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Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data.
Control flow analysis
Frances E Allen · 1970
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Shapley value
Sergiu Hart · 1989
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Random graph models of social networks
Mark EJ Newman, Duncan J Watts, and Steven H Strogatz · 2002
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The political blogosphere and the 2004 us election: divided they blog
Lada A Adamic and Natalie Glance · 2005
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Node classification in social networks
Smriti Bhagat, Graham Cormode, and S Muthukrishnan · 2011
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
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Quantitative analysis of the full bitcoin transaction graph
Dorit Ron and Adi Shamir · 2013
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Node classification in social network via a factor graph model
Huan Xu, Yujiu Yang, Liangwei Wang, and Wenhuang Liu · 2013
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Explaining and harnessing adversarial examples
Christian Szegedy Ian J. Goodfellow, Jonathon Shlens · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Deep convolutional networks on graph-structured data
Mikael Henaff, Joan Bruna, and Yann LeCun · 2015
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Deep neural networks for learning graph representations
Shaosheng Cao, Wei Lu, and Qiongkai Xu · 2016
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Graph based convolutional neural network
Michael Edwards and Xianghua Xie · 2016
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An unexpected unity among methods for interpreting model predictions
Scott Lundberg and Su-In Lee · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Defending against adversarial images using basis functions transformations
Uri Shaham, James Garritano, Yutaro Yamada, Ethan Weinberger, Alex Cloninger, Xiuyuan Cheng, Kelly Stanton, and Yuval Kluger · 2018
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Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Attack graph convolutional networks by adding fake nodes
Xiaoyun Wang, Joe Eaton, Cho-Jui Hsieh, and Felix Wu · 2018
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Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2018
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Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Deep gaussian embedding of attributed graphs: Unsupervised inductive learning via ranking
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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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Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Nicolas Papernot and Patrick McDaniel · 2018
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Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 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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Simplifying graph convolutional networks
Felix Wu, Tianyi Zhang, Amauri Holanda Souza Jr., Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
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Adversarial examples: Attacks and defenses for deep learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li · 2019
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