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Graph neural networks (GNN) have shown great success in learning from graph-structured data.
A fast and high quality multilevel scheme for partitioning irregular graphs
George Karypis and Vipin Kumar · 1998
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Multilevel algorithms for multi-constraint graph partitioning
George Karypis and Vipin Kumar · 1998
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The general inefficiency of batch training for gradient descent learning
D Randall Wilson and Tony R Martinez · 2003
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Parmetis 4.0: Parallel graph partitioning and sparse matrix ordering library
G. Karypis and Kirk Schloegel · 2011
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Efficient backprop
Yann A LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 2012
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Cluster-gcn
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Neugraph: Parallel deep neural network computation on large graphs
Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, and Yafei Dai · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
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 · 2019
Pagraph: Scaling gnn training on large graphs via computation-aware caching
Zhiqi Lin, Cheng Li, Youshan Miao, Yunxin Liu, and Yinlong Xu · 2020
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Reducing communication in graph neural network training
Alok Tripathy, Katherine Yelick, and Aydin Buluc · 2020
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AGL: a scalable system for industrial-purpose graph machine learning
Dalong Zhang, Xin Huang, Ziqi Liu, Zhiyang Hu, Xianzheng Song, Zhibang Ge, Zhiqiang Zhang, Lin Wang, Jun Zhou, Yang Shuang, and Yuan Qi · 2020
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P3: Distributed deep graph learning at scale
Swapnil Gandhi and Anand Padmanabha Iyer · 2021
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Ogb-lsc: A large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
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AliGraph: A comprehensive graph neural network platform
Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, and Jingren Zhou · 2019
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Layer-dependent importance sampling for training deep and large graph convolutional networks
Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, and Quanquan Gu · 2019
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https://github.com/alibaba/euler , 2020
Euler github · 2020
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
Cited alongside, same era.
Improving the accuracy, scalability, and performance of graph neural networks with roc
Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken · 2020
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Freebase data dumps
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Bgl: Gpu-efficient gnn training by optimizing graph data i/o and preprocessing
Tianfeng Liu, Yangrui Chen, Dan Li, Chuan Wu, Yibo Zhu, Jun He, Yanghua Peng, Hongzheng Chen, Hongzhi Chen, and Chuanxiong Guo · 2021
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Distgnn: Scalable distributed training for large-scale graph neural networks
Vasimuddin Md, Sanchit Misra, Guixiang Ma, Ramanarayan Mohanty, Evangelos Georganas, Alexander Heinecke, Dhiraj D. Kalamkar, Nesreen K. Ahmed, and Sasikanth Avancha · 2021
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Dorylus: Affordable, scalable, and accurate GNN training with distributed CPU servers and serverless threads
John Thorpe, Yifan Qiao, Jonathan Eyolfson, Shen Teng, Guanzhou Hu, Zhihao Jia, Jinliang Wei, Keval Vora, Ravi Netravali, Miryung Kim, and Guoqing Harry Xu · 2021
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Distdgl: Distributed graph neural network training for billion-scale graphs
Da Zheng, Chao Ma, Minjie Wang, Jinjing Zhou, Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, and George Karypis · 2021
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