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GNNPapersCommNets

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# Must-read papers on GNN for communication networks
Please, feel free to contribute to this list by making a pull request.

## Content
<table>
<tr><td><a href="#surveys-and-related-articles">1-. Surveys and related articles</a></td></tr>
<tr><td><a href="#wired-networks">2-. Wired networks</a></td></tr>
<tr><td><a href="#wireless-networks">3-. Wireless networks</a></td></tr>
<tr><td><a href="#job-scheduling-in-data-centers">4-. Job scheduling in data centers</a></td></tr>
<tr><td><a href="#explainability">5-. Explainability</a></td></tr>
</table>

## Surveys and related articles

- **Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities.** 
	IEEE Network, 2021. [[DOI](https://doi.org/10.1109/MNET.123.2100773)] [[ArXiv](https://arxiv.org/abs/2112.14792)] 
	*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.*
	
- **Graph-based Deep Learning for Communication Networks: A Survey.** 
	Elsevier Computer Communications, 2021. [[DOI](https://doi.org/10.1016/j.comcom.2021.12.015)] 
	*Jiang W.*

- **Learning Combinatorial Optimization on Graphs: A Survey With Applications to Networking.** 
	IEEE ACCESS, 2020. [[paper](https://arxiv.org/pdf/2005.11081.pdf)] 
	N. Vesselinova, R. Steinert, D. Perez-Ramirez, M. Boman.

- **IGNNITION: A framework for fast prototyping of Graph Neural Networks.** 
	GNNSys workshop, 2021. [[paper](https://gnnsys.github.io/papers/GNNSys21_paper_4.pdf)] 
	*D. Pujol-Perich, J. Suárez-Varela, M. Ferriol-Galmés, S. Xiao, B. Wu, A. Cabellos-Aparicio, P. Barlet-Ros.*

## Wired networks
 
 - **RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN.** 
	IEEE JSAC, 2020. [[paper](https://arxiv.org/pdf/1910.01508.pdf)] 	
	*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](https://www.net.in.tum.de/fileadmin/bibtex/publications/papers/geyer2018bigdama.pdf)] [[code](https://github.com/BNN-UPC/ignnition/tree/main/examples/Graph_query_networks)] 	
	*F. Geyer, G. Carle.*
	
 - **Is machine learning ready for traffic engineering optimization?** 
	IEEE International Conference on Network Protocols (ICNP), 2021. [[paper](https://arxiv.org/pdf/2109.01445.pdf)] 	
	*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](https://www.net.in.tum.de/fileadmin/bibtex/publications/papers/geyer2019infocom.pdf)] 	
	*F. Geyer, S. Bondorf.*
	
- **Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN.** 
	 ACM SOSR, 2019. [[paper](https://arxiv.org/pdf/1901.08113.pdf)] [[code](https://github.com/BNN-UPC/ignnition/tree/main/examples/Routenet)] 	 
	 *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](https://upcommons.upc.edu/bitstream/handle/2117/190294/paper_CoNEXT_postprint.pdf)] [[code](https://github.com/BNN-UPC/ignnition/tree/main/examples/Q-size)] 	
	*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](https://arxiv.org/pdf/1910.07421.pdf)] 
	*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](https://hal.inria.fr/hal-02427641/document)] 	
	*Q. T. A. Pham, Y. Hadjadj-Aoul, A. Outtagarts.*

- **DeepMPLS: Fast Analysis of MPLS Configurations Using Deep Learning.** 
	 IFIP Networking, 2019. [[paper](https://www.net.in.tum.de/fileadmin/bibtex/publications/papers/geyer2019networking.pdf)] 	
	*F. Geyer, S. Schmid.*

- **Combining Deep Reinforcement Learning With Graph Neural Networks for Optimal VNF Placement.** 
	IEEE Communications Letters, 2020. [[paper](https://ieeexplore.ieee.org/abstract/document/9201405)] 	
	*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](https://doi.org/10.1109/ACCESS.2020.2966045)] 	
	*H. Wang, Y. Wu, G. Min, W. Miao*

- **Network Planning with Deep Reinforcement Learning.** 
	 ACM SIGCOMM, 2021. [[doi](https://dl.acm.org/doi/10.1145/3452296.3472902)] 
	 *H. Zhu, V. Gupta, S. S. Ahuja, Y. D. Tian, Y. Zhang, and X. Jin*
	 
	 
## Wireless networks

- **Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis.** 
	IEEE JSAC, 2020. [[paper](https://arxiv.org/pdf/2007.07632.pdf)] 
	*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](https://arxiv.org/pdf/1909.01865.pdf)] 
	*M. Eisen, A. Ribeiro.*
	
 - **Relational Deep Reinforcement Learning for Routing in Wireless Networks.** 
	arXiv preprint arXiv:2012.15700, 2020. [[paper](https://arxiv.org/pdf/2012.15700.pdf)] 
	*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](https://arxiv.org/pdf/2011.02644.pdf)] 
	*Z. Wang, M. Eisen, A. Ribeiro.*
	
- **Graph Attention Spatial-Temporal Network with Collaborative Global-Local Learning for Citywide Mobile Traffic Prediction** 
	IEEE Transactions on Mobile Computing, 2020. [[paper](https://ieeexplore.ieee.org/document/9184280)] 
	*K. He, X. Chen, Q. Wu, S. Yu, Z. Zhou*

- **Channel Estimation for Full-Duplex RIS-assisted HAPS Backhauling with Graph Attention Networks** 
	IEEE International Conference on Communications, 2021. [[paper](https://ieeexplore.ieee.org/document/9500697)] 
	*K. Tekbıyık, G. K. Kurt, C. Huang, A. R. Ekti, H. Yanikomeroglu.*

## Job scheduling in data centers

- **Learning scheduling algorithms for data processing clusters.** 
	ACM SIGCOMM, 2019. [[paper](https://arxiv.org/pdf/1810.01963.pdf)] 
	*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](https://www.ijcai.org/Proceedings/2020/0458.pdf)] 
	*P. Sun, Z. Guo, J. Wang, J. Li, J. Lan, Y. Hu*

## Explainability

- **Interpreting Deep Learning-Based Networking Systems.** 
	ACM SIGCOMM, 2020. [[paper](https://arxiv.org/pdf/1910.03835.pdf)] 
	*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](https://gnnsys.github.io/papers/GNNSys21_paper_7.pdf)] 
	*D. Pujol-Perich, J. Suárez-Varela, S. Xiao, B. Wu, A. Cabellos-Aparicio, P. Barlet-Ros.*
	
## Other lists
This list is intended to be short and keep only relevant references on different types of communication networks. You may refer to the following link for a more complete list with all the existing works in the field:

GNN-Communication-Networks: https://github.com/jwwthu/GNN-Communication-Networks

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