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Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have attracted growing interests given their simplicity and effectiveness.
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 Mark Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad · 2008
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Arnetminer: extraction and mining of academic social networks
Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su · 2008
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Fast incremental and personalized pagerank
Bahman Bahmani, Abdur Chowdhury, and Ashish Goel · 2010
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Leveraging social media networks for classification
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Neural word embedding as implicit matrix factorization
Omer Levy and Yoav Goldberg · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
Cited alongside, same era.
Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Cited alongside, same era.
Variational graph auto-encoders
Thomas Kipf and Max Welling · 2016
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Data poisoning attacks on factorization-based collaborative filtering
Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik · 2016
Houdini: Fooling deep structured prediction models
Moustapha Cisse, Yossi Adi, Natalia Neverova, and Joseph Keshet · 2017
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
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Semi-supervised classification with graph convolutional networks
Thomas Kipf and Max Welling · 2017
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Adversarial examples for semantic segmentation and object detection
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, and Alan Yuille · 2017
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Seq2sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples
Minhao Cheng, Jinfeng Yi, Huan Zhang, Pin-Yu Chen, and Cho-Jui Hsieh · 2018
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Visualizing large-scale and high-dimensional data
Jian Tang, Jingzhou Liu, Ming Zhang, and Qiaozhu Mei · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
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Characterizing adversarial examples based on spatial consistency information for semantic segmentation
Chaowei Xiao, Ruizhi Deng, Bo Li, Fisher Yu, Dawn Song, et al
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Generating adversarial examples with adversarial networks
Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu, and Dawn Song
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Spatially transformed adversarial examples
Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, and Dawn Song
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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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Network embedding as matrix factorization
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang · 2018
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Realistic adversarial examples in 3d meshes
Dawei Yang, Chaowei Xiao, Bo Li, Jia Deng, and Mingyan Liu · 2018
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Adversarial attacks on neural networks for graph data
Daniel Zugner, Amir Akbarnejad, and Stephan Gunnemann · 2018
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