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Graph Neural Networks (GNNs) are increasingly important given their popularity and the diversity of applications.
The notion of breakdown point
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Automating the construction of internet portals with machine learning
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Collective classification in network data
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Efficiency of coordinate descent methods on huge-scale optimization problems
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Coordinate Descent Algorithms
S. J. Wright · 2015
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Training Deep Nets with Sublinear Memory Cost
T. Chen, B. Xu, C. Zhang, and C. Guestrin · 2016
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Towards Evaluating the Robustness of Neural Networks
N. Carlini and D. Wagner · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Efficiency of accelerated coordinate descent method on structured optimization problems
Y. Nesterov and S. Stich · 2017
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Deep Gaussian embedding of graphs: Unsupervised inductive learning via ranking
A. Bojchevski and S. Günnemann · 2018
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Adversarial attack on graph structured data
H. Dai, H. Li, T. Tian, H. Xin, L. Wang, Z. Jun, and S. Le · 2018
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Hiding individuals and communities in a social network
M. Waniek, T. P. Michalak, M. J. Wooldridge, and T. Rahwan · 2018
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Towards Fast Computation of Certified Robustness for ReLU Networks
L. Weng, H. Zhang, H. Chen, Z. Song, C.-J. Hsieh, L. Daniel, D. Boning, and I. Dhillon · 2018
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Graph convolutional neural networks for web-scale recommender systems
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Efficient Neural Network Robustness Certification with General Activation Functions
H. Zhang, T.-W. Weng, P.-Y. Chen, C.-J. Hsieh, and L. Daniel · 2018
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Adversarial attacks on neural networks for graph data
D. Zügner, A. Akbarnejad, and S. Günnemann · 2018
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Provably robust boosted decision stumps and trees against adversarial attacks
M. Andriushchenko and M. Hein · 2019
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Certifiable Robustness to Graph Perturbations
A. Bojchevski and S. Günnemann · 2019
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Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks
W. L. Chiang, Y. Li, X. Liu, S. Bengio, S. Si, and C. J. Hsieh · 2019
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Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering
Reliable Graph Neural Networks via Robust Aggregation
S. Geisler, D. Zügner, and S. Günnemann · 2020
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Open Graph Benchmark: Datasets for Machine Learning on Graphs
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec · 2020
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Towards More Practical Adversarial Attacks on Graph Neural Networks
J. Ma, S. Ding, and Q. Mei · 2020
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SoftSort: A Continuous Relaxation for the argsort Operator
S. Prillo and J. Martin Eisenschlos · 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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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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M. Jin, H. Chang, W. Zhu, and S. Sojoudi · 2019
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Diffusion Improves Graph Learning
J. Klicpera, S. Weißenberger, and S. Günnemann · 2019
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Pitfalls of Graph Neural Network Evaluation
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann · 2019
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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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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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Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification
Y. Zhang, S. Pal, M. Coates, and D. Ustebay · 2019
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Robust graph convolutional networks against adversarial attacks
D. Zhu, P. Cui, Z. Zhang, and W. Zhu · 2019
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Graph Information Bottleneck
T. Wu, H. Ren, P. Li, and J. Leskovec · 2020
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GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
X. Zhang and M. Zitnik · 2020
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Understanding Structural Vulnerability in Graph Convolutional Networks
L. Chen, J. Li, Q. Peng, Y. Liu, Z. Zheng, and C. Yang · 2021
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Attacking Graph Neural Networks at Scale
S. Geisler, D. Zügner, A. Bojchevski, and S. Günnemann · 2021
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Graph neural networks: Adversarial robustness
S. Günnemann · 2021
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Node Similarity Preserving Graph Convolutional Networks
W. Jin, T. Derr, Y. Wang, Y. Ma, Z. Liu, and J. Tang · 2021
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Adversarial attack on large scale graph
J. Li, T. Xie, L. Chen, F. Xie, X. He, and Z. Zheng · 2021
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Collective robustness certificates
J. Schuchardt, A. Bojchevski, J. Klicpera, and S. Günnemann · 2021
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