"arXiv preprint arXiv:2103.09430"},{"key":"e_1_3_2_78_2","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open graph benchmark: Datasets for machine learning on graphs. 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IEEE, Los Alamitos, CA, 552\u2013563."},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437801.3441585"},{"key":"e_1_3_2_82_2","first-page":"103","article-title":"GPipe: Efficient training of giant neural networks using pipeline parallelism","author":"Huang Yanping","year":"2019","unstructured":"Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Chen, HyoukJoong Lee, et\u00a0al. 2019. GPipe: Efficient training of giant neural networks using pipeline parallelism. 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University of Minnesota.","journal-title":"Technical Report 97-061."},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1137\/S1064827595287997"},{"key":"e_1_3_2_94_2","doi-asserted-by":"publisher","DOI":"10.1109\/SC.1998.10018"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.1145\/2465351.2465369"},{"key":"e_1_3_2_96_2","volume-title":"Proceedings of the Workshop on Resource-Constrained Machine Learning (ReCoML\u201920)","author":"Kiningham Kevin","year":"2020","unstructured":"Kevin Kiningham, Philip Levis, and Christopher R\u00e9. 2020. GReTA: Hardware optimized graph processing for GNNs. In Proceedings of the Workshop on Resource-Constrained Machine Learning (ReCoML\u201920)."},{"key":"e_1_3_2_97_2","article-title":"GRIP: A graph neural network accelerator architecture","author":"Kiningham Kevin","year":"2020","unstructured":"Kevin Kiningham, Christopher Re, and Philip Levis. 2020. GRIP: A graph neural network accelerator architecture. arXiv preprint arXiv:2007.13828 (2020).","journal-title":"arXiv preprint arXiv:2007.13828"},{"key":"e_1_3_2_98_2","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).","journal-title":"arXiv preprint arXiv:1609.02907"},{"key":"e_1_3_2_99_2","article-title":"Variational graph auto-encoders","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016).","journal-title":"arXiv preprint arXiv:1611.07308"},{"key":"e_1_3_2_100_2","article-title":"CROSSBOW: Scaling deep learning with small batch sizes on multi-GPU servers","author":"Koliousis Alexandros","year":"2019","unstructured":"Alexandros Koliousis, Pijika Watcharapichat, Matthias Weidlich, Luo Mai, Paolo Costa, and Peter Pietzuch. 2019. 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In Proceedings of the International Conference on Machine Learning. 6437\u20136449."},{"key":"e_1_3_2_107_2","article-title":"DeeperGCN: All you need to train deeper GCNs","author":"Li Guohao","year":"2020","unstructured":"Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem. 2020. DeeperGCN: All you need to train deeper GCNs. arXiv preprint arXiv:2006.07739 (2020).","journal-title":"arXiv preprint arXiv:2006.07739"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1145\/2741948.2741965"},{"key":"e_1_3_2_109_2","article-title":"GraphTheta: A distributed graph neural network learning system with flexible training strategy","author":"Li Houyi","year":"2021","unstructured":"Houyi Li, Yongchao Liu, Yongyong Li, Bin Huang, Peng Zhang, Guowei Zhang, Xintan Zeng, Kefeng Deng, Wenguang Chen, and Changhua He. 2021. GraphTheta: A distributed graph neural network learning system with flexible training strategy. arXiv preprint arXiv:2104.10569 (2021).","journal-title":"arXiv preprint arXiv:2104.10569"},{"key":"e_1_3_2_110_2","doi-asserted-by":"crossref","unstructured":"Yaoman Li and Irwin King. 2020. AutoGraph: Automated graph neural network. In Neural Information Processing . Lecture Notes in Computer Science Vol. 12533. Springer 189\u2013201.","DOI":"10.1007\/978-3-030-63833-7_16"},{"key":"e_1_3_2_111_2","article-title":"Gated graph sequence neural networks","author":"Li Yujia","year":"2015","unstructured":"Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015. Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015).","journal-title":"arXiv preprint arXiv:1511.05493"},{"key":"e_1_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.1145\/3419111.3421281"},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"e_1_3_2_114_2","article-title":"BGL: GPU-efficient GNN training by optimizing graph data I\/O and preprocessing","author":"Liu Tianfeng","year":"2021","unstructured":"Tianfeng Liu, Yangrui Chen, Dan Li, Chuan Wu, Yibo Zhu, Jun He, Yanghua Peng, Hongzheng Chen, Hongzhi Chen, and Chuanxiong Guo. 2021. BGL: GPU-efficient GNN training by optimizing graph data I\/O and preprocessing. arXiv preprint arXiv:2112.08541 (2021).","journal-title":"arXiv preprint arXiv:2112.08541"},{"key":"e_1_3_2_115_2","article-title":"GraphLab: A Distributed Abstraction for Large Scale Machine Learning","author":"Low Yucheng","year":"2013","unstructured":"Yucheng Low. 2013. GraphLab: A Distributed Abstraction for Large Scale Machine Learning. University of California.","journal-title":"University of California."},{"key":"e_1_3_2_116_2","article-title":"Distributed GraphLab: A framework for machine learning in the cloud","author":"Low Yucheng","year":"2012","unstructured":"Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, and Joseph M. Hellerstein. 2012. Distributed GraphLab: A framework for machine learning in the cloud. arXiv preprint arXiv:1204.6078 (2012).","journal-title":"arXiv preprint arXiv:1204.6078"},{"key":"e_1_3_2_117_2","article-title":"GraphLab: A new framework for parallel machine learning","author":"Low Yucheng","year":"2014","unstructured":"Yucheng Low, Joseph E. Gonzalez, Aapo Kyrola, Danny Bickson, Carlos E. Guestrin, and Joseph Hellerstein. 2014. GraphLab: A new framework for parallel machine learning. arXiv preprint arXiv:1408.2041 (2014).","journal-title":"arXiv preprint arXiv:1408.2041"},{"key":"e_1_3_2_118_2","first-page":"443","volume-title":"Proceedings of the 2019 USENIX Annual Technical Conference (USENIX ATC\u201919)","author":"Ma Lingxiao","year":"2019","unstructured":"Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, and Yafei Dai. 2019. Neugraph: Parallel deep neural network computation on large graphs. In Proceedings of the 2019 USENIX Annual Technical Conference (USENIX ATC\u201919). 443\u2013458."},{"key":"e_1_3_2_119_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401092"},{"key":"e_1_3_2_120_2","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807184"},{"key":"e_1_3_2_121_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-5010"},{"key":"e_1_3_2_122_2","article-title":"Encoding sentences with graph convolutional networks for semantic role labeling","author":"Marcheggiani Diego","year":"2017","unstructured":"Diego Marcheggiani and Ivan Titov. 2017. Encoding sentences with graph convolutional networks for semantic role labeling. arXiv preprint arXiv:1703.04826 (2017).","journal-title":"arXiv preprint arXiv:1703.04826"},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-syb:20060038"},{"key":"e_1_3_2_124_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2018.00072"},{"key":"e_1_3_2_125_2","doi-asserted-by":"publisher","DOI":"10.1145\/3363554"},{"key":"e_1_3_2_126_2","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457300"},{"key":"e_1_3_2_127_2","article-title":"2PS: High-quality edge partitioning with two-phase streaming","author":"Mayer Ruben","year":"2020","unstructured":"Ruben Mayer, Kamil Orujzade, and Hans-Arno Jacobsen. 2020. 2PS: High-quality edge partitioning with two-phase streaming. arXiv preprint arXiv:2001.07086 (2020).","journal-title":"arXiv preprint arXiv:2001.07086"},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00242"},{"key":"e_1_3_2_129_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","DOI":"10.1145\/2818185"},{"key":"e_1_3_2_131_2","article-title":"DistGNN: Scalable distributed training for large-scale graph neural networks","author":"Md Vasimuddin","year":"2021","unstructured":"Vasimuddin Md, Sanchit Misra, Guixiang Ma, Ramanarayan Mohanty, Evangelos Georganas, Alexander Heinecke, Dhiraj Kalamkar, Nesreen K. Ahmed, and Sasikanth Avancha. 2021. DistGNN: Scalable distributed training for large-scale graph neural networks. arXiv preprint arXiv:2104.06700 (2021).","journal-title":"arXiv preprint arXiv:2104.06700"},{"key":"e_1_3_2_132_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00185"},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.1145\/219717.219748"},{"key":"e_1_3_2_134_2","doi-asserted-by":"publisher","DOI":"10.1145\/3341301.3359646"},{"key":"e_1_3_2_135_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2020.11.005"},{"key":"e_1_3_2_136_2","article-title":"Learning cross-domain representation with multi-graph neural network","author":"Ouyang Yi","year":"2019","unstructured":"Yi Ouyang, Bin Guo, Xing Tang, Xiuqiang He, Jian Xiong, and Zhiwen Yu. 2019. Learning cross-domain representation with multi-graph neural network. arXiv preprint arXiv:1905.10095 (2019).","journal-title":"arXiv preprint arXiv:1905.10095"},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3300076"},{"key":"e_1_3_2_138_2","volume-title":"The PageRank Citation Ranking: Bringing Order to the Web.","author":"Page Lawrence","year":"1999","unstructured":"Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web.Technical Report. Stanford InfoLab."},{"key":"e_1_3_2_139_2","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et\u00a0al. 2019. PyTorch: An imperative style, high-performance deep learning library. In Proceedings of the 33rd Conference on Neural Information Processing Systems. 1\u201312.","journal-title":"Proceedings of the 33rd Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_140_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.01.043"},{"key":"e_1_3_2_141_2","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806424"},{"key":"e_1_3_2_142_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.76.036106"},{"key":"e_1_3_2_143_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2005.07.009"},{"key":"e_1_3_2_144_2","article-title":"Searching for activation functions","author":"Ramachandran Prajit","year":"2017","unstructured":"Prajit Ramachandran, Barret Zoph, and Quoc V. Le. 2017. Searching for activation functions. arXiv preprint arXiv:1710.05941 (2017).","journal-title":"arXiv preprint arXiv:1710.05941"},{"key":"e_1_3_2_145_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2018.8545310"},{"key":"e_1_3_2_146_2","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"e_1_3_2_147_2","article-title":"Temporal graph networks for deep learning on dynamic graphs","author":"Rossi Emanuele","year":"2020","unstructured":"Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020. Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637 (2020).","journal-title":"arXiv preprint arXiv:2006.10637"},{"key":"e_1_3_2_148_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412758"},{"key":"e_1_3_2_149_2","article-title":"An overview of gradient descent optimization algorithms","author":"Ruder Sebastian","year":"2016","unstructured":"Sebastian Ruder. 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016).","journal-title":"arXiv preprint arXiv:1609.04747"},{"key":"e_1_3_2_150_2","first-page":"1","article-title":"Backpropagation: The basic theory","author":"Rumelhart David E.","year":"1995","unstructured":"David E. Rumelhart, Richard Durbin, Richard Golden, and Yves Chauvin. 1995. Backpropagation: The basic theory. In Backpropagation: Theory, Architectures and Applications, Y. Chauvin and D. E. Rumelhart (Eds.). Lawrence Erlbaum Associates, 1\u201334.","journal-title":"Backpropagation: Theory, Architectures and Applications,"},{"key":"e_1_3_2_151_2","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"e_1_3_2_152_2","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO54536.2021.9615973"},{"key":"e_1_3_2_153_2","doi-asserted-by":"publisher","DOI":"10.1145\/2484838.2484843"},{"key":"e_1_3_2_154_2","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1007\/978-3-030-25209-0","volume-title":"Sequential and Parallel Algorithms and Data Structures","author":"Sanders Peter","year":"2019","unstructured":"Peter Sanders, Kurt Mehlhorn, Martin Dietzfelbinger, and Roman Dementiev. 2019. Sequential and Parallel Algorithms and Data Structures. Springer, 403\u2013404."},{"key":"e_1_3_2_155_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"e_1_3_2_156_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_3_2_157_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975499.17"},{"key":"e_1_3_2_158_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534540.3534693"},{"key":"e_1_3_2_159_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"e_1_3_2_160_2","doi-asserted-by":"publisher","DOI":"10.1145\/3469379.3469387"},{"issue":"12","key":"e_1_3_2_161_2","first-page":"310","article-title":"Activation functions in neural networks","volume":"6","author":"Sharma Sagar","year":"2017","unstructured":"Sagar Sharma, Simone Sharma, and Anidhya Athaiya. 2017. Activation functions in neural networks. Towards Data Science 6, 12 (2017), 310\u2013316.","journal-title":"Towards Data Science"},{"key":"e_1_3_2_162_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306"},{"key":"e_1_3_2_163_2","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000530"},{"key":"e_1_3_2_164_2","doi-asserted-by":"publisher","DOI":"10.1145\/3128571"},{"key":"e_1_3_2_165_2","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2010.5586144"},{"key":"e_1_3_2_166_2","doi-asserted-by":"publisher","DOI":"10.1109\/SISY.2008.4664953"},{"key":"e_1_3_2_167_2","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339722"},{"key":"e_1_3_2_168_2","article-title":"Learning multiagent communication with backpropagation","author":"Sukhbaatar Sainbayar","year":"2016","unstructured":"Sainbayar Sukhbaatar, Arthur Szlam, and Rob Fergus. 2016. Learning multiagent communication with backpropagation. In Proceedings of the 30th International Conference on Neural Information Processing Systems. 2252\u20132260.","journal-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_169_2","article-title":"Degree-Quant: Quantization-aware training for graph neural networks","author":"Tailor Shyam A.","year":"2020","unstructured":"Shyam A. Tailor, Javier Fernandez-Marques, and Nicholas D. Lane. 2020. Degree-Quant: Quantization-aware training for graph neural networks. arXiv preprint arXiv:2008.05000 (2020).","journal-title":"arXiv preprint arXiv:2008.05000"},{"key":"e_1_3_2_170_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-6045-0_16"},{"key":"e_1_3_2_171_2","article-title":"Communication-efficient distributed deep learning: A comprehensive survey","author":"Tang Zhenheng","year":"2020","unstructured":"Zhenheng Tang, Shaohuai Shi, Xiaowen Chu, Wei Wang, and Bo Li. 2020. Communication-efficient distributed deep learning: A comprehensive survey. arXiv preprint arXiv:2003.06307 (2020).","journal-title":"arXiv preprint arXiv:2003.06307"},{"key":"e_1_3_2_172_2","doi-asserted-by":"publisher","DOI":"10.1137\/0201010"},{"key":"e_1_3_2_173_2","first-page":"495","volume-title":"Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201921)","author":"Thorpe John","year":"2021","unstructured":"John Thorpe, Yifan Qiao, Jonathan Eyolfson, Shen Teng, Guanzhou Hu, Zhihao Jia, Jinliang Wei, et\u00a0al. 2021. Dorylus: Affordable, scalable, and accurate GNN training with distributed CPU servers and serverless threads. In Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201921). 495\u2013514."},{"key":"e_1_3_2_174_2","doi-asserted-by":"publisher","DOI":"10.14778\/2732232.2732238"},{"key":"e_1_3_2_175_2","doi-asserted-by":"publisher","DOI":"10.1145\/2556195.2556213"},{"key":"e_1_3_2_176_2","doi-asserted-by":"publisher","DOI":"10.1145\/79173.79181"},{"key":"e_1_3_2_177_2","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).","journal-title":"arXiv preprint arXiv:1710.10903"},{"key":"e_1_3_2_178_2","first-page":"3","volume-title":"Proceedings of the 6th Biennial Conference on Innovative Data Systems Research (CIDR\u201913)","author":"Wang Guozhang","year":"2013","unstructured":"Guozhang Wang, Wenlei Xie, Alan J. Demers, and Johannes Gehrke. 2013. Asynchronous large-scale graph processing made easy. In Proceedings of the 6th Biennial Conference on Innovative Data Systems Research (CIDR\u201913). 3\u20136."},{"key":"e_1_3_2_179_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447786.3456229"},{"key":"e_1_3_2_180_2","unstructured":"Minjie Wang Lingfan Yu Da Zheng Quan Gan Yu Gai Zihao Ye Mufei Li et\u00a0al. 2019. Deep Graph Library: Towards efficient and scalable deep learning on graphs. arXiv:1909.01315 (2019)."},{"key":"e_1_3_2_181_2","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457564"},{"key":"e_1_3_2_182_2","doi-asserted-by":"publisher","DOI":"10.1145\/2851141.2851145"},{"key":"e_1_3_2_183_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503221.3508408"},{"key":"e_1_3_2_184_2","first-page":"515","volume-title":"Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201921)","author":"Wang Yuke","unstructured":"Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, and Yufei Ding. 2021. GNNAdvisor: An adaptive and efficient runtime system for GNN acceleration on GPUs. In Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201921). 515\u2013531."},{"key":"e_1_3_2_185_2","doi-asserted-by":"publisher","DOI":"10.1145\/2987550.2987586"},{"key":"e_1_3_2_186_2","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2915220"},{"key":"e_1_3_2_187_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.58323"},{"key":"e_1_3_2_188_2","first-page":"6861","volume-title":"Proceedings of the","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In Proceedings of theInternational Conference on Machine Learning. 6861\u20136871."},{"key":"e_1_3_2_189_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447786.3456247"},{"key":"e_1_3_2_190_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_191_2","doi-asserted-by":"publisher","DOI":"10.1145\/2858788.2688508"},{"key":"e_1_3_2_192_2","first-page":"1673","volume-title":"Proceedings of the 28th Conference on Neural Information Processing Systems","author":"Xie Cong","year":"2014","unstructured":"Cong Xie, Ling Yan, Wu-Jun Li, and Zhihua Zhang. 2014. Distributed power-law graph computing: Theoretical and empirical analysis. In Proceedings of the 28th Conference on Neural Information Processing Systems. 1673\u20131681."},{"key":"e_1_3_2_193_2","article-title":"How powerful are graph neural networks?","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018).","journal-title":"arXiv preprint arXiv:1810.00826"},{"key":"e_1_3_2_194_2","first-page":"5453","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-Ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In Proceedings of the International Conference on Machine Learning. 5453\u20135462."},{"key":"e_1_3_2_195_2","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC.2014.6983053"},{"key":"e_1_3_2_196_2","article-title":"HyperGCN: A new method for training graph convolutional networks on hypergraphs","author":"Yadati Naganand","year":"2019","unstructured":"Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Talukdar. 2019. HyperGCN: A new method for training graph convolutional networks on hypergraphs. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. 1511\u20131522.","journal-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_197_2","doi-asserted-by":"publisher","DOI":"10.14778\/2733085.2733103"},{"key":"e_1_3_2_198_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3340404"},{"key":"e_1_3_2_199_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0693-z"},{"key":"e_1_3_2_200_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"e_1_3_2_201_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_202_2","first-page":"7134","volume-title":"Proceedings of the International Conference on Machine Learning","author":"You Jiaxuan","year":"2019","unstructured":"Jiaxuan You, Rex Ying, and Jure Leskovec. 2019. Position-aware graph neural networks. In Proceedings of the International Conference on Machine Learning. 7134\u20137143."},{"key":"e_1_3_2_203_2","article-title":"GraphSAINT: Graph sampling based inductive learning method","author":"Zeng Hanqing","year":"2019","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2019. GraphSAINT: Graph sampling based inductive learning method. arXiv preprint arXiv:1907.04931 (2019).","journal-title":"arXiv preprint arXiv:1907.04931"},{"key":"e_1_3_2_204_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASAP49362.2020.00019"},{"key":"e_1_3_2_205_2","article-title":"AGL: A scalable system for industrial-purpose graph machine learning","author":"Zhang Dalong","year":"2020","unstructured":"Dalong Zhang, Xin Huang, Ziqi Liu, Zhiyang Hu, Xianzheng Song, Zhibang Ge, Zhiqiang Zhang, et\u00a0al. 2020. AGL: A scalable system for industrial-purpose graph machine learning. arXiv preprint arXiv:2003.02454 (2020).","journal-title":"arXiv preprint arXiv:2003.02454"},{"key":"e_1_3_2_206_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00287"},{"key":"e_1_3_2_207_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Zhang Shichang","year":"2022","unstructured":"Shichang Zhang, Yozen Liu, Yizhou Sun, and Neil Shah. 2022. Graph-less neural networks: Teaching old MLPs new tricks via distillation. In Proceedings of the International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=4p6_5HBWPCw"},{"key":"e_1_3_2_208_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482419"},{"key":"e_1_3_2_209_2","doi-asserted-by":"publisher","DOI":"10.1145\/3405671.3405810"},{"key":"e_1_3_2_210_2","article-title":"Deep learning on graphs: A survey","author":"Zhang Ziwei","year":"2022","unstructured":"Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2022. Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering 34 (2022), 249\u2013270.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_211_2","article-title":"ZIPPER: Exploiting tile- and operator-level parallelism for general and scalable graph neural network acceleration","author":"Zhang Zhihui","year":"2021","unstructured":"Zhihui Zhang, Jingwen Leng, Shuwen Lu, Youshan Miao, Yijia Diao, Minyi Guo, Chao Li, and Yuhao Zhu. 2021. ZIPPER: Exploiting tile- and operator-level parallelism for general and scalable graph neural network acceleration. arXiv preprint arXiv:2107.08709 (2021).","journal-title":"arXiv preprint arXiv:2107.08709"},{"key":"e_1_3_2_212_2","doi-asserted-by":"publisher","DOI":"10.1109\/PADSW.2018.8644613"},{"key":"e_1_3_2_213_2","article-title":"Learned low precision graph neural networks","author":"Zhao Yiren","year":"2020","unstructured":"Yiren Zhao, Duo Wang, Daniel Bates, Robert Mullins, Mateja Jamnik, and Pietro Lio. 2020. Learned low precision graph neural networks. arXiv preprint arXiv:2009.09232 (2020).","journal-title":"arXiv preprint arXiv:2009.09232"},{"key":"e_1_3_2_214_2","doi-asserted-by":"publisher","DOI":"10.1109\/IA351965.2020.00011"},{"key":"e_1_3_2_215_2","doi-asserted-by":"publisher","DOI":"10.14778\/3461535.3461547"},{"key":"e_1_3_2_216_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_2_217_2","first-page":"375","volume-title":"Proceedings of the 2015 USENIX Annual Technical Conference (USENIX ATC\u201915)","author":"Zhu Xiaowei","unstructured":"Xiaowei Zhu, Wentao Han, and Wenguang Chen. 2015. GridGraph: Large-scale graph processing on a single machine using 2-level hierarchical partitioning. In Proceedings of the 2015 USENIX Annual Technical Conference (USENIX ATC\u201915). 375\u2013386."},{"key":"e_1_3_2_218_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx252"},{"key":"e_1_3_2_219_2","article-title":"Layer-dependent importance sampling for training deep and large graph convolutional networks","author":"Zou Difan","year":"2019","unstructured":"Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, and Quanquan Gu. 2019. 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