Must-read papers on GNN for communication networks

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Content

1-. Surveys and related articles

2-. Wired networks

3-. Wireless networks

4-. Job scheduling in data centers

5-. Explainability

Surveys and related articles

• Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities.
  IEEE Network, 2021. [DOI] [ArXiv]
  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]
  Jiang W.

• 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.

Wired networks

• 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

Wireless networks

• 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.

• Graph Attention Spatial-Temporal Network with Collaborative Global-Local Learning for Citywide Mobile Traffic Prediction
  IEEE Transactions on Mobile Computing, 2020. [paper]
  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]
  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]
  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

Explainability

• 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.

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