research-article

Adversarial Attacks and Defenses on Graphs

Authors:

Charu Aggarwal,

Jiliang TangAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 22, Issue 2

Pages 19 - 34

https://doi.org/10.1145/3447556.3447566

Published: 17 January 2021 Publication History

Abstract

Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks.

References

[1]

L. A. Adamic and N. Glance. The political blogosphere and the 2004 us election: divided they blog. In Proceedings of the 3rd international workshop on Link discovery, pages 36--43, 2005.

Digital Library

[2]

C. C. Aggarwal, H. Wang, et al. Managing and mining graph data, volume 40. Springer, 2010.

[3]

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. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261, 2018.

[4]

A. Bojchevski and S. G¨unnemann. Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. arXiv preprint arXiv:1707.03815, 2017.

[5]

A. Bojchevski and S. G¨unnemann. Adversarial attacks on node embeddings via graph poisoning. arXiv preprint arXiv:1809.01093, 2018.

[6]

A. Bojchevski and S. G¨unnemann. Certifiable robustness to graph perturbations. In Advances in Neural Information Processing Systems, pages 8317--8328, 2019.

[7]

A. Bojchevski, J. Klicpera, and S. G¨unnemann. Efficient robustness certificates for discrete data: Sparsityaware randomized smoothing for graphs, images and more. arXiv preprint arXiv:2008.12952, 2020.

[8]

A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems, pages 2787--2795, 2013.

Digital Library

[9]

H. Chang, Y. Rong, T. Xu, W. Huang, H. Zhang, P. Cui, W. Zhu, and J. Huang. The general blackbox attack method for graph neural networks. arXiv preprint arXiv:1908.01297, 2019.

[10]

J. Chen, L. Chen, Y. Chen, M. Zhao, S. Yu, Q. Xuan, and X. Yang. Ga-based q-attack on community detection. IEEE Transactions on Computational Social Systems, 6(3):491--503, 2019.

[11]

J. Chen, Z. Shi, Y. Wu, X. Xu, and H. Zheng. Link prediction adversarial attack. arXiv preprint arXiv:1810.01110, 2018.

[12]

J. Chen, Y. Wu, X. Lin, and Q. Xuan. Can adversarial network attack be defended? arXiv preprint arXiv:1903.05994, 2019.

[13]

J. Chen, Y. Wu, X. Xu, Y. Chen, H. Zheng, and Q. Xuan. Fast gradient attack on network embedding. arXiv preprint arXiv:1809.02797, 2018.

[14]

X. Chen, C. Liu, B. Li, K. Lu, and D. Song. Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526, 2017.

[15]

Y. Chen, Y. Nadji, A. Kountouras, F. Monrose, R. Perdisci, M. Antonakakis, and N. Vasiloglou. Practical attacks against graph-based clustering. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pages 1125--1142, 2017.

Digital Library

[16]

J. M. Cohen, E. Rosenfeld, and J. Z. Kolter. Certified adversarial robustness via randomized smoothing. arXiv preprint arXiv:1902.02918, 2019.

[17]

H. Dai, H. Li, T. Tian, X. Huang, L. Wang, J. Zhu, and L. Song. Adversarial attack on graph structured data. arXiv preprint arXiv:1806.02371, 2018.

[18]

Q. Dai, X. Shen, L. Zhang, Q. Li, and D. Wang. Adversarial training methods for network embedding. In The World Wide Web Conference, pages 329--339, 2019.

Digital Library

[19]

Z. Deng, Y. Dong, and J. Zhu. Batch virtual adversarial training for graph convolutional networks. arXiv preprint arXiv:1902.09192, 2019.

[20]

Y. Dou, G. Ma, P. S. Yu, and S. Xie. Robust spammer detection by nash reinforcement learning. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 924--933, 2020.

Digital Library

[21]

N. Entezari, S. A. Al-Sayouri, A. Darvishzadeh, and E. E. Papalexakis. All you need is low (rank) defending against adversarial attacks on graphs. In Proceedings of the 13th International Conference on Web Search Mining, pages 169--177, 2020.

Digital Library

[22]

F. Feng, X. He, J. Tang, and T.-S. Chua. Graph adversarial training: Dynamically regularizing based on graph structure. IEEE Transactions on Knowledge and Data Engineering, 2019.

[23]

L. Franceschi, M. Niepert, M. Pontil, and X. He. Learning discrete structures for graph neural networks. arXiv preprint arXiv:1903.11960, 2019.

[24]

J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl. Neural message passing for quantum chemistry. arXiv preprint arXiv:1704.01212, 2017.

[25]

I. J. Goodfellow, J. Shlens, and C. Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014.

[26]

W. L. Hamilton, R. Ying, and J. Leskovec. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584, 2017.

[27]

X. Huang, J. Li, and X. Hu. Label informed attributed network embedding. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pages 731--739, 2017.

Digital Library

[28]

V. N. Ioannidis, D. Berberidis, and G. B. Giannakis. Graphsac: Detecting anomalies in large-scale graphs. arXiv preprint arXiv:1910.09589, 2019.

[29]

G. Jeh and J. Widom. Scaling personalized web search. In Proceedings of the 12th international conference on World Wide Web, pages 271--279, 2003.

Digital Library

[30]

J. Jia, B. Wang, X. Cao, and N. Z. Gong. Certified robustness of community detection against adversarial structural perturbation via randomized smoothing. arXiv preprint arXiv:2002.03421, 2020.

Digital Library

[31]

B. Jiang, Z. Zhang, D. Lin, J. Tang, and B. Luo. Semisupervised learning with graph learning-convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 11313--11320, 2019.

[32]

H. Jin and X. Zhang. Latent adversarial training of graph convolution networks. In ICML Workshop on Learning and Reasoning with GraphStructured Representations, 2019.

[33]

W. Jin, Y. Ma, X. Liu, X. Tang, S. Wang, and J. Tang. Graph structure learning for robust graph neural networks. arXiv preprint arXiv:2005.10203, 2020.

[34]

T. N. Kipf and M. Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.

[35]

T. N. Kipf and M. Welling. Variational graph autoencoders. arXiv preprint arXiv:1611.07308, 2016.

[36]

J. Klicpera, A. Bojchevski, and S. G¨unnemann. Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997, 2018.

[37]

L. Landrieu and M. Simonovsky. Large-scale point cloud semantic segmentation with superpoint graphs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4558--4567, 2018.

[38]

J. Li, H. Zhang, Z. Han, Y. Rong, H. Cheng, and J. Huang. Adversarial attack on community detection by hiding individuals, 2020.

Digital Library

[39]

Y. Li, S. Bai, C. Xie, Z. Liao, X. Shen, and A. Yuille. Regional homogeneity: Towards learning transferable universal adversarial perturbations against defenses. arXiv preprint arXiv:1904.00979, 2019.

[40]

Y. Li, S. Bai, Y. Zhou, C. Xie, Z. Zhang, and A. L. Yuille. Learning transferable adversarial examples via ghost networks. In AAAI, pages 11458--11465, 2020.

[41]

Y. Li, W. Jin, H. Xu, and J. Tang. Deeprobust: A pytorch library for adversarial attacks and defenses. arXiv preprint arXiv:2005.06149, 2020.

[42]

Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu. Learning entity and relation embeddings for knowledge graph completion. In Twentyninth AAAI conference on artificial intelligence, 2015.

Digital Library

[43]

X. Liu, S. Si, X. Zhu, Y. Li, and C.-J. Hsieh. A unified framework for data poisoning attack to graph-based semi-supervised learning. arXiv preprint arXiv:1910.14147, 2019.

[44]

J. Ma, S. Ding, and Q. Mei. Black-box adversarial attacks on graph neural networks with limited node access. arXiv preprint arXiv:2006.05057, 2020.

[45]

T. Ma, C. Xiao, J. Zhou, and F. Wang. Drug similarity integration through attentive multi-view graph autoencoders. arXiv preprint arXiv:1804.10850, 2018.

Digital Library

[46]

Y. Ma, S. Wang, L. Wu, and J. Tang. Attacking graph convolutional networks via rewiring. arXiv preprint arXiv:1906.03750, 2019.

[47]

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083, 2017.

[48]

D. Marcheggiani and I. Titov. Encoding sentences with graph convolutional networks for semantic role labeling. arXiv preprint arXiv:1703.04826, 2017.

[49]

M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1):415--444, 2001.

[50]

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.

[51]

B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 701--710, 2014.

Digital Library

[52]

T. Pham, T. Tran, D. Phung, and S. Venkatesh. Column networks for collective classification. arXiv preprint arXiv:1609.04508, 2016.

Digital Library

[53]

J. Qiu, Y. Dong, H. Ma, J. Li, K. Wang, and J. Tang. Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pages 459--467, 2018.

Digital Library

[54]

A. Said, E. W. De Luca, and S. Albayrak. How social relationships affect user similarities.

[55]

P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad. Collective classification in network data. AI magazine, 29(3):93--93, 2008.

Digital Library

[56]

L. Sun, J.Wang, P. S. Yu, and B. Li. Adversarial attack and defense on graph data: A survey. arXiv preprint arXiv:1812.10528, 2018.

[57]

Y. Sun, S. Wang, X. Tang, T.-Y. Hsieh, and V. Honavar. Node injection attacks on graphs via reinforcement learning. arXiv preprint arXiv:1909.06543, 2019.

[58]

M. Sundararajan, A. Taly, and Q. Yan. Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine LearningVolume 70, pages 3319--3328. JMLR. org, 2017.

Digital Library

[59]

J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. Line: Large-scale information network embedding. In Proceedings of the 24th international conference on world wide web, pages 1067--1077, 2015.

Digital Library

[60]

X. Tang, Y. Li, Y. Sun, H. Yao, P. Mitra, and Wang. Transferring robustness for graph neural network against poisoning attacks. In Proceedings of the 13th International Conference on Web Search and Data Mining, pages 600--608, 2020.

Digital Library

[61]

S. Tao, H. Shen, Q. Cao, L. Hou, and X. Cheng. Adversarial immunization for improving certifiable robustness on graphs. arXiv preprint arXiv:2007.09647, 2020.

[62]

Veli?ckovi´c, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.

[63]

P. Veli?ckovi´c, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio. Graph attention networks. 2018.

[64]

B. Wang and N. Z. Gong. Attacking graph-based classification via manipulating the graph structure. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pages 2023-- 2040, 2019.

Digital Library

[65]

J. Jia, X. Cao, and N. Z. Gong. Certified robustness of graph neural networks against adversarial structural perturbation. arXiv preprint arXiv:2008.10715, 2020.

[66]

B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao. Neural cleanse: Identifying and mitigating backdoor attacks in neural networks. In 2019 IEEE Symposium on Security and Privacy (SP), pages 707--723. IEEE, 2019.

[67]

J.Wang, M. Luo, F. Suya, J. Li, Z. Yang, and Q. Zheng. Scalable attack on graph data by injecting vicious nodes. arXiv preprint arXiv:2004.13825, 2020.

[68]

H. Ji, C. Shi, B. Wang, Y. Ye, P. Cui, and P. S. Yu. Heterogeneous graph attention network. In The World Wide Web Conference, pages 2022--2032, 2019.

Digital Library

[69]

X. Wang, X. Liu, and C.-J. Hsieh. Graphdefense: Towards robust graph convolutional networks, 2019.

[70]

Jan 2018.

[71]

M. Waniek, T. P. Michalak, M. J. Wooldridge, and T. Rahwan. Hiding individuals and communities in a social network. Nature Human Behaviour, 2(2):139--147, 2018.

[72]

H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. Zhu. Adversarial examples for graph data: deep insights into attack and defense. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, pages 4816--4823. AAAI Press, 2019.

[73]

Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu. A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596, 2019.

[74]

Z. Xi, R. Pang, S. Ji, and T. Wang. Graph backdoor. arXiv preprint arXiv:2006.11890, 2020.

[75]

H. Y. Ma, H. Liu, D. Deb, H. Liu, J. Tang, and A. Jain. Adversarial attacks and defenses in images, graphs and text: A review. arXiv preprint arXiv:1909.08072, 2019.

[76]

Xu, H. Chen, S. Liu, P.-Y. Chen, T.-W. Weng, M. Hong, and X. Lin. Topology attack and defense for graph neural networks: An optimization perspective. arXiv preprint arXiv:1906.04214, 2019.

[77]

K. C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka. Representation learning on graphs with jumping knowledge networks. arXiv preprint arXiv:1806.03536, 2018.

[78]

X. Xu, Y. Yu, B. Li, L. Song, C. Liu, and C. Gunter. Characterizing malicious edges targeting on graph neural networks. 2018.

[79]

X. Zang, Y. Xie, J. Chen, and B. Yuan. Graph universal adversarial attacks: A few bad actors ruin graph learning models, 2020.

[80]

A. Zhang and J. Ma. Defensevgae: Defending against adversarial attacks on graph data via a variational graph autoencoder. arXiv preprint arXiv:2006.08900, 2020.

[81]

H. Zhang, T. Zheng, J. Gao, C. Miao, L. Su, Y. Li, and K. Ren. Towards data poisoning attack against knowledge graph embedding. ArXiv, abs/1904.12052, 2019.

[82]

X. Zhang and M. Zitnik. Gnnguard: Defending graph neural networks against adversarial attacks. arXiv preprint arXiv:2006.08149, 2020.

[83]

Z. Zhang, J. Jia, B. Wang, and N. Z. Gong. Backdoor attacks to graph neural networks. arXiv preprint arXiv:2006.11165, 2020.

[84]

J. Zhou, G. Cui, Z. Zhang, C. Yang, Z. Liu, and M. Sun. Graph neural networks: A review of methods and applications. arXiv preprint arXiv:1812.08434, 2018.

[85]

L. Li, N. Cao, L. Ying, and H. Tong. Admiring: Adversarial multi-network mining. In 2019 IEEE International Conference on Data Mining (ICDM), pages 1522--1527. IEEE, 2019.

[86]

Q. Zhou, Y. Ren, T. Xia, L. Yuan, and L. Chen. Data poisoning attacks on graph convolutional matrix completion. In Algorithms and Architectures for Parallel Processing, 2020.

[87]

D. Zhu, Z. Zhang, P. Cui, and W. Zhu. Robust graph convolutional networks against adversarial attacks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1399--1407, 2019.

Digital Library

[88]

X. Zhu and Z. Ghahramani. Learning from labeled and unlabeled data with label propagation. 2002.

[89]

D. Z¨ugner, A. Akbarnejad, and S. G¨unnemann. Adversarial attacks on neural networks for graph data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2847--2856, 2018.

Digital Library

[90]

D. Z¨ugner, O. Borchert, A. Akbarnejad, and S. Guennemann. Adversarial attacks on graph neural networks: Perturbations and their patterns. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(5):1-- 31, 2020.

Digital Library

[91]

D. Z¨ugner and S. G¨unnemann. Adversarial attacks on graph neural networks via meta learning. arXiv preprint arXiv:1902.08412, 2019.

[92]

D. Z¨ugner and S. G¨unnemann. Certifiable robustness and robust training for graph convolutional networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 246--256, 2019.

Digital Library

[93]

D. Z¨ugner and S. G¨unnemann. Certifiable robustness of graph convolutional networks under structure perturbations. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1656--1665, 2020.

Digital Library

Cited By

Singh VKumar SPrasad AJayadeva (2025)A Unified Optimization-Based Framework for Certifiably Robust and Fair Graph Neural NetworksIEEE Transactions on Signal Processing10.1109/TSP.2024.351409173(83-98)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TSP.2024.3514091
Zhang ZJiang BTang JLuo B(2025)Label Guided Graph Optimized Convolutional Network for Semi-Supervised LearningIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2025.352596111(71-84)Online publication date: 2025
https://doi.org/10.1109/TSIPN.2025.3525961
Liao LZheng LShang JLi XChen F(2025)ATPF: An Adaptive Temporal Perturbation Framework for Adversarial Attacks on Temporal Knowledge GraphIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.351068937:3(1091-1104)Online publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1109/TKDE.2024.3510689
Show More Cited By

Adversarial Attacks and Defenses on Graphs
1. Computing methodologies

Recommendations

Efficient Defenses Against Adversarial Attacks
AISec '17: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security

Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of ...
Adversarial Attacks and Defenses: Frontiers, Advances and Practice
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples leaves us a big hesitation when applying DNN models on safety-critical tasks such as ...
Adversarial Attacks and Defenses: An Interpretation Perspective

Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the ...

Comments

Information & Contributors

Information

Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 22, Issue 2

December 2020

50 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/3447556

Editors:
Hanghang Tong
Arizona State University
,
Xin Luna Dong
Google
,
Ankur Teredesai
University of Washington Tacoma
,
Reza Zafarani
Syracuse University

Issue’s Table of Contents

Copyright © 2021 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 January 2021

Published in SIGKDD Volume 22, Issue 2

Check for updates

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

102
Total Citations
View Citations
2,155
Total Downloads

Downloads (Last 12 months)231
Downloads (Last 6 weeks)32

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Singh VKumar SPrasad AJayadeva (2025)A Unified Optimization-Based Framework for Certifiably Robust and Fair Graph Neural NetworksIEEE Transactions on Signal Processing10.1109/TSP.2024.351409173(83-98)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TSP.2024.3514091
Zhang ZJiang BTang JLuo B(2025)Label Guided Graph Optimized Convolutional Network for Semi-Supervised LearningIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2025.352596111(71-84)Online publication date: 2025
https://doi.org/10.1109/TSIPN.2025.3525961
Liao LZheng LShang JLi XChen F(2025)ATPF: An Adaptive Temporal Perturbation Framework for Adversarial Attacks on Temporal Knowledge GraphIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.351068937:3(1091-1104)Online publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1109/TKDE.2024.3510689
Zhang ZLu SHuang ZZhao Z(2025)Graph Neural Networks With Adaptive StructuresIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2024.348589219:1(181-194)Online publication date: Jan-2025
https://doi.org/10.1109/JSTSP.2024.3485892
Chanda DGheshlaghi SSoltani N(2025)Explainability-based adversarial attack on graphs through edge perturbationKnowledge-Based Systems10.1016/j.knosys.2024.112895310(112895)Online publication date: Feb-2025
https://doi.org/10.1016/j.knosys.2024.112895
Xia HJiang YMa MZhang RYou Y(2025)Topological Vulnerability-Based Imperceptible Node Injection Attack Against Dynamic Graph Neural NetworkComputing and Combinatorics10.1007/978-981-96-1093-8_45(551-562)Online publication date: 20-Feb-2025
https://doi.org/10.1007/978-981-96-1093-8_45
Zhang TLv LBardou D(2024)Evolutionary Perturbation Attack on Temporal Link PredictionJournal of the Physical Society of Japan10.7566/JPSJ.93.07400293:7Online publication date: 15-Jul-2024
https://doi.org/10.7566/JPSJ.93.074002
Gohil VPatnaik SKalathil DRajendran JBalzarotti DXu W(2024)AttackGNNProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3698905(73-90)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3698905
Huang JShen JShi XZhu XSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)On which nodes does GCN fail? enhancing GCN from the node perspectiveProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692879(20073-20095)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692879
Zhao JJiang NPei KWen JZhan HTu Z(2024)TPoison: Data-Poisoning Attack against GNN-Based Social Trust ModelMathematics10.3390/math1212181312:12(1813)Online publication date: 11-Jun-2024
https://doi.org/10.3390/math12121813
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents