Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured data.
Label-only membership inference attacks. In ICML . PMLR, 1964–1974
Christopher A Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot. 2021 · 1974
Earlier work this paper cites.
The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek. 2000 · 2000
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. 2011 · 2011
Earlier work this paper cites.
ZINC: a free tool to discover chemistry for biology
John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, and Ryan G Coleman. 2012 · 2012
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML , Vol. 3. 896
Dong-Hyun Lee et al · 2013
Earlier work this paper cites.
Privacy-preserving deep learning. In CCS . 1310–1321
Reza Shokri and Vitaly Shmatikov. 2015 · 2015
Earlier work this paper cites.
Deep learning with differential privacy. In CCS . 308–318
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Earlier work this paper cites.
Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. 2016 · 2016
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar. 2016 · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs. In NeurIPS . 1024–1034
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Logan: Membership inference attacks against generative models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro. 2017 · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP) . IEEE, 3–18
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
Earlier work this paper cites.
Adversarial attack on graph structured data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. 2018 · 2018
Earlier work this paper cites.
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
Machine learning with membership privacy using adversarial regularization. In CCS . 634–646
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2018 · 2018
Earlier work this paper cites.
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes. 2018 · 2018
Earlier work this paper cites.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems. In SIGKDD . 974–983
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Cited alongside, same era.
Adversarial attacks on neural networks for graph data. In SIGKDD . 2847–2856
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann. 2018 · 2018
Cited alongside, same era.
A Semi-supervised Graph Attentive Network for Financial Fraud Detection. In ICDM . IEEE, 598–607
Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, and Yuan Qi. 2019 · 2019
Cited alongside, same era.
Adversarial examples on graph data: Deep insights into attack and defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu. 2019b · 2019
Cited alongside, same era.
Molecular generative Graph Neural Networks for Drug Discovery
Pietro Bongini, Monica Bianchini, and Franco Scarselli. 2021 · 2021
Later among the works it cites.
Understanding structural vulnerability in graph convolutional networks
Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, and Carl Yang. 2021 · 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 · 2021
Later among the works it cites.
Robustness of graph neural networks at scale
Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, and Stephan Günnemann. 2021 · 2021
Later among the works it cites.
Node-level membership inference attacks against graph neural networks
Xinlei He, Rui Wen, Yixin Wu, Michael Backes, Yun Shen, and Yang Zhang. 2021 · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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 · 2019
Cited alongside, same era.
Robust graph convolutional networks against adversarial attacks. In SIGKDD . 1399–1407
Dingyuan Zhu, Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2019 · 2019
Cited alongside, same era.
All You Need Is Low (Rank) Defending Against Adversarial Attacks on Graphs. In WSDM . 169–177
Negin Entezari, Saba A Al-Sayouri, Amirali Darvishzadeh, and Evangelos E Papalexakis. 2020 · 2020
Cited alongside, same era.
Graph structure learning for robust graph neural networks. In SIGKDD . 66–74
Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. 2020 · 2020
Cited alongside, same era.
Gcc: Graph contrastive coding for graph neural network pre-training. In SIGKDD . 1150–1160
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020 · 2020
Cited alongside, same era.
Transferring Robustness for Graph Neural Network Against Poisoning Attacks. In WSDM . 600–608
Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, and Suhang Wang. 2020 · 2020
Cited alongside, same era.
Graph information bottleneck
Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Distilling robust and non-robust features in adversarial examples by information bottleneck
Junho Kim, Byung-Kwan Lee, and Yong Man Ro. 2021 · 2021
Later among the works it cites.
Elastic graph neural networks. In ICML . PMLR, 6837–6849
Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, and Jiliang Tang. 2021 · 2021
Later among the works it cites.
A weighted patient network-based framework for predicting chronic diseases using graph neural networks
Haohui Lu and Shahadat Uddin. 2021 · 2021
Later among the works it cites.
Membership inference attack on graph neural networks. In TPS-ISA . IEEE, 11–20
Iyiola E Olatunji, Wolfgang Nejdl, and Megha Khosla. 2021 · 2021
Later among the works it cites.
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness
Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, and Jennifer Dy. 2021 · 2021
Later among the works it cites.
Adapting membership inference attacks to gnn for graph classification: Approaches and implications. In ICDM . IEEE, 1421–1426
Bang Wu, Xiangwen Yang, Shirui Pan, and Xingliang Yuan. 2021 · 2021
Later among the works it cites.
Tdgia: Effective injection attacks on graph neural networks. In SIGKDD, pages=2461–2471, year=2021
Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, and Jie Tang. [n.d.] · 2021
Later among the works it cites.
Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. 2022b · 2022
Later among the works it cites.
Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN. In SIGKDD . 925–935
Kuan Li, Yang Liu, Xiang Ao, Jianfeng Chi, Jinghua Feng, Hao Yang, and Qing He. 2022 · 2022
Later among the works it cites.
Interpretable and generalizable graph learning via stochastic attention mechanism. In ICML . PMLR, 15524–15543
Siqi Miao, Mia Liu, and Pan Li. 2022 · 2022
Later among the works it cites.
Graph structure learning with variational information bottleneck. In AAAI , Vol. 36. 4165–4174
Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, and S Yu Philip. 2022 · 2022
Later among the works it cites.
Unnoticeable Backdoor Attacks on Graph Neural Networks. In WWW . 2263–2273
Enyan Dai, Minhua Lin, Xiang Zhang, and Suhang Wang. 2023 · 2023
Closest in time.