Multiple-model and time-sensitive dynamic active learning for recurrent graph convolutional network model extraction attacks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s13042-023-01916-4.pdf
Reference48 articles.
1. Rozemberczki B et al (2021) Chickenpox cases in hungary: a benchmark dataset for spatiotemporal signal processing with graph neural networks. arXiv preprint arXiv:2102.08100
2. Yao Y, Joe-Wong C (2021) Interpretable clustering on dynamic graphs with recurrent graph neural networks. In: AAAI
3. Djenouri Y et al (2023) Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting. Future Gen Comput Syst 139:100–108
4. Djenouri Y et al (2022) Intelligent graph convolutional neural network for road crack detection. In: IEEE transactions on intelligent transportation systems
5. Zhao L et al (2019) T-gcn: a temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transp Syst 21(9):3848–3858
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