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Deep neural networks (DNNs) have achieved significant performance in various tasks.
Birds of a feather: Homophily in social networks
M. McPherson, L. Smith-Lovin, and J. M. Cook · 2001
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Learning from labeled and unlabeled data with label propagation
X. Zhu and Z. Ghahramani · 2002
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Scaling personalized web search
G. Jeh and J. Widom · 2003
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The political blogosphere and the 2004 us election: divided they blog
L. A. Adamic and N. Glance · 2005
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Collective classification in network data
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad · 2008
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Managing and mining graph data
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Translating embeddings for modeling multi-relational data
A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko · 2013
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Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Deepwalk: Online learning of social representations
B. Perozzi, R. Al-Rfou, and S. Skiena · 2014
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Learning entity and relation embeddings for knowledge graph completion
Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu · 2015
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Line: Large-scale information network embedding
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei · 2015
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
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Variational graph auto-encoders
T. N. Kipf and M. Welling · 2016
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Column networks for collective classification
T. Pham, T. Tran, D. Phung, and S. Venkatesh · 2016
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
A. Bojchevski and S. Günnemann · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
X. Chen, C. Liu, B. Li, K. Lu, and D. Song · 2017
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Practical attacks against graph-based clustering
Y. Chen, Y. Nadji, A. Kountouras, F. Monrose, R. Perdisci, M. Antonakakis, and N. Vasiloglou · 2017
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Representation learning on graphs: Methods and applications
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Label informed attributed network embedding
X. Huang, J. Li, and X. Hu · 2017
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
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Encoding sentences with graph convolutional networks for semantic role labeling
D. Marcheggiani and I. Titov · 2017
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2017
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Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al · 2018
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Adversarial attacks on node embeddings via graph poisoning
A. Bojchevski and S. Günnemann · 2018
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Link prediction adversarial attack
J. Chen, Z. Shi, Y. Wu, X. Xu, and H. Zheng · 2018
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Fast gradient attack on network embedding
J. Chen, Y. Wu, X. Xu, Y. Chen, H. Zheng, and Q. Xuan · 2018
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Adversarial attack on graph structured data
H. Dai, H. Li, T. Tian, X. Huang, L. Wang, J. Zhu, and L. Song · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
J. Klicpera, A. Bojchevski, and S. Günnemann · 2018
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Large-scale point cloud semantic segmentation with superpoint graphs
L. Landrieu and M. Simonovsky · 2018
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Drug similarity integration through attentive multi-view graph auto-encoders
T. Ma, C. Xiao, J. Zhou, and F. Wang · 2018
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Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec
J. Qiu, Y. Dong, H. Ma, J. Li, K. Wang, and J. Tang · 2018
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Adversarial attack and defense on graph data: A survey
L. Sun, J. Wang, P. S. Yu, and B. Li · 2018
Cited alongside, same era.
Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2018
Cited alongside, same era.
Adversarial examples for graph data: deep insights into attack and defense
H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. Zhu · 2019
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A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu · 2019
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Adversarial attacks and defenses in images, graphs and text: A review
H. Xu, Y. Ma, H. Liu, D. Deb, H. Liu, J. Tang, and A. Jain · 2019
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Topology attack and defense for graph neural networks: An optimization perspective
K. Xu, H. Chen, S. Liu, P.-Y. Chen, T.-W. Weng, M. Hong, and X. Lin · 2019
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Towards data poisoning attack against knowledge graph embedding
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M. Waniek, T. P. Michalak, M. J. Wooldridge, and T. Rahwan · 2018
Cited alongside, same era.
Hiding individuals and communities in a social network
M. Waniek, T. P. Michalak, M. J. Wooldridge, and T. Rahwan · 2018
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka · 2018
Cited alongside, same era.
Characterizing malicious edges targeting on graph neural networks
X. Xu, Y. Yu, B. Li, L. Song, C. Liu, and C. Gunter · 2018
Cited alongside, same era.
Graph neural networks: A review of methods and applications
J. Zhou, G. Cui, Z. Zhang, C. Yang, Z. Liu, and M. Sun · 2018
Cited alongside, same era.
Adversarial attacks on neural networks for graph data
D. Zügner, A. Akbarnejad, and S. Günnemann · 2018
Cited alongside, same era.
Certifiable robustness to graph perturbations
A. Bojchevski and S. Günnemann · 2019
Cited alongside, same era.
H. Zhang, T. Zheng, J. Gao, C. Miao, L. Su, Y. Li, and K. Ren · 2019
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Admiring: Adversarial multi-network mining
Q. Zhou, L. Li, N. Cao, L. Ying, and H. Tong · 2019
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Robust graph convolutional networks against adversarial attacks
D. Zhu, Z. Zhang, P. Cui, and W. Zhu · 2019
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Adversarial attacks on graph neural networks via meta learning
D. Zügner and S. Günnemann · 2019
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Certifiable robustness and robust training for graph convolutional networks
D. Zügner and S. Günnemann · 2019
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A. Bojchevski, J. Klicpera, and S. Günnemann · 2020
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Robust spammer detection by nash reinforcement learning
Y. Dou, G. Ma, P. S. Yu, and S. Xie · 2020
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All you need is low (rank) defending against adversarial attacks on graphs
N. Entezari, S. A. Al-Sayouri, A. Darvishzadeh, and E. E. Papalexakis · 2020
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J. Jia, B. Wang, X. Cao, and N. Z. Gong · 2020
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Graph structure learning for robust graph neural networks
W. Jin, Y. Ma, X. Liu, X. Tang, S. Wang, and J. Tang · 2020
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Adversarial attack on community detection by hiding individuals, 2020
J. Li, H. Zhang, Z. Han, Y. Rong, H. Cheng, and J. Huang · 2020
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Learning transferable adversarial examples via ghost networks
Y. Li, S. Bai, Y. Zhou, C. Xie, Z. Zhang, and A. L. Yuille · 2020
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Deeprobust: A pytorch library for adversarial attacks and defenses
Y. Li, W. Jin, H. Xu, and J. Tang · 2020
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Black-box adversarial attacks on graph neural networks with limited node access
J. Ma, S. Ding, and Q. Mei · 2020
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Transferring robustness for graph neural network against poisoning attacks
X. Tang, Y. Li, Y. Sun, H. Yao, P. Mitra, and S. Wang · 2020
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Adversarial immunization for improving certifiable robustness on graphs
S. Tao, H. Shen, Q. Cao, L. Hou, and X. Cheng · 2020
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Certified robustness of graph neural networks against adversarial structural perturbation
B. Wang, J. Jia, X. Cao, and N. Z. Gong · 2020
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Scalable attack on graph data by injecting vicious nodes
J. Wang, M. Luo, F. Suya, J. Li, Z. Yang, and Q. Zheng · 2020
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Z. Xi, R. Pang, S. Ji, and T. Wang · 2020
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Graph universal adversarial attacks: A few bad actors ruin graph learning models, 2020
X. Zang, Y. Xie, J. Chen, and B. Yuan · 2020
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Defensevgae: Defending against adversarial attacks on graph data via a variational graph autoencoder
A. Zhang and J. Ma · 2020
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Gnnguard: Defending graph neural networks against adversarial attacks
X. Zhang and M. Zitnik · 2020
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Backdoor attacks to graph neural networks
Z. Zhang, J. Jia, B. Wang, and N. Z. Gong · 2020
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Data poisoning attacks on graph convolutional matrix completion
Q. Zhou, Y. Ren, T. Xia, L. Yuan, and L. Chen · 2020
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Adversarial attacks on graph neural networks: Perturbations and their patterns
D. Zügner, O. Borchert, A. Akbarnejad, and S. Guennemann · 2020
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Certifiable robustness of graph convolutional networks under structure perturbations
D. Zügner and S. Günnemann · 2020
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