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Graph unlearning emerges as a crucial advancement in the pursuit of responsible AI, providing the means to remove sensitive data traces from trained models, thereby upholding the \textit{right to be forgotten}.
Carnegie Mellon University, School of CS, Machine Learning , 2006
T. M. Mitchell, The discipline of machine learning · 2006
Earlier work this paper cites.
C. Dwork, “Differential privacy: A survey of results,” in International conference on theory and applications of models of computation
2008
Earlier work this paper cites.
C. Fuchs, “Web 2.0, prosumption, and surveillance,” Surveillance & Society
2011
Earlier work this paper cites.
H. Hu, G.-J. Ahn, and J. Jorgensen, “Detecting and resolving privacy conflicts for collaborative data sharing in online social networks,” in Annual Computer Security Applications Conference
2011
Earlier work this paper cites.
J. Rosen, “The right to be forgotten,” Stanford Law Review
2012
Earlier work this paper cites.
K. G. Giota and G. Kleftaras, “Mental health apps: innovations, risks and ethical considerations,” E-Health Telecommunication Systems and Networks
2014
Earlier work this paper cites.
Y. Cao and J. Yang, “Towards making systems forget with machine unlearning,” in 2015 IEEE Symposium on Security and Privacy
2015
Earlier work this paper cites.
P. Glasserman and H. P. Young, “Contagion in financial networks,” Journal of Economic Literature
2016
Earlier work this paper cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in ACM SIGSAC CCS
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
P. W. Koh and P. Liang, “Understanding black-box predictions via influence functions,” in Proceedings of the 34th International Conference on Machine Learning
2017
Earlier work this paper cites.
M. G. Campana and F. Delmastro, “Recommender systems for online and mobile social networks: A survey,” OSNEM
2017
Earlier work this paper cites.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” NeurIPS
2017
Earlier work this paper cites.
G. D. P. Regulation, “Art 17 gdpr—right to erasure (‘right to be forgotten’),” 2018
2018
Earlier work this paper cites.
“California consumer privacy act of 2018.” Cal. Civ. Code § 1798.100 et seq., 2018
2018
Earlier work this paper cites.
C. Bettini, “Privacy protection in location-based services: a survey,” Handbook of Mobile Data Privacy
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha, “Privacy risk in machine learning: Analyzing the connection to overfitting,” in IEEE 31st computer security foundations symposium
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
H. Cai, V. W. Zheng, and K. C.-C. Chang, “A comprehensive survey of graph embedding: Problems, techniques, and applications,” IEEE TKDE
2018
Earlier work this paper cites.
A. Tsitsulin, D. Mottin, P. Karras, A. Bronstein, and E. Müller, “Netlsd: hearing the shape of a graph,” in ACM KDD
2018
Earlier work this paper cites.
G. Mei, Z. Guo, S. Liu, and L. Pan, “Sgnn: A graph neural network based federated learning approach by hiding structure,” in 2019 IEEE International Conference on Big Data
2019
Earlier work this paper cites.
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec, “Open graph benchmark: Datasets for machine learning on graphs,” NeurIPS
2020
Earlier work this paper cites.
C. Guo, T. Goldstein, A. Hannun, and L. Van Der Maaten, “Certified data removal from machine learning models,” in Proceedings of the 37th ICML
2020
Earlier work this paper cites.
X. Li, Y. Xin, C. Zhao, Y. Yang, and Y. Chen, “Graph convolutional networks for privacy metrics in online social networks,” Applied Sciences
2020
Earlier work this paper cites.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE TNNLS
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Morgan & Claypool Publishers, 2020
W. L. Hamilton, Graph representation learning · 2020
Earlier work this paper cites.
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot, “Machine unlearning,” in 2021 IEEE Symposium on Security and Privacy (SP)
2021
Earlier work this paper cites.
B. Z. H. Zhao, A. Agrawal, C. Coburn, H. J. Asghar, R. Bhaskar, M. A. Kaafar, D. Webb, and P. Dickinson, “On the (in) feasibility of attribute inference attacks on machine learning models,” in 2021 IEEE European Symposium on Security and Privacy (EuroS&P)
2021
Earlier work this paper cites.
I. E. Olatunji, W. Nejdl, and M. Khosla, “Membership inference attack on graph neural networks,” in The 3rd IEEE TPS-ISA
2021
Earlier work this paper cites.
E. Ullah, T. Mai, A. Rao, R. A. Rossi, and R. Arora, “Machine unlearning via algorithmic stability,” in Conference on Learning Theory
2021
Earlier work this paper cites.
B. Wu, X. Yang, S. Pan, and X. Yuan, “Model extraction attacks on graph neural networks: Taxonomy and realization,” 2021
2021
Earlier work this paper cites.
S. Rezaei and X. Liu, “On the difficulty of membership inference attacks,” 2021
2021
Earlier work this paper cites.
N. Proferes, N. Jones, S. Gilbert, C. Fiesler, and M. Zimmer, “Studying reddit: A systematic overview of disciplines, approaches, methods, and ethics,” Social Media+ Society
2021
Earlier work this paper cites.
A. Derrow-Pinion, J. She, D. Wong, O. Lange, T. Hester, L. Perez, M. Nunkesser, S. Lee, X. Guo, B. Wiltshire, et al
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
S.-U. Hassan, M. Shabbir, S. Iqbal, A. Said, F. Kamiran, R. Nawaz, and U. Saif, “Leveraging deep learning and sna approaches for smart city policing in the developing world,” IJIM
2021
Cited alongside, same era.
2021
Cited alongside, same era.
A. Sekhari, J. Acharya, G. Kamath, and A. T. Suresh, “Remember what you want to forget: Algorithms for machine unlearning,” 2021
2021
Cited alongside, same era.
A. Sekhari, J. Acharya, G. Kamath, and A. T. Suresh, “Remember what you want to forget: Algorithms for machine unlearning,” Advances in Neural Information Processing Systems
2021
Cited alongside, same era.
S. Sajadmanesh and D. Gatica-Perez, “Locally private graph neural networks,” in ACM SIGSAC CCS
S. Sajadmanesh, A. S. Shamsabadi, A. Bellet, and D. Gatica-Perez, “Gap: Differentially private graph neural networks with aggregation perturbation,” in USENIX Security 2023-32nd USENIX Security Symposium
2023
Closest in time.
2023
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2023
Closest in time.
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al
2023
Closest in time.
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2021
Cited alongside, same era.
A. Said, S.-U. Hassan, W. Abbas, and M. Shabbir, “Netki: a kirchhoff index based statistical graph embedding in nearly linear time,” Neurocomputing
2021
Cited alongside, same era.
A. Said, S.-U. Hassan, S. Tuarob, R. Nawaz, and M. Shabbir, “Dgsd: Distributed graph representation via graph statistical properties,” Future Generation Computer Systems
2021
Cited alongside, same era.
B. Jayaraman and D. Evans, “Are attribute inference attacks just imputation?,” 2022
2022
Cited alongside, same era.
Z. Zhang, M. Chen, M. Backes, Y. Shen, and Y. Zhang, “Inference attacks against graph neural networks,” in USENIX Security
2022
Cited alongside, same era.
M. Chen, Z. Zhang, T. Wang, M. Backes, M. Humbert, and Y. Zhang, “Graph unlearning,” in Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
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Closest in time.
2024
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2024
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Y. Li, C. Chen, Y. Zhang, W. Liu, L. Lyu, X. Zheng, D. Meng, and J. Wang, “Ultrare: Enhancing receraser for recommendation unlearning via error decomposition,” NeurIPS
2024
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J. Zhang, L. Wang, S. Wang, and W. Fan, “Graph unlearning with efficient partial retraining,” 2024
2024
Closest in time.
X. You, J. Xu, M. Zhang, Z. Gao, and M. Yang, “Rrl: Recommendation reverse learning,” in AAAI
2024
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X. Li, Y. Zhao, Z. Wu, W. Zhang, R.-H. Li, and G. Wang, “Towards effective and general graph unlearning via mutual evolution,” 2024
2024
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Y. Sinha, M. Mandal, and M. Kankanhalli, “Distill to delete: Unlearning in graph networks with knowledge distillation,” 2024
2024
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Y. Dong, B. Zhang, Z. Lei, N. Zou, and J. Li, “Idea: A flexible framework of certified unlearning for graph neural networks,” in ACM SIGKDD Conference on Knowledge Discovery and Data Mining
2024
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2024
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2024
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2024
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2024
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H. Zhang, B. Wu, X. Yuan, S. Pan, H. Tong, and J. Pei, “Trustworthy graph neural networks: Aspects, methods, and trends,” Proceedings of the IEEE
2024
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2024
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2024
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2024
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2024
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2024
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2024
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2025
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2025
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2025
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2025
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2025
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Y. Kim, S. Cha, and D. Kim, “Are we truly forgetting? a critical re-examination of machine unlearning evaluation protocols,” 2025
2025
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2025
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2025
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2025
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