Fetching the paper…
Reading the bibliography…
Despite the success of the carefully-annotated benchmarks, the effectiveness of existing graph neural networks (GNNs) can be considerably impaired in practice when the real-world graph data is noisily labeled.
A set of measures of centrality based on betweenness
Linton C Freeman · 1977
Earlier work this paper cites.
Rethinking centrality: Methods and examples
Karen Stephenson and Marvin Zelen · 1989
Earlier work this paper cites.
Probably approximately correct learning
David Haussler · 1990
Earlier work this paper cites.
Centrality in valued graphs: A measure of betweenness based on network flow
Linton C Freeman, Stephen P Borgatti, and Douglas R White · 1991
Earlier work this paper cites.
The probably approximately correct (pac) and other learning models
David Haussler and Manfred Warmuth · 1993
Earlier work this paper cites.
The nature of statistical learning theory
Vladimir Vapnik · 1999
Earlier work this paper cites.
A faster algorithm for betweenness centrality
Ulrik Brandes · 2001
Earlier work this paper cites.
An analytical comparison of approaches to personalizing pagerank
Taher Haveliwala, Sepandar Kamvar, Glen Jeh, et al · 2003
Earlier work this paper cites.
Betweenness centrality in large complex networks
Marc Barthelemy · 2004
Earlier work this paper cites.
Random walks on complex networks
Jae Dong Noh and Heiko Rieger · 2004
Earlier work this paper cites.
A measure of betweenness centrality based on random walks
Mark EJ Newman · 2005
Earlier work this paper cites.
Learning theory: an approximation theory viewpoint , volume 24
Felipe Cucker and Ding Xuan Zhou · 2007
Earlier work this paper cites.
Graphical models for marked point processes based on local independence
Vanessa Didelez · 2008
Earlier work this paper cites.
Markov chains
Daniel Revuz · 2008
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
Fast incremental and personalized pagerank
Bahman Bahmani, Abdur Chowdhury, and Ashish Goel · 2010
Earlier work this paper cites.
Active learning by querying informative and representative examples
Sheng-Jun Huang, Rong Jin, and Zhi-Hua Zhou · 2010
Earlier work this paper cites.
Self-paced learning for latent variable models
M Kumar, Benjamin Packer, and Daphne Koller · 2010
Earlier work this paper cites.
Link prediction based on local random walk
Weiping Liu and Linyuan Lü · 2010
Earlier work this paper cites.
Fast random walk graph kernel
U Kang, Hanghang Tong, and Jimeng Sun · 2012
Earlier work this paper cites.
Classification in the presence of label noise: a survey
Benoît Frénay and Michel Verleysen · 2013
Earlier work this paper cites.
Novel tight classification error bounds under mismatch conditions based on f-divergence
Ralf Schlüter, Markus Nussbaum-Thom, Eugen Beck, Tamer Alkhouli, and Hermann Ney · 2013
Earlier work this paper cites.
Self-paced learning with diversity
Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, and Alexander Hauptmann · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2015
Earlier work this paper cites.
Local dependence in random graph models: characterization, properties and statistical inference
Michael Schweinberger and Mark S Handcock · 2015
Earlier work this paper cites.
Why curriculum learning & self-paced learning work in big/noisy data: A theoretical perspective
Tieliang Gong, Qian Zhao, Deyu Meng, and Zongben Xu · 2016
Earlier work this paper cites.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Tri-party deep network representation
Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Yang Wang · 2016
Earlier work this paper cites.
Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
Earlier work this paper cites.
A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Deep learning is robust to massive label noise
David Rolnick, Andreas Veit, Serge Belongie, and Nir Shavit · 2017
Cited alongside, same era.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Cited alongside, same era.
Fastgcn: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
Cited alongside, same era.
Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John S Shawe-Taylor, and Massimiliano Pontil · 2018
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
Topology-imbalance learning for semi-supervised node classification
Deli Chen, Yankai Lin, Guangxiang Zhao, Xuancheng Ren, Peng Li, Jie Zhou, and Xu Sun · 2021
Later among the works it cites.
Nrgnn: Learning a label noise resistant graph neural network on sparsely and noisily labeled graphs
Enyan Dai, Charu Aggarwal, and Suhang Wang · 2021
Later among the works it cites.
Pi-gnn: A novel perspective on semi-supervised node classification against noisy labels
Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, and Junzhou Huang · 2021
Later among the works it cites.
Unified robust training for graph neural networks against label noise
Yayong Li, Jie Yin, and Ling Chen · 2021
Later among the works it cites.
Is homophily a necessity for graph neural networks?
Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Cited alongside, same era.
Curriculumnet: Weakly supervised learning from large-scale web images
Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R Scott, and Dinglong Huang · 2018
Cited alongside, same era.
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 Tsang, and Masashi Sugiyama · 2018
Cited alongside, same era.
Decision boundary analysis of adversarial examples
Warren He, Bo Li, and Dawn Song · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Cited alongside, same era.
Understanding and utilizing deep neural networks trained with noisy labels
Pengfei Chen, Ben Ben Liao, Guangyong Chen, and Shengyu Zhang · 2019
Cited alongside, same era.
Multi-modal curriculum learning over graphs
Chen Gong, Jian Yang, and Dacheng Tao · 2019
Cited alongside, same era.
Confident learning: Estimating uncertainty in dataset labels
Curtis Northcutt, Lu Jiang, and Isaac Chuang · 2021
Later among the works it cites.
Multi-scale attributed node embedding
Benedek Rozemberczki, Carl Allen, and Rik Sarkar · 2021
Later among the works it cites.
To smooth or not? when label smoothing meets noisy labels
Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, and Yang Liu · 2021
Later among the works it cites.
Handling distribution shifts on graphs: An invariance perspective
Qitian Wu, Hengrui Zhang, Junchi Yan, and David Wipf · 2021
Later among the works it cites.
Sample selection with uncertainty of losses for learning with noisy labels
Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, and Masashi Sugiyama · 2021
Later among the works it cites.
Instance-dependent label-noise learning under a structural causal model
Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, and Kun Zhang · 2021
Later among the works it cites.
Towards robust graph neural networks for noisy graphs with sparse labels
Enyan Dai, Wei Jin, Hui Liu, and Suhang Wang · 2022
Later among the works it cites.
Missdag: Causal discovery in the presence of missing data with continuous additive noise models
Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, and Howard Bondell · 2022
Later among the works it cites.
What makes graph neural networks miscalibrated?
Hans Hao-Hsun Hsu, Yuesong Shen, Christian Tomani, and Daniel Cremers · 2022
Later among the works it cites.
Tam: Topology-aware margin loss for class-imbalanced node classification
Jaeyun Song, Joonhyung Park, and Eunho Yang · 2022
Later among the works it cites.
Energy-based out-of-distribution detection for graph neural networks
Qitian Wu, Yiting Chen, Chenxiao Yang, and Junchi Yan · 2022
Later among the works it cites.
Geometric knowledge distillation: Topology compression for graph neural networks
Chenxiao Yang, Qitian Wu, and Junchi Yan · 2022
Later among the works it cites.
The random walk-based gravity model to identify influential nodes in complex networks
Jie Zhao, Tao Wen, Hadi Jahanshahi, and Kang Hao Cheong · 2022
Later among the works it cites.
Towards open-set object detection and discovery
Jiyang Zheng, Weihao Li, Jie Hong, Lars Petersson, and Nick Barnes · 2022
Later among the works it cites.
Mentorgnn: Deriving curriculum for pre-training gnns
Dawei Zhou, Lecheng Zheng, Dongqi Fu, Jiawei Han, and Jingrui He · 2022
Later among the works it cites.
Clip2scene: Towards label-efficient 3d scene understanding by clip
Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, and Wenping Wang · 2023
Later among the works it cites.
Noise-robust graph learning by estimating and leveraging pairwise interactions
Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Yixuan Li, and Junzhou Huang · 2023
Later among the works it cites.
Curriculum graph machine learning: A survey
Haoyang Li, Xin Wang, and Wenwu Zhu · 2023
Later among the works it cites.
Sam-guided unsupervised domain adaptation for 3d segmentation
Xidong Peng, Runnan Chen, Feng Qiao, Lingdong Kong, Youquan Liu, Tai Wang, Xinge Zhu, and Yuexin Ma · 2023
Later among the works it cites.
Robust training of graph neural networks via noise governance
Siyi Qian, Haochao Ying, Renjun Hu, Jingbo Zhou, Jintai Chen, Danny Z Chen, and Jian Wu · 2023
Later among the works it cites.
Clnode: Curriculum learning for node classification
Xiaowen Wei, Xiuwen Gong, Yibing Zhan, Bo Du, Yong Luo, and Wenbin Hu · 2023
Later among the works it cites.
Making binary classification from multiple unlabeled datasets almost free of supervision
Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, and Tongliang Liu · 2023
Later among the works it cites.
Gnn cleaner: Label cleaner for graph structured data
Jun Xia, Haitao Lin, Yongjie Xu, Cheng Tan, Lirong Wu, Siyuan Li, and Stan Z Li · 2023
Later among the works it cites.
Relational curriculum learning for graph neural networks, 2023
Zheng Zhang, Junxiang Wang, and Liang Zhao · 2023
Later among the works it cites.
Enhancing contrastive learning for ordinal regression via ordinal content preserved data augmentation
Jiyang Zheng, Yu Yao, Bo Han, Dadong Wang, and Tongliang Liu · 2023
Later among the works it cites.
Towards label-free scene understanding by vision foundation models
Runnan Chen, Youquan Liu, Lingdong Kong, Nenglun Chen, Xinge Zhu, Yuexin Ma, Tongliang Liu, and Wenping Wang · 2024
Closest in time.
Instant: Semi-supervised learning with instance-dependent thresholds
Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang, Bo Han, and Tongliang Liu · 2024
Closest in time.
Unraveling the impact of heterophilic structures on graph positive-unlabeled learning, 2024
Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, and Tongliang Liu · 2024
Closest in time.
Early stopping against label noise without validation data
Suqin Yuan, Lei Feng, and Tongliang Liu · 2024
Closest in time.