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Backdoor attack is a powerful attack algorithm to deep learning model.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
Asim, Kumar, Debnath, Rosa, L., Lopez, de, Compadre, Gargi, and Debnath and · 1991
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Early stopping-but when?
Lutz Prechelt · 1998
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Graph kernels for disease outcome prediction from protein-protein interaction networks
Karsten M Borgwardt, Hans Peter Kriegel, S V N Vishwanathan, and Nicol N Schraudolph · 2007
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Graph kernels based on tree patterns for molecules
Mahe, Vert, and JP · 2009
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Graph wavelet alignment kernels for drug virtual screening
A. Smalter, J. Huan, and G. Lushington · 2009
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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The network data repository with interactive graph analytics and visualization
Ryan Rossi and Nesreen Ahmed · 2015
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Detecting adversarial samples from artifacts
R. Feinman, R. R. Curtin, S. Shintre, and A. B. Gardner · 2017
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Protein interface prediction using graph convolutional networks
A. Fout, J. Byrd, B. Shariat, and A. Ben-Hur · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
T. Gu, B. Dolan-Gavitt, and S. Garg · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
T. Gu, B. Dolan-Gavitt, and S. Garg · 2017
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Generating adversarial malware examples for black-box attacks based on gan
W. Hu and Y. Tan · 2017
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Trojaning attack on neural networks
Y. Liu, S. Ma, Y. Aafer, W. C. Lee, and X. Zhang · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Efficient defenses against adversarial attacks
V. Zantedeschi, M. I. Nicolae, and A. Rawat · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
Adversarial attack on graph structured data
H. Dai, H. Li, T. Tian, X. Huang, L. Wang, J. Zhu, and L. Song · 2018
Cited alongside, same era.
Hu-fu: Hardware and software collaborative attack framework against neural networks
W. Li, J. Yu, X. Ning, P. Wang, Q. Wei, Y. Wang, and H. Yang · 2018
Cited alongside, same era.
Weisfeiler and leman go neural: Higher-order graph neural networks
C. Morris, M. Ritzert, M. Fey, William L Hamilton, J. E. Lenssen, G. Rattan, and M. Grohe · 2018
Cited alongside, same era.
Spectral signatures in backdoor attacks
B. Tran, J. Li, and A. Madry · 2018
Hard masking for explaining graph neural networks
Thorben Funke, Megha Khosla, and Avishek Anand · 2020
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Parameterized explainer for graph neural network
D. Luo, W. Cheng, D. Xu, W. Yu, and X. Zhang · 2020
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Interpreting graph neural networks for nlp with differentiable edge masking
Michael Sejr Schlichtkrull, Nicola De Cao, and Ivan Titov · 2020
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Higher-order explanations of graph neural networks via relevant walks
Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T Schütt, Klaus-Robert Müller, and Grégoire Montavon · 2020
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Pgm-explainer: Probabilistic graphical model explanations for graph neural networks
Minh Vu and My T Thai · 2020
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Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 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.
Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
Cited alongside, same era.
Strip: A defence against trojan attacks on deep neural networks
Y. Gao, C. Xu, D. Wang, S. Chen, Damith C Ranasinghe, and S. Nepal · 2019
Cited alongside, same era.
A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
Cited alongside, same era.
Explainability methods for graph convolutional neural networks
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann · 2019
Cited alongside, same era.
Causal screening to interpret graph neural networks
Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, and Tat-seng Chua · 2020
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Graph backdoor
Z. Xi, R. Pang, S. Ji, and T. Wang · 2020
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Xgnn: Towards model-level explanations of graph neural networks
H. Yuan, J. Tang, X. Hu, and S. Ji · 2020
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Ml-doctor: Holistic risk assessment of inference attacks against machine learning models
Y. Liu, R. Wen, X. He, A. Salem, Z. Zhang, M. Backes, E De Cristofaro, M. Fritz, and Y. Zhang · 2021
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Explainability-based backdoor attacks against graph neural networks
Jing Xu, Minhui Xue, and Stjepan Picek · 2021
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Relex: A model-agnostic relational model explainer
Yue Zhang, David Defazio, and Arti Ramesh · 2021
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Backdoor attacks to graph neural networks
Z. Zhang, J. Jia, B. Wang, and N. Z. Gong · 2021
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Graphlime: Local interpretable model explanations for graph neural networks
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, and Yi Chang · 2022
Closest in time.
Graphlime: Local interpretable model explanations for graph neural networks
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, and Yi Chang · 2022
Closest in time.