A double-layer attentive graph convolution networks based on transfer learning for dynamic graph classification
Author:
Funder
the National Natural Science Foundation of China under Grant
MoE-CMCC “Artifical Intelligence” Project
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/s13042-023-01944-0.pdf
Reference44 articles.
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3. Yu B, Yin H, Zhu Z (2017) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875
4. Song C, Lin Y, Guo S, Wan H (2020) Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 914–921
5. Roy A, Roy KK, Ali AA, Amin MA, Rahman AM (2021) Unified spatio-temporal modeling for traffic forecasting using graph neural network. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE
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