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CiteSeer: An Automatic Citation Indexing System
C. Lee Giles, Kurt D. Bollacker, and Steve Lawrence · 1998
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Collective Classification in Network Data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad · 2008
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Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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Adversarial Machine Learning
Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, and J. D. Tygar · 2011
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DeepWalk: Online Learning of Social Representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Image-Based Recommendations on Styles and Substitutes
Julian J. McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel · 2015
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node2vec: Scalable Feature Learning for Networks
Aditya Grover and Jure Leskovec · 2016
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Molecular Graph Convolutions: Moving Beyond Fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley · 2016
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Tri-Party Deep Network Representation
Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Yang Wang · 2016
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Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Stealing Machine Learning Models via Prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
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Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini and David Wagner · 2017
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Inductive Representation Learning on Large Graphs
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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Practical Black-Box Attacks Against Machine Learning
Nicolas Papernot, Patrick D. McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
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Social Network Analysis
John Scott · 2017
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Membership Inference Attacks Against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
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Graph Convolutional Matrix Completion
Rianne van den Berg, Thomas N. Kipf, and Max Welling · 2017
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Adversarial Attack on Graph Structured Data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song · 2018
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Graph embedding techniques, applications, and performance: A survey
Palash Goyal and Emilio Ferrara · 2018
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Towards Reverse-Engineering Black-Box Neural Networks
Seong Joon Oh, Max Augustin, Bernt Schiele, and Mario Fritz · 2018
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Pitfalls of Graph Neural Network Evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Adversarial Attack and Defense on Graph Data: A Survey
Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Lifang He, and Bo Li · 2018
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Data Poisoning Attack against Unsupervised Node Embedding Methods
Mingjie Sun, Jian Tang, Huichen Li, Bo Li, Chaowei Xiao, Yao Chen, and Dawn Song · 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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Stealing Hyperparameters in Machine Learning
Binghui Wang and Neil Zhenqiang Gong · 2018
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Hierarchical Graph Representation Learning with Differentiable Pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec · 2018
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Graph Neural Networks: A Review of Methods and Applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun · 2018
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Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, and Jure Leskovec · 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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Adversarial Attacks on Node Embeddings via Graph Poisoning
Aleksandar Bojchevski and Stephan Günnemann · 2019
Cited alongside, same era.
Certifiable Robustness to Graph Perturbations
Aleksandar Bojchevski and Stephan Günnemann · 2019
Cited alongside, same era.
Adversarial Training Methods for Network Embedding
Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, and Dan Wang · 2019
Cited alongside, same era.
Adversarial Model Extraction on Graph Neural Networks
David DeFazio and Arti Ramesh · 2019
Cited alongside, same era.
Batch Virtual Adversarial Training for Graph Convolutional Networks
Zhijie Deng, Yinpeng Dong, and Jun Zhu · 2019
Cited alongside, same era.
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
Yu Chen, Lingfei Wu, and Mohammed J. Zaki · 2020
Later among the works it cites.
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, and Chang-Tien Lu · 2020
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Quantifying Privacy Leakage in Graph Embedding
Vasisht Duddu, Antoine Boutet, and Virat Shejwalkar · 2020
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All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs
Negin Entezari, Saba A. Al-Sayouri, Amirali Darvishzadeh, and Evangelos E. Papalexakis · 2020
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Strategies for Pre-training Graph Neural Networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
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Fuli Feng, Xiangnan He, Jie Tang, and Tat-Seng Chua · 2019
Cited alongside, same era.
GraphSAC: Detecting Anomalies in Large-scale Graphs
Vassilis N. Ioannidis, Dimitris Berberidis, and Georgios B. Giannakis · 2019
Cited alongside, same era.
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong · 2019
Cited alongside, same era.
PRADA: Protecting Against DNN Model Stealing Attacks
Mika Juuti, Sebastian Szyller, Samuel Marchal, and N. Asokan · 2019
Cited alongside, same era.
Attention Models in Graphs: A Survey
John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, and Eunyee Koh · 2019
Cited alongside, same era.
Self-Attention Graph Pooling
Junhyun Lee, Inyeop Lee, and Jaewoo Kang · 2019
Cited alongside, same era.
Defending Against Neural Network Model Stealing Attacks Using Deceptive Perturbations
Taesung Lee, Benjamin Edwards, Ian Molloy, and Dong Su · 2019
Cited alongside, same era.
Later among the works it cites.
GNNVis: Visualize Large-Scale Data by Learning a Graph Neural Network Representation
Yajun Huang, Jingbin Zhang, Yiyang Yang, Zhiguo Gong, and Zhifeng Hao · 2020
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High Accuracy and High Fidelity Extraction of Neural Networks
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, and Nicolas Papernot · 2020
Later among the works it cites.
Latent Adversarial Training of Graph Convolution Networks
Hongwei Jin and Xinhua Zhang · 2020
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Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study
Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, and Jiliang Tang · 2020
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Thieves on Sesame Street! Model Extraction of BERT-based APIs
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, and Mohit Iyyer · 2020
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Adversarial Attacks on Link Prediction Algorithms Based on Graph Neural Networks
Wanyu Lin, Shengxiang Ji, and Baochun Li · 2020
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Towards More Practical Adversarial Attacks on Graph Neural Networks
Jiaqi Ma, Shuangrui Ding, and Qiaozhu Mei · 2020
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Self-Supervised Graph Transformer on Large-Scale Molecular Data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
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Non-target-specific Node Injection Attacks on Graph Neural Networks: A Hierarchical Reinforcement Learning Approach
Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, and Vasant Honavar · 2020
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Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization
Bang Wu, Xiangwen Yang, Shirui Pan, and Xingliang Yuan · 2020
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Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation
Le Wu, Yonghui Yang, Lei Chen, Defu Lian, Richang Hong, and Meng Wang · 2020
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A Comprehensive Survey on Graph Neural Networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 2020
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Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Han Xu, Yao Ma, Haochen Liu, Debayan Deb, Hui Liu, Jiliang Tang, and Anil K. Jain · 2020
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Backdoor Attacks to Graph Neural Networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong · 2020
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Deep Learning on Graphs: A Survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2020
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Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
Qi Zhu, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han, and Carl Yang · 2020
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Label-Only Membership Inference Attacks
Christopher A. Choquette Choo, Florian Tramèr, Nicholas Carlini, and Nicolas Papernot · 2021
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FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, and Salman Avestimehr · 2021
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Stealing Links from Graph Neural Networks
Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang · 2021
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Node-Level Membership Inference Attacks Against Graph Neural Networks
Xinlei He, Rui Wen, Yixin Wu, Michael Backes, Yun Shen, and Yang Zhang · 2021
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Quantifying and Mitigating Privacy Risks of Contrastive Learning
Xinlei He and Yang Zhang · 2021
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Membership Leakage in Label-Only Exposures
Zheng Li and Yang Zhang · 2021
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Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection
Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, and Qing He · 2021
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Graph Backdoor
Zhaohan Xi, Ren Pang, Shouling Ji, and Ting Wang · 2021
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Interpreting and Unifying Graph Neural Networks with An Optimization Framework
Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, and Peng Cui · 2021
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ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models
Yugeng Liu, Rui Wen, Xinlei He, Ahmed Salem, Zhikun Zhang, Michael Backes, Emiliano De Cristofaro, Mario Fritz, and Yang Zhang · 2022
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Inference Attacks Against Graph Neural Networks
Zhikun Zhang, Min Chen, Michael Backes, Yun Shen, and Yang Zhang · 2022
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