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Deep graph learning (DGL) has achieved remarkable progress in both business and scientific areas ranging from finance and e-commerce to drug and advanced material discovery.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Fastgcn: Fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
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Adversarial attack and defense on graph data: A survey
Lichao Sun, Ji Wang, Philip S. Yu, and Bo Li · 2018
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Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
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Learning graph neural networks with noisy labels
Hoang NT, Choong Jun Jin, and Tsuyoshi Murata · 2019
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Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2019
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Learning robust representations with graph denoising policy network
Lu Wang, Wenchao Yu, Wei Wang, Wei Cheng, Wei Zhang, Hongyuan Zha, Xiaofeng He, and Haifeng Chen · 2019
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Adversarial examples for graph data: Deep insights into attack and defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu · 2019
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Topology attack and defense for graph neural networks: An optimization perspective
Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin · 2019
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Certifiable robustness and robust training for graph convolutional networks
Daniel Zügner and Stephan Günnemann · 2019
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A restricted black-box adversarial framework towards attacking graph embedding models
Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, and Junzhou Huang · 2020
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A survey of adversarial learning on graphs
Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, and Zibin Zheng · 2020
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Iterative deep graph learning for graph neural networks: Better and robust node embeddings
Yu Chen, Lingfei Wu, and Mohammed Zaki · 2020
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A targeted universal attack on graph convolutional network
Jiazhu Dai, Weifeng Zhu, and Xiangfeng Luo · 2020
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Enhancing graph neural network-based fraud detectors against camouflaged fraudsters
Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, and Philip S. Yu · 2020
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Bayesian graph neural networks with adaptive connection sampling
Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, and Xiaoning Qian · 2020
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Graph structure learning for robust graph neural networks
Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang · 2020
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FLAG: adversarial data augmentation for graph neural networks
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, and Tom Goldstein · 2020
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Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach
Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, and Vasant G. Honavar · 2020
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Scalable attack on graph data by injecting vicious nodes
Jihong Wang, Minnan Luo, Fnu Suya, Jundong Li, Zijiang Yang, and Qinghua Zheng · 2020
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Robust graph representation learning via neural sparsification
Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, and Wei Wang · 2020
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Size-invariant graph representations for graph classification extrapolations
Beatrice Bevilacqua, Yangze Zhou, and Bruno Ribeiro · 2021
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Understanding structural vulnerability in graph convolutional networks
Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, and Carl Yang · 2021
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Nrgnn: Learning a label noise-resistant graph neural network on sparsely and noisily labeled graphs
Enyan Dai, Charu Aggarwal, and Suhang Wang · 2021
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Graph adversarial training: Dynamically regularizing based on graph structure
Fuli Feng, Xiangnan He, Jie Tang, and Tat-Seng Chua · 2021
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Robustness of graph neural networks at scale
Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, and Stephan Günnemann · 2021
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Dockstream: a docking wrapper to enhance de novo molecular design
Graph universal adversarial attacks: A few bad actors ruin graph learning models
Xiao Zang, Yi Xie, Jie Chen, and Bo Yuan · 2021
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A comparative study on robust graph neural networks to structural noises
Zeyu Zhang and Yulong Pei · 2021
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Backdoor attacks to graph neural networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong · 2021
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Expressive 1-lipschitz neural networks for robust multiple graph learning against adversarial attacks
Xin Zhao, Zeru Zhang, Zijie Zhang, Lingfei Wu, Jiayin Jin, Yang Zhou, Ruoming Jin, Dejing Dou, and Da Yan · 2021
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Domain generalization: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2021
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Jeff Guo, Jon Paul Janet, Matthias Bauer, Eva Nittinger, Kathryn Giblin, Kostas Papadopoulos, Alexey Voronov, Atanas Patronov, Ola Engkvist, and Christian Margreitter · 2021
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Reliable graph neural networks for drug discovery under distributional shift
Kehang Han, Balaji Lakshminarayanan, and Jeremiah Zhe Liu · 2021
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Adversarial attack on large scale graph
Jintang Li, Tao Xie, Chen Liang, Fenfang Xie, Xiangnan He, and Zibin Zheng · 2021
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Unified robust training for graph neural networks against label noise
Yayong Li, Jie Yin, and Ling Chen · 2021
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Graph neural networks with adaptive residual
Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, and Jiliang Tang · 2021
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Elastic graph neural networks
Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, and Jiliang Tang · 2021
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Learning to drop: Robust graph neural network via topological denoising
Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, and Xiang Zhang · 2021
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Shift-robust GNNs: Overcoming the limitations of localized graph training data
Qi Zhu, Natalia Ponomareva, Jiawei Han, and Bryan Perozzi · 2021
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Deep graph structure learning for robust representations: A survey
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Qiang Liu, Shu Wu, and Liang Wang · 2021
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TDGIA: effective injection attacks on graph neural networks
Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, and Jie Tang · 2021
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Neighboring backdoor attacks on graph convolutional network
Liang Chen, Qibiao Peng, Jintang Li, Yang Liu, Jiawei Chen, Yong Li, and Zibin Zheng · 2022
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Understanding and improving graph injection attack by promoting unnoticeability
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Learning causally invariant representations for out-of-distribution generalization on graphs
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Towards robust graph neural networks for noisy graphs with sparse labels
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G-mixup: Graph data augmentation for graph classification
Xiaotian Han, Zhimeng Jiang, Ninghao Liu, and Xia Hu · 2022
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Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren, Ding Xue, Houtim Lai, Shaoyong Xu, Jing Feng, Wei Liu, Ping Luo, Shuigeng Zhou, Junzhou Huang, Peilin Zhao, and Yatao Bian · 2022
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Fair node representation learning via adaptive data augmentation
Öykü Deniz Kose and Yanning Shen · 2022
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Out-of-distribution generalization on graphs: A survey
Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu · 2022
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Spectral adversarial training for robust graph neural network
Jintang Li, Jiaying Peng, Liang Chen, Zibin Zheng, Tingting Liang, and Qing Ling · 2022
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Are defenses for graph neural networks robust?
Felix Mujkanovic, Simon Geisler, Stephan Günnemann, and Aleksandar Bojchevski · 2022
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On the unreasonable effectiveness of feature propagation in learning on graphs with missing node features
Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein · 2022
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Towards distribution shift of node-level prediction on graphs: An invariance perspective
Qitian Wu, Hengrui Zhang, Junchi Yan, and David Wipf · 2022
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Unsupervised graph poisoning attack via contrastive loss back-propagation
Sixiao Zhang, Hongxu Chen, Xiangguo Sun, Yicong Li, and Guandong Xu · 2022
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Empowering graph representation learning with test-time graph transformation
Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, and Neil Shah · 2023
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