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Graph is an important data representation ubiquitously existing in the real world.
Computers and Intractability: A Guide to the Theory of NP-Completeness
M. R. Garey and David S. Johnson · 1979
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The Graph Neural Network Model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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Weisfeiler-Lehman Graph Kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt · 2011
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Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing
Matt Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart · 2014
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DeepWalk: Online Learning of Social Representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Can Deep Learning Revolutionize Mobile Sensing?
Nicholas D. Lane and Petko Georgiev · 2015
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LINE: Large-scale Information Network Embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
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Diffusion-Convolutional Neural Networks
James Atwood and Don Towsley · 2016
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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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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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You Are What Apps You Use: Demographic Prediction Based on User’s Apps
Eric Malmi and Ingmar Weber · 2016
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walk2friends: Inferring Social Links from Mobility Profiles
Michael Backes, Mathias Humbert, Jun Pang, and Yang Zhang · 2017
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Inductive Representation Learning on Large Graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
Yiping Kang, Johann Hauswald, Cao Gao, Austin Rovinski, Trevor Mudge, Jason Mars, and Lingjia Tang · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
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Column Networks for Collective Classification
Trang Pham, Truyen Tran, Dinh Q. Phung, and Svetha Venkatesh · 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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Machine Learning Models that Remember Too Much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov · 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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Knock Knock, Who’s There? Membership Inference on Aggregate Location Data
Apostolos Pyrgelis, Carmela Troncoso, and Emiliano De Cristofaro · 2018
Cited alongside, same era.
DeepInf: Social Influence Prediction with Deep Learning
Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang · 2018
Cited alongside, same era.
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Graph Attention Networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
Hierarchical Graph Representation Learning with Differentiable Pooling
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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A Fair Comparison of Graph Neural Networks for Graph Classification
Federico Errica, Marco Podda, Davide Bacciu, and Alessio Micheli · 2020
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Graph Representation Learning
William L. Hamilton · 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
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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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Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec · 2018
Cited alongside, same era.
Adversarial Attacks on Neural Networks for Graph Data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
Cited alongside, same era.
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.
On the equivalence between graph isomorphism testing and function approximation with GNNs
Zhengdao Chen, Soledad Villar, Lei Chen, and Joan Bruna · 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.
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Fuli Feng, Xiangnan He, Jie Tang, and Tat-Seng Chua · 2019
Cited alongside, same era.
Xiaoxiao Li, João Saúde, Prashant Reddy, and Manuela Veloso · 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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TUDataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, and Yang Zhang · 2020
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Information Leakage in Embedding Models
Congzheng Song and Ananth Raghunathan · 2020
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Overlearning Reveals Sensitive Attributes
Congzheng Song and Vitaly Shmatikov · 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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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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Towards Plausible Graph Anonymization
Yang Zhang, Mathias Humbert, Bartlomiej Surma, Praveen Manoharan, Jilles Vreeken, and Michael Backes · 2020
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Deep Learning on Graphs: A Survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2020
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When Machine Unlearning Jeopardizes Privacy
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang · 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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Graph Backdoor
Zhaohan Xi, Ren Pang, Shouling Ji, and Ting Wang · 2021
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PrivSyn: Differentially Private Data Synthesis
Zhikun Zhang, Tianhao Wang, Jean Honorio, Ninghui Li, Michael Backes, Shibo He, Jiming Chen, and Yang Zhang · 2021
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