Open Access
| Issue |
MATEC Web Conf.
Volume 413, 2025
International Conference on Measurement, AI, Quality and Sustainability (MAIQS 2025)
|
|
|---|---|---|
| Article Number | 04004 | |
| Number of page(s) | 6 | |
| Section | Artificial Intelligence and Robotics | |
| DOI | https://doi.org/10.1051/matecconf/202541304004 | |
| Published online | 01 October 2025 | |
- Z. Shu, H. B. Du, X. Y. Zhu, et al., Research on the control strategies of data flow transmission paths for MPTCP-based communication networks. PeerJ Comput. Sci. 9, e1716 (2023). https://doi.org/10.1016/j.nanoen.2020.104652 [Google Scholar]
- F. Jowkarishasaltaneh, J. But, An Analysis of MPTCP Congestion Control. Telecom 3, 581–609(2022). https://doi.org/10.3390/telecom3040033 [Google Scholar]
- D. Chen, W. Zhang, D. Gao, et al., GFlow: GNN- Based Optimal Flow Scheduling for Multipath Transmission with Link Overlapping. IEEE Trans. Netw. Sci. Eng. 11(6), 6244–6258(2024). https://doi.org/10.1109/TNSE.2024.3481413 [Google Scholar]
- P. Almasan, J. Suárez-Varela, K. Rusek, et al., Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case. Comput. Commun. 196, 184–194(2022). https://doi.org/10.1016/j.comcom.2022.09.029 [Google Scholar]
- Y. Shi, W. Wang, X. Zhu, et al., Low Earth Orbit Satellite Network Routing Algorithm Based on Graph Neural Networks and Deep Q-Network. Appl. Sci. 14(9), 3840 (2024). https://doi.org/10.3390/app14093840 [Google Scholar]
- J. Xu, Y. Wang, B. Zhang, et al., A Graph reinforcement learning based SDN routing path selection for optimizing long-term revenue. Future Gener. Comput. Syst. 150, 412–423(2024). https://doi.org/10.1016/j.future.2023.09.017 [Google Scholar]
- B. Yan, Q. Liu, J. L. Shen, et al., Flowlet-level multipath routing based on graph neural network in OpenFlow-based SDN. Future Gener. Comput. Syst. 134, 140–153(2022). https://doi.org/10.1016/j.future.2022.04.006 [Google Scholar]
- K. Rusek, J. Suárez-Varela, P. Almasan, et al., RouteNet: Leveraging graph neural networks for network modeling and optimization in SDN. IEEE J. Sel. Areas Commun. 38(10), 2260–2270(2020). https://doi.org/10.1109/JSAC.2020.3000405 [Google Scholar]
- J. B. Hamrick, K. R. Allen, V. Bapst, et al., Relational inductive bias for physical construction in humans and machines. arXiv preprint arXiv:1806.01203 (2018). https://doi.org/10.48550/arXiv.1806.01203 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.