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Teaching Graph Neural Networks (GNNs) to accurately classify nodes under severely noisy labels is an important problem in real-world graph learning applications, but is currently underexplored.
Learning from labeled and unlabeled data with label propagation
Xiaojin Zhu and Zoubin Ghahramani · 2002
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Qing Lu and Lise Getoor · 2003
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Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schölkopf · 2003
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Xiaojin Zhu, Zoubin Ghahramani, and John D. Lafferty · 2003
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Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, and Masashi Sugiyama · 2011
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Noisy labels can induce good representations
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Tongliang Liu and Dacheng Tao · 2015
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Scott E. Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2015
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Training convolutional networks with noisy labels, 2015
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2015
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Learning with symmetric label noise: The importance of being unhinged
Brendan van Rooyen, Aditya Krishna Menon, and Robert C. Williamson · 2015
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov · 2016
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Neural graph machines: Learning neural networks using graphs
Thang D. Bui, Sujith Ravi, and Vivek Ramavajjala · 2017
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William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Eran Malach and Shai Shalev-Shwartz · 2017
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Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 2017
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Brendan van Rooyen and Robert C. Williamson · 2017
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Yixin Wang, Alp Kucukelbir, and David M. Blei · 2017
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Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Contextual graph markov model: A deep and generative approach to graph processing
Davide Bacciu, Federico Errica, and Alessio Micheli · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, and Masashi Sugiyama · 2018
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Bayesian semi-supervised learning with graph gaussian processes
Yin Cheng Ng, Nicolò Colombo, and Ricardo Silva · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert R. Sabuncu · 2018
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, and Bin Luo · 2019
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A simple and effective framework for pairwise deep metric learning
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Graphaf: a flow-based autoregressive model for molecular graph generation
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Graph-revised convolutional network
Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, and Yiming Yang · 2020
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Xujiang Zhao, Feng Chen, Shu Hu, and Jin-Hee Cho · 2020
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Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, and Wei Wang · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
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Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, and Richard S. Zemel · 2019
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CGNF: Conditional graph neural fields, 2019
Tengfei Ma, Cao Xiao, Junyuan Shang, and Jimeng Sun · 2019
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Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil A. Platanios, Sujith Ravi, and Andrew Tomkins · 2019
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Combating label noise in deep learning using abstention
Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff A. Bilmes, Gopinath Chennupati, and Jamal Mohd-Yusof · 2019
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Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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