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Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs.
Semi-supervised learning using gaussian fields and harmonic functions
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Local graph partitioning using pagerank vectors. In FOCS’06 . IEEE, 475–486
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Semi-supervised learning
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Arnetminer: extraction and mining of academic social networks. In KDD’08
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
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On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
Shirish Nitish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Tak Peter Ping Tang. 2017 · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
N. Thomas Kipf and Max Welling. 2017 · 2017
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FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
Jie Chen, Tengfei Ma, and Cao Xiao. 2018 · 2018
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Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. In KDD’19
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 2019
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Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann. 2019a · 2019
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Diffusion improves graph learning
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Optimizing generalized pagerank methods for seed-expansion community detection
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Simplifying graph convolutional networks
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Semi-supervised learning on graphs with generative adversarial nets
Ming Ding, Jie Tang, and Jie Zhang. 2018 · 2018
Cited alongside, same era.
Deeper insights into graph convolutional networks for semi-supervised learning. In AAAI’18
Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018 · 2018
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Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In WSDM’18 . 459–467
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018 · 2018
Cited alongside, same era.
Graph Attention Networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
Mixhop: Higher-order graph convolution architectures via sparsified neighborhood mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Hrayr Harutyunyan, Nazanin Alipourfard, Kristina Lerman, Greg Ver Steeg, and Aram Galstyan. 2019 · 2019
Cited alongside, same era.
MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot, Nicholas Carlini, J. Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel. 2019 · 2019
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Simple and deep graph convolutional networks. In ICML . PMLR, 1725–1735
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020b
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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 · 2019
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Scaling graph neural networks with approximate pagerank. In KDD’20 . 2464–2473
Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann. 2020 · 2020
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Scalable Graph Neural Networks via Bidirectional Propagation
Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, and Ji-Rong Wen. 2020a · 2020
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Graph Random Neural Network for Semi-Supervised Learning on Graphs
Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang. 2020 · 2020
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FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Sohn Kihyuk, Berthelot David, Li Chun-Liang, Zhang Zizhao, Carlini Nicholas, Ekin Cubuk D., Kurakin Alex, Zhang Han, and Raffel Colin. 2020 · 2020
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GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2020 · 2020
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