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GraphMix: Improved Training of GNNs for Semi-Supervised Learning

GraphMix: Improved Training of GNNs for Semi-Supervised Learning

25 September 2019
Vikas Verma
Meng Qu
Kenji Kawaguchi
Alex Lamb
Yoshua Bengio
Juho Kannala
Jian Tang
ArXivPDFHTML

Papers citing "GraphMix: Improved Training of GNNs for Semi-Supervised Learning"

35 / 35 papers shown
Title
Half-Hop: A graph upsampling approach for slowing down message passing
Half-Hop: A graph upsampling approach for slowing down message passing
Mehdi Azabou
Venkataraman Ganesh
S. Thakoor
Chi-Heng Lin
Lakshmi Sathidevi
Ran Liu
Michal Valko
Petar Velickovic
Eva L. Dyer
16
19
0
17 Aug 2023
RegExplainer: Generating Explanations for Graph Neural Networks in
  Regression Task
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task
Jiaxing Zhang
Zhuomin Chen
Hao Mei
Dongsheng Luo
Hua Wei
31
8
0
15 Jul 2023
Boosting long-term forecasting performance for continuous-time dynamic graph networks via data augmentation
Yu Tian
Mingjie Zhu
Jiachi Luo
Song Li
13
0
0
12 Apr 2023
Synthetic Over-sampling for Imbalanced Node Classification with Graph
  Neural Networks
Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks
Tianxiang Zhao
Xiang Zhang
Suhang Wang
17
6
0
10 Jun 2022
GPN: A Joint Structural Learning Framework for Graph Neural Networks
GPN: A Joint Structural Learning Framework for Graph Neural Networks
Qianggang Ding
Deheng Ye
Tingyang Xu
P. Zhao
11
0
0
12 May 2022
LEReg: Empower Graph Neural Networks with Local Energy Regularization
LEReg: Empower Graph Neural Networks with Local Energy Regularization
Xiaojun Ma
Hanyue Chen
Guojie Song
8
3
0
20 Mar 2022
Model-Agnostic Augmentation for Accurate Graph Classification
Model-Agnostic Augmentation for Accurate Graph Classification
Jaemin Yoo
Sooyeon Shim
U. Kang
GNN
14
29
0
21 Feb 2022
Graph Data Augmentation for Graph Machine Learning: A Survey
Graph Data Augmentation for Graph Machine Learning: A Survey
Tong Zhao
Wei Jin
Yozen Liu
Yingheng Wang
Gang Liu
Stephan Günnemann
Neil Shah
Meng-Long Jiang
OOD
14
78
0
17 Feb 2022
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels
Yayong Li
Jie Yin
Ling-Hao Chen
11
32
0
20 Jan 2022
Bringing Your Own View: Graph Contrastive Learning without Prefabricated
  Data Augmentations
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations
Yuning You
Tianlong Chen
Zhangyang Wang
Yang Shen
SSL
11
59
0
04 Jan 2022
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
Jin Xu
Mingjian Chen
Jianqiang Huang
Xingyuan Tang
Ke Hu
Jian Li
Jia Cheng
Jun Lei
15
2
0
25 Nov 2021
Network representation learning: A macro and micro view
Network representation learning: A macro and micro view
Xueyi Liu
Jie Tang
GNN
AI4TS
17
23
0
21 Nov 2021
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure
  Preservation
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation
Joonhyung Park
Hajin Shim
Eunho Yang
79
48
0
10 Nov 2021
ifMixup: Interpolating Graph Pair to Regularize Graph Classification
ifMixup: Interpolating Graph Pair to Regularize Graph Classification
Hongyu Guo
Yongyi Mao
17
9
0
18 Oct 2021
Scalable Consistency Training for Graph Neural Networks via
  Self-Ensemble Self-Distillation
Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation
Cole Hawkins
V. Ioannidis
Soji Adeshina
George Karypis
GNN
SSL
21
2
0
12 Oct 2021
Self-Training with Differentiable Teacher
Self-Training with Differentiable Teacher
Simiao Zuo
Yue Yu
Chen Liang
Haoming Jiang
Siawpeng Er
Chao Zhang
T. Zhao
H. Zha
32
14
0
15 Sep 2021
Counterfactual Adversarial Learning with Representation Interpolation
Counterfactual Adversarial Learning with Representation Interpolation
Wen Wang
Boxin Wang
Ning Shi
Jinfeng Li
Bingyu Zhu
Xiangyu Liu
Rongxin Zhang
AAML
OOD
CML
6
2
0
10 Sep 2021
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive
  Benchmark Study
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study
Tianlong Chen
Kaixiong Zhou
Keyu Duan
Wenqing Zheng
Peihao Wang
Xia Hu
Zhangyang Wang
AAML
GNN
19
61
0
24 Aug 2021
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange
Liang Zeng
Jin Xu
Zijun Yao
Yanqiao Zhu
Jian Li
11
1
0
10 Jun 2021
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural
  Networks
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
Hao Peng
Ruitong Zhang
Yingtong Dou
Renyu Yang
Jingyi Zhang
Philip S. Yu
28
115
0
16 Apr 2021
On the Theory of Implicit Deep Learning: Global Convergence with
  Implicit Layers
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers
Kenji Kawaguchi
PINN
12
41
0
15 Feb 2021
Graph Neural Networks: Taxonomy, Advances and Trends
Graph Neural Networks: Taxonomy, Advances and Trends
Yu Zhou
Haixia Zheng
Xin Huang
Shufeng Hao
Dengao Li
Jumin Zhao
AI4TS
23
113
0
16 Dec 2020
Rethinking the Promotion Brought by Contrastive Learning to
  Semi-Supervised Node Classification
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification
Deli Chen
Yankai Lin
Lei Li
Xuancheng Ren
Peng Li
Jie Zhou
Xu Sun
24
5
0
14 Dec 2020
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning
  Attacks
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks
U. Shanthamallu
Jayaraman J. Thiagarajan
A. Spanias
AAML
11
16
0
30 Sep 2020
Rethinking Graph Regularization for Graph Neural Networks
Rethinking Graph Regularization for Graph Neural Networks
Han Yang
Kaili Ma
James Cheng
AI4CE
8
72
0
04 Sep 2020
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged
  Fraudsters
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
Yingtong Dou
Zhiwei Liu
Li Sun
Yutong Deng
Hao Peng
Philip S. Yu
AAML
11
446
0
19 Aug 2020
Understanding and Resolving Performance Degradation in Graph
  Convolutional Networks
Understanding and Resolving Performance Degradation in Graph Convolutional Networks
Kuangqi Zhou
Yanfei Dong
Kaixin Wang
W. Lee
Bryan Hooi
Huan Xu
Jiashi Feng
GNN
BDL
34
88
0
12 Jun 2020
Data Augmentation for Graph Neural Networks
Data Augmentation for Graph Neural Networks
Tong Zhao
Yozen Liu
Leonardo Neves
Oliver J. Woodford
Meng-Long Jiang
Neil Shah
GNN
8
404
0
11 Jun 2020
Interpolation-based semi-supervised learning for object detection
Interpolation-based semi-supervised learning for object detection
Jisoo Jeong
Vikas Verma
Minsung Hyun
Juho Kannala
Nojun Kwak
23
73
0
03 Jun 2020
Graph Random Neural Network for Semi-Supervised Learning on Graphs
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
Jie Tang
19
383
0
22 May 2020
MixText: Linguistically-Informed Interpolation of Hidden Space for
  Semi-Supervised Text Classification
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
Jiaao Chen
Zichao Yang
Diyi Yang
VLM
14
355
0
25 Apr 2020
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance
  and the Abstractions Learned by Deep Networks
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks
Alex Lamb
Sherjil Ozair
Vikas Verma
David R Ha
AAML
13
4
0
25 Dec 2019
Interpolated Adversarial Training: Achieving Robust Neural Networks
  without Sacrificing Too Much Accuracy
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy
Alex Lamb
Vikas Verma
Kenji Kawaguchi
Alexander Matyasko
Savya Khosla
Juho Kannala
Yoshua Bengio
AAML
24
98
0
16 Jun 2019
Representation Learning on Graphs with Jumping Knowledge Networks
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu
Chengtao Li
Yonglong Tian
Tomohiro Sonobe
Ken-ichi Kawarabayashi
Stefanie Jegelka
GNN
229
1,935
0
09 Jun 2018
Geometric deep learning on graphs and manifolds using mixture model CNNs
Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti
Davide Boscaini
Jonathan Masci
Emanuele Rodolà
Jan Svoboda
M. Bronstein
GNN
234
1,801
0
25 Nov 2016
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