A privacy preserving graph neural networks framework by protecting user’s attributes
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DOI: 10.1016/j.physa.2023.129187
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References listed on IDEAS
- He, Changxiang & Zeng, Jiayuan & Li, Yan & Liu, Shuting & Liu, Lele & Xiao, Chen, 2022. "Two-stream signed directed graph convolutional network for link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
- Cheng, Rongjun & Lyu, Hao & Zheng, Yaxing & Ge, Hongxia, 2022. "Modeling and stability analysis of cyberattack effects on heterogeneous intelligent traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
- Feng, Pihu & Lu, Xin & Gong, Zaiwu & Sun, Duoyong, 2021. "A case study of the pyramid scheme in China based on communication network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
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Keywords
Graph neural networks; Privacy preserving; Homomorphic encryption; Differential privacy;All these keywords.
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