A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
IEEE/CAA Journal of Automatica Sinica
| Citation: | Y. Yuan, D. Lei, C. Zhang, Z. Xiong, C. Li, and L. Zhu, “Personalized differential privacy graph neural network,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125279
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M. Li, A. Micheli, Y. G. Wang, S. Pan, P. Lió, G. S. Gnecco, and M. Sanguineti, “Guest editorial: Deep neural networks for graphs: Theory, models, algorithms, and applications,” IEEE Trans. Neural Networks and Learning Systems, vol. 35, no. 4, pp. 4367–4372, 2024. doi: 10.1109/TNNLS.2024.3371592
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Z. Wei, H. Zhao, Z. Li, X. Bu, Y. Chen, X. Zhang, Y. Lv, and F.-Y. Wang, “STGSA: A novel spatial-temporal graph synchronous aggregation model for traffic prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 226–238, 2023. doi: 10.1109/JAS.2023.123033
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I. E. Olatunji, T. Funke, and M. Khosla, “Releasing graph neural networks with differential privacy guarantees,” arXiv preprint arXiv: 2109.08907, 2021.
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S. Sajadmanesh, A. S. Shamsabadi, A. Bellet, and D. Gatica-Perez, “Gap: Differentially private graph neural networks with aggregation perturbation,” in Proc. USENIX Security 32nd USENIX Security Symp., 2023.
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F. Wu, Y. Long, C. Zhang, and B. Li, “Linkteller: Recovering private edges from graph neural networks via influence analysis,” in Proc. 2022 IEEE Symp. Security and Privacy, 2022, pp. 2005–2024.
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C. Huang, M. Li, F. Cao, H. Fujita, Z. Li, and X. Wu, “Are graph convolutional networks with random weights feasible?” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 2751–2768, 2022.
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T. He, Y. S. Ong, and L. Bai, “Learning conjoint attentions for graph neural nets,” Advances in Neural Information Processing Systems, vol. 34, pp. 2641–2653, 2021.
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H. Zhou, T. He, Y.-S. Ong, G. Cong, and Q. Chen, “Differentiable clustering for graph attention,” IEEE Trans. Knowledge and Data Engineering, 2024.
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