Fetching the paper…
Reading the bibliography…
The emergence of Graph Neural Networks (GNNs) in graph data analysis and their deployment on Machine Learning as a Service platforms have raised critical concerns about data misuse during model training.
N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramèr, “Membership inference attacks from first principles,” in IEEE Symposium on Security and Privacy . IEEE, 2022, pp. 1897–1914
1914
Earlier work this paper cites.
C. A. Choquette-Choo, F. Tramèr, N. Carlini, and N. Papernot, “Label-only membership inference attacks,” in ICML , ser. Proceedings of Machine Learning Research, vol. 139. PMLR, 2021, pp. 1964–1974
1974
Earlier work this paper cites.
R. Mrowka, A. Patzak, and H. Herzel, “Is there a bias in proteome research?” Genome research , vol. 11, no. 12, pp. 1971–1973, 2001
2001
Earlier work this paper cites.
Y. Cao and J. Yang, “Towards making systems forget with machine unlearning,” in IEEE Symposium on Security and Privacy . IEEE Computer Society, 2015, pp. 463–480
2015
Earlier work this paper cites.
R. Melo, R. Fieldhouse, A. Melo, J. D. Correia, M. N. D. Cordeiro, Z. H. Gümüş, J. Costa, A. M. Bonvin, and I. S. Moreira, “A machine learning approach for hot-spot detection at protein-protein interfaces,” International journal of molecular sciences , vol. 17, no. 8, p. 1215, 2016
2016
Earlier work this paper cites.
W. L. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in NIPS , 2017, pp. 1024–1034
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR (Poster) . OpenReview.net, 2017
2017
Earlier work this paper cites.
Z. You, M. Zhou, X. Luo, and S. Li, “Highly efficient framework for predicting interactions between proteins,” IEEE Trans. Cybern. , vol. 47, no. 3, pp. 731–743, 2017
2017
Earlier work this paper cites.
L. Chen, Y. Liu, Z. Zheng, and P. S. Yu, “Heterogeneous neural attentive factorization machine for rating prediction,” in CIKM 2018 . ACM, pp. 833–842
2018
Earlier work this paper cites.
J. B. Mitchell, “Artificial intelligence in pharmaceutical research and development,” pp. 1529–1531, 2018
2018
Earlier work this paper cites.
A. Shafahi, W. R. Huang, M. Najibi, O. Suciu, C. Studer, T. Dumitras, and T. Goldstein, “Poison frogs! targeted clean-label poisoning attacks on neural networks,” in NeurIPS , 2018, pp. 6106–6116
2018
Earlier work this paper cites.
Themeix, Jul 2018. [Online]. Available: https://www.dgl.ai/pages/about.html
2018
Earlier work this paper cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in ICLR (Poster) . OpenReview.net, 2018
2018
Earlier work this paper cites.
J. Wang, P. Huang, H. Zhao, Z. Zhang, B. Zhao, and D. L. Lee, “Billion-scale commodity embedding for e-commerce recommendation in alibaba,” in KDD 2018 . ACM, pp. 839–848
2018
Earlier work this paper cites.
F. Xie, L. Chen, Y. Ye, Z. Zheng, and X. Lin, “Factorization machine based service recommendation on heterogeneous information networks,” in ICWS 2018 . IEEE, pp. 115–122
2018
Earlier work this paper cites.
D. Geer, “Medical informatics engineering breach: The gift that keeps on giving,” Jan 2019. [Online]. Available: https://medium.com/the-aftermath-of-a-data-breach/medical-informatics-engineering-breach-the-gift-that-keeps-on-giving-9948231d2e95
2019
Earlier work this paper cites.
M. Kop, “Ai & intellectual property: Towards an articulated public domain,” Tex. Intell. Prop. LJ , vol. 28, p. 297, 2019
2019
Earlier work this paper cites.
J. Simon, “Now available on amazon sagemaker: The deep graph library,” Dec 2019. [Online]. Available: https://aws.amazon.com/blogs/aws/now-available-on-amazon-sagemaker-the-deep-graph-library/
2019
Earlier work this paper cites.
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in ICLR . OpenReview.net, 2019
2019
Earlier work this paper cites.
H. Yang, “Aligraph: A comprehensive graph neural network platform,” in KDD . ACM, 2019, pp. 3165–3166
2019
Earlier work this paper cites.
K. Yang, K. Swanson, W. Jin, C. Coley, P. Eiden, H. Gao, A. Guzman-Perez, T. Hopper, B. Kelley, M. Mathea et al. , “Analyzing learned molecular representations for property prediction,” Journal of chemical information and modeling , vol. 59, no. 8, pp. 3370–3388, 2019
2019
Earlier work this paper cites.
Y. Chen, L. Wu, and M. J. Zaki, “Iterative deep graph learning for graph neural networks: Better and robust node embeddings,” in NeurIPS 2020,
2020
Earlier work this paper cites.
W. Jin, Y. Ma, X. Liu, X. Tang, S. Wang, and J. Tang, “Graph structure learning for robust graph neural networks,” in KDD . ACM, 2020, pp. 66–74
2020
Earlier work this paper cites.
Y. Liu, H. Yuan, L. Cai, and S. Ji, “Deep learning of high-order interactions for protein interface prediction,” in KDD . ACM, 2020, pp. 679–687
2020
Earlier work this paper cites.
A. Sablayrolles, M. Douze, C. Schmid, and H. Jégou, “Radioactive data: tracing through training,” in ICML , ser. Proceedings of Machine Learning Research, vol. 119. PMLR, 2020, pp. 8326–8335
2020
Earlier work this paper cites.
Z. Xi, R. Pang, S. Ji, and T. Wang, “Graph backdoor,” CoRR , vol. abs/2006.11890, 2020
2020
Earlier work this paper cites.
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. K. Prasanna, “Graphsaint: Graph sampling based inductive learning method,” in ICLR . OpenReview.net, 2020
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 IEEE Symposium on Security and Privacy . IEEE, 2021, pp. 141–159
2021
Cited alongside, same era.
A. Deng and B. Hooi, “Graph neural network-based anomaly detection in multivariate time series,” in AAAI 2021 . AAAI Press, pp. 4027–4035
2021
Cited alongside, same era.
X. He, J. Jia, M. Backes, N. Z. Gong, and Y. Zhang, “Stealing links from graph neural networks.” in USENIX Security Symposium , 2021, pp. 2669–2686
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Later among the works it cites.
S. Zhang, H. Chen, X. Sun, Y. Li, and G. Xu, “Unsupervised graph poisoning attack via contrastive loss back-propagation,” in WWW . ACM, 2022, pp. 1322–1330
2022
Later among the works it cites.
Z. Zhang, Y. Zhou, X. Zhao, T. Che, and L. Lyu, “Prompt certified machine unlearning with randomized gradient smoothing and quantization,” in NeurIPS , 2022
2022
Later among the works it cites.
Amazon Web Services. (Accessed 2023) Amazon sagemaker - machine learning platform. [Online]. Available: https://aws.amazon.com/pm/sagemaker/
2023
Closest in time.
AWS Labs, “GraphStorm,” https://github.com/awslabs/graphstorm , accessed on June 29, 2023
2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
W. Jin, J. M. Stokes, R. T. Eastman, Z. Itkin, A. V. Zakharov, J. J. Collins, T. S. Jaakkola, and R. Barzilay, “Deep learning identifies synergistic drug combinations for treating covid-19,” Proceedings of the National Academy of Sciences , vol. 118, no. 39, p. e2105070118, 2021
2021
Cited alongside, same era.
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko et al. , “Highly accurate protein structure prediction with alphafold,” Nature , vol. 596, no. 7873, pp. 583–589, 2021
2021
Cited alongside, same era.
I. E. Olatunji, W. Nejdl, and M. Khosla, “Membership inference attack on graph neural networks,” in TPS-ISA . IEEE, 2021, pp. 11–20
2021
Cited alongside, same era.
Y. Wan, Y. Liu, D. Wang, and Y. Wen, “GLAD-PAW: graph-based log anomaly detection by position aware weighted graph attention network,” in PAKDD 2021 , ser. Lecture Notes in Computer Science, vol. 12712. Springer, pp. 66–77
2021
Cited alongside, same era.
B. Wu, X. Yang, S. Pan, and X. Yuan, “Adapting membership inference attacks to GNN for graph classification: Approaches and implications,” in ICDM . IEEE, 2021, pp. 1421–1426
2021
Cited alongside, same era.
H. Zhang, B. Wu, X. Yang, C. Zhou, S. Wang, X. Yuan, and S. Pan, “Projective ranking: A transferable evasion attack method on graph neural networks,” in CIKM . ACM, 2021, pp. 3617–3621
2021
Cited alongside, same era.
Z. Zhang, J. Jia, B. Wang, and N. Z. Gong, “Backdoor attacks to graph neural networks,” in SACMAT 2021 . ACM, pp. 15–26
2021
Cited alongside, same era.
A. Burky, “Advocate aurora says 3m patients’ health data possibly exposed through tracking technologies,” Oct 2022. [Online]. Available: https://www.fiercehealthcare.com/health-tech/advocate-aurora-health-data-breach-revealed-pixels-protected-health-information-3
2022
Cited alongside, same era.
Closest in time.
J. Cheng, G. Dasoulas, H. He, C. Agarwal, and M. Zitnik, “GNNDelete: A general strategy for unlearning in graph neural networks,” in ICLR . OpenReview.net, 2023
2023
Closest in time.
V. S. Chundawat, A. K. Tarun, M. Mandal, and M. S. Kankanhalli, “Zero-shot machine unlearning,” IEEE Trans. Inf. Forensics Secur. , vol. 18, pp. 2345–2354, 2023
2023
Closest in time.
S. Frenkel and S. A. Thompson, ““not for machines to harvest”: Data revolts break out against a.i.” Jul 2023. [Online]. Available: https://www.nytimes.com/2023/07/15/technology/artificial-intelligence-models-chat-data.html?searchResultPosition=1
2023
Closest in time.
S. Gatlan, “Healthcare giant chs reports first data breach in goanywhere hacks,” Mar 2023. [Online]. Available: https://www.bleepingcomputer.com/news/security/healthcare-giant-chs-reports-first-data-breach-in-goanywhere-hacks/
2023
Closest in time.
Google Cloud. (Accessed 2023) Google cloud vertex ai. [Online]. Available: https://cloud.google.com/vertex-ai
2023
Closest in time.
IBM. (Accessed 2023) Ibm watson machine learning for z/os. [Online]. Available: https://www.ibm.com/docs/en/wml-for-zos
2023
Closest in time.
D. Jin, L. Wang, H. Zhang, Y. Zheng, W. Ding, F. Xia, and S. Pan, “A survey on fairness-aware recommender systems,” Inf. Fusion , vol. 100, p. 101906, 2023
2023
Closest in time.
H. A. K. LLP, “European parliament agrees on position on the ai act,” Jun 2023. [Online]. Available: https://www.huntonprivacyblog.com/2023/06/15/european-parliament-agrees-on-position-on-the-ai-act/
2023
Closest in time.
Microsoft Azure. (Accessed 2023) Azure machine learning. [Online]. Available: https://azure.microsoft.com/en-au/products/machine-learning
2023
Closest in time.
C. Pan, E. Chien, and O. Milenkovic, “Unlearning graph classifiers with limited data resources,” in WWW . ACM, 2023, pp. 716–726
2023
Closest in time.
Y. Qin, J. Hu, and B. Wu, “Toward evaluating the robustness of deep learning based rain removal algorithm in autonomous driving,” in SecTL@AsiaCCS . ACM, 2023, pp. 1–7
2023
Closest in time.
2023
Closest in time.
X.-W. Wang, L. Madeddu, K. Spirohn, L. Martini, A. Fazzone, L. Becchetti, T. P. Wytock, I. A. Kovács, O. M. Balogh, B. Benczik et al. , “Assessment of community efforts to advance network-based prediction of protein–protein interactions,” Nature Communications , vol. 14, no. 1, p. 1582, 2023
2023
Closest in time.
A. Warnecke, L. Pirch, C. Wressnegger, and K. Rieck, “Machine unlearning of features and labels,” in NDSS . The Internet Society, 2023
2023
Closest in time.
B. Wu, S. Wang, X. Yuan, C. Wang, C. Rudolph, and X. Yang, “Defeating misclassification attacks against transfer learning,” IEEE Trans. Dependable Secur. Comput. , vol. 20, no. 2, pp. 886–901, 2023
2023
Closest in time.
J. Wu, Y. Yang, Y. Qian, Y. Sui, X. Wang, and X. He, “GIF: A general graph unlearning strategy via influence function,” in WWW . ACM, 2023, pp. 651–661
2023
Closest in time.
H. Zhang, B. Wu, S. Wang, X. Yang, M. Xue, S. Pan, and X. Yuan, “Demystifying uneven vulnerability of link stealing attacks against graph neural networks,” in ICML , ser. Proceedings of Machine Learning Research, vol. 202. PMLR, 2023, pp. 41 737–41 752
2023
Closest in time.
2023
Closest in time.
H. Zhang, X. Yuan, C. Zhou, and S. Pan, “Projective ranking-based GNN evasion attacks,” IEEE Trans. Knowl. Data Eng. , vol. 35, no. 8, pp. 8402–8416, 2023
2023
Closest in time.
Y. Zheng, H. Zhang, V. C. Lee, Y. Zheng, X. Wang, and S. Pan, “Finding the missing-half: Graph complementary learning for homophily-prone and heterophily-prone graphs,” in ICML , ser. Proceedings of Machine Learning Research, vol. 202. PMLR, 2023, pp. 42 492–42 505
2023
Closest in time.
F. Wu, Y. Long, C. Zhang, and B. Li, “Linkteller: Recovering private edges from graph neural networks via influence analysis,” in 2022 IEEE Symposium on Security and Privacy (SP) . IEEE, 2022, pp. 2005–2024
2024
Closest in time.