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Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming.
Self-supervised training of graph convolutional networks
Qikui Zhu, Bo Du, and Pingkun Yan · 2006
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Deep graph contrastive representation learning
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2006
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Image-based recommendations on styles and substitutes
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel · 2015
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov · 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
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 2018
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Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Smoothout: Smoothing out sharp minima to improve generalization in deep learning
Wei Wen, Yandan Wang, Feng Yan, Cong Xu, Chunpeng Wu, Yiran Chen, and Hai Li · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec · 2018
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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Graph convolutional networks: Algorithms, applications and open challenges
Si Zhang, Hanghang Tong, Jiejun Xu, and Ross Maciejewski · 2018
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
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Exploring the landscape of spatial robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2019
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Graph neural networks for social recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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Understanding generalization through visualizations
W Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K Terry, Furong Huang, and Tom Goldstein · 2019
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Estimating node importance in knowledge graphs using graph neural networks
Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, and Christos Faloutsos · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2019
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Graph-mlp: node classification without message passing in graph
Yang Hu, Haoxuan You, Zhecan Wang, Zhicheng Wang, Erjin Zhou, and Yue Gao · 2021
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Training graph neural networks with 1000 layers
Guohao Li, Matthias Müller, Bernard Ghanem, and Vladlen Koltun · 2021
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A unified view on graph neural networks as graph signal denoising
Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, and Neil Shah · 2021
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Graph neural networks for friend ranking in large-scale social platforms
Aravind Sankar, Yozen Liu, Jun Yu, and Neil Shah · 2021
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Scalable and adaptive graph neural networks with self-label-enhanced training
Chuxiong Sun, Hongming Gu, and Jie Hu · 2021
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Siddhant Arora · 2020
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Graph coarsening with neural networks
Chen Cai, Dingkang Wang, and Yusu Wang · 2020
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Principal neighbourhood aggregation for graph nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković · 2020
Cited alongside, same era.
Sign: Scalable inception graph neural networks
Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, and Federico Monti · 2020
Cited alongside, same era.
Contrastive multi-view representation learning on graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 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.
Combining label propagation and simple models out-performs graph neural networks
Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, and Austin R Benson · 2020
Cited alongside, same era.
Do we need anisotropic graph neural networks?
Shyam A Tailor, Felix Opolka, Pietro Lio, and Nicholas Donald Lane · 2021
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Grand++: Graph neural diffusion with a source term
Matthew Thorpe, Tan Minh Nguyen, Hedi Xia, Thomas Strohmer, Andrea Bertozzi, Stanley Osher, and Bao Wang · 2021
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Mixed-curvature multi-relational graph neural network for knowledge graph completion
Shen Wang, Xiaokai Wei, Cicero Nogueira Nogueira dos Santos, Zhiguo Wang, Ramesh Nallapati, Andrew Arnold, Bing Xiang, Philip S Yu, and Isabel F Cruz · 2021
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Graph contrastive learning automated
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang · 2021
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Action sequence augmentation for early graph-based anomaly detection
Tong Zhao, Bo Ni, Wenhao Yu, Zhichun Guo, Neil Shah, and Meng Jiang · 2021
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Graph contrastive learning with adaptive augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2021
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A comprehensive study on large-scale graph training: Benchmarking and rethinking
Keyu Duan, Zirui Liu, Peihao Wang, Wenqing Zheng, Kaixiong Zhou, Tianlong Chen, Xia Hu, and Zhangyang Wang · 2022
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Tf-gnn: Graph neural networks in tensorflow
Oleksandr Ferludin, Arno Eigenwillig, Martin Blais, Dustin Zelle, Jan Pfeifer, Alvaro Sanchez-Gonzalez, Sibon Li, Sami Abu-El-Haija, Peter Battaglia, Neslihan Bulut, et al · 2022
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Geometric graph representation learning via maximizing rate reduction
Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Qingquan Song, Jundong Li, and Xia Hu · 2022
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Graph neural network for traffic forecasting: A survey
Weiwei Jiang and Jiayun Luo · 2022
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Graph rationalization with environment-based augmentations
Gang Liu, Tong Zhao, Jiaxin Xu, Tengfei Luo, and Meng Jiang · 2022
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Friend story ranking with edge-contextual local graph convolutions
Xianfeng Tang, Yozen Liu, Xinran He, Suhang Wang, and Neil Shah · 2022
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Bns-gcn: Efficient full-graph training of graph convolutional networks with partition-parallelism and random boundary node sampling
Cheng Wan, Youjie Li, Ang Li, Nam Sung Kim, and Yingyan Lin · 2022
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Graphfm: Improving large-scale gnn training via feature momentum
Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, and Shuiwang Ji · 2022
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Graph attention multi-layer perceptron
Wentao Zhang, Ziqi Yin, Zeang Sheng, Yang Li, Wen Ouyang, Xiaosen Li, Yangyu Tao, Zhi Yang, and Bin Cui · 2022
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Graph neural networks are inherently good generalizers: Insights by bridging gnns and mlps
Chenxiao Yang, Qitian Wu, Jiahua Wang, and Junchi Yan · 2023
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