Please, feel free to contribute to this list by making a pull request.
1-. Surveys and related articles |
2-. Wired networks |
3-. Wireless networks |
4-. Job scheduling in data centers |
5-. Explainability |
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Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities. arXiv:2112.14792, 2021. [paper]
J. Suárez-Varela, P. Almasan, M. Ferriol-Galmés, K. Rusek, F. Geyer, X. Cheng, X. Shi, S. Xiao, F. Scarselli, A. Cabellos-Aparicio, P. Barlet-Ros. -
Learning Combinatorial Optimization on Graphs: A Survey With Applications to Networking. IEEE ACCESS, 2020. [paper]
N. Vesselinova, R. Steinert, D. Perez-Ramirez, M. Boman. -
IGNNITION: A framework for fast prototyping of Graph Neural Networks. GNNSys workshop, 2021. [paper]
D. Pujol-Perich, J. Suárez-Varela, M. Ferriol-Galmés, S. Xiao, B. Wu, A. Cabellos-Aparicio, P. Barlet-Ros.
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RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN. IEEE JSAC, 2020. [paper]
K. Rusek, J. Suárez-Varela, P. Almasan, P. Barlet-Ros, A. Cabellos-Aparicio. -
Learning and generating distributed routing protocols using graph-based deep learning. ACM SIGCOMM BigDAMA workshop, 2018. [paper] [code]
F. Geyer, G. Carle. -
Is machine learning ready for traffic engineering optimization? IEEE International Conference on Network Protocols (ICNP), 2021. [paper]
G. Bernrdez, J. Suárez-Varela, A. López, B. Wu, S. Xiao, X. Cheng, P. Barlet-Ros, and A. Cabellos-Aparicio. -
DeepTMA: Predicting Effective Contention Models for Network Calculus using Graph Neural Networks. IEEE INFOCOM, 2019. [paper]
F. Geyer, S. Bondorf. -
Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN. ACM SOSR, 2019. [paper] [code]
K. Rusek, J. Suárez-Varela, A. Mestres, P. Barlet-Ros, A. Cabellos-Aparicio. -
Towards more realistic network models based on Graph Neural Networks. ACM CoNEXT student workshop, 2019. [paper] [code]
A. Badia-Sampera, J. Suárez-Varela, P. Almasan, K. Rusek, P. Barlet-Ros, A. Cabellos-Aparicio. -
Deep Reinforcement Learning meets Graph Neural Networks: Exploring a routing optimization use case. ArXiv preprint arXiv:1910.07421, 2019 [paper]
P. Almasan, J. Suárez-Varela, A. Badia-Sampera, K. Rusek, P. Barlet-Ros, A. Cabellos-Aparicio. -
A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding. IEEE Transactions on Network and Service Management, 2019. [paper]
Q. T. A. Pham, Y. Hadjadj-Aoul, A. Outtagarts. -
DeepMPLS: Fast Analysis of MPLS Configurations Using Deep Learning. IFIP Networking, 2019. [paper]
F. Geyer, S. Schmid. -
Combining Deep Reinforcement Learning With Graph Neural Networks for Optimal VNF Placement. IEEE Communications Letters, 2020. [paper]
P Sun, J Lan, J Li, Z Guo, Y Hu. -
GCLR: GNN-Based Cross Layer Optimization for Multipath TCP by Routing. IEEE Access, 2020. [doi]
H. Wang, Y. Wu, G. Min, W. Miao -
Network Planning with Deep Reinforcement Learning. ACM SIGCOMM, 2021. [doi]
H. Zhu, V. Gupta, S. S. Ahuja, Y. D. Tian, Y. Zhang, and X. Jin
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Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis. IEEE JSAC, 2020. [paper]
Y. Shen, Y. Shi, J. Zhang, K.B. Letaief. -
Optimal wireless resource allocation with random edge graph neural networks. IEEE Transactions on Signal Processing, 2020. [paper]
M. Eisen, A. Ribeiro. -
Relational Deep Reinforcement Learning for Routing in Wireless Networks. arXiv preprint arXiv:2012.15700, 2020. [paper]
V. Manfredi,, A. Wolfe, B. Wang, X. Zhang. -
Unsupervised Learning for Asynchronous Resource Allocation in Ad-hoc Wireless Networks. arXiv preprint arXiv:2011.02644, 2020. [paper]
Z. Wang, M. Eisen, A. Ribeiro.
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Learning scheduling algorithms for data processing clusters. ACM SIGCOMM, 2019. [paper]
H. Mao, M. Schwarzkopf, S. B. Venkatakrishnan, Z. Meng, M. Alizadeh. -
DeepWeave: Accelerating Job Completion Time with Deep Reinforcement Learning-based Coflow Scheduling. IJCAI, 2020. [paper]
P. Sun, Z. Guo, J. Wang, J. Li, J. Lan, Y. Hu
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Interpreting Deep Learning-Based Networking Systems. ACM SIGCOMM, 2020. [paper]
Z. Meng, M. Wang, J. Bai, M. Xu, H. Mao, H. Hu. -
NetXplain: Real-time explainability of Graph Neural Networks applied to Computer Networks. GNNSys workshop, 2021. [paper]
D. Pujol-Perich, J. Suárez-Varela, S. Xiao, B. Wu, A. Cabellos-Aparicio, P. Barlet-Ros.