Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities

F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE transactions on neural networks , vol. 20, no. 1, pp. 61–80, 2008

2008

Earlier work this paper cites.

M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, “Geometric deep learning: going beyond euclidean data,” IEEE Signal Processing Magazine , vol. 34, no. 4, pp. 18–42, 2017

2017

Earlier work this paper cites.

J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in Proceedings of the International Conference on Machine Learning (ICML) , 2017, pp. 1263–1272

2017

Earlier work this paper cites.

Z. Xu, J. Tang, J. Meng, W. Zhang, Y. Wang, C. H. Liu, and D. Yang, “Experience-driven networking: A deep reinforcement learning based approach,” in IEEE INFOCOM , 2018, pp. 1871–1879

2018

Earlier work this paper cites.

P. W. Battaglia et al. , “Relational inductive biases, deep learning, and graph networks,” arXiv preprint arXiv:1806.01261 , 2018

Original

2018

Earlier work this paper cites.

F. Geyer and G. Carle, “Learning and generating distributed routing protocols using graph-based deep learning,” in Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks , 2018, pp. 40–45

2018

Cited alongside, same era.

K. Rusek, J. Suárez-Varela, A. Mestres, P. Barlet-Ros, and A. Cabellos-Aparicio, “Unveiling the potential of graph neural networks for network modeling and optimization in sdn,” in Proceedings of the 2019 ACM Symposium on SDN Research , 2019, pp. 140–151

2019

Cited alongside, same era.

F. Geyer and S. Bondorf, “DeepTMA: Predicting effective contention models for network calculus using graph neural networks,” in IEEE INFOCOM , 2019, pp. 1009–1017

2019

Cited alongside, same era.

N. Vesselinova, R. Steinert, D. F. Perez-Ramirez, and M. Boman, “Learning combinatorial optimization on graphs: A survey with applications to networking,” IEEE Access , vol. 8, pp. 120 388–120 416, 2020

2020

Cited alongside, same era.

T. Zhu, X. Chen, L. Chen, W. Wang, and G. Wei, “GCLR: GNN-based cross layer optimization for multipath TCP by routing,” IEEE Access , vol. 8, pp. 17 060–17 070, 2020

2020

Later among the works it cites.

Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis,” IEEE Journal on Selected Areas in Communications , vol. 39, no. 1, pp. 101–115, 2020

2020

Later among the works it cites.

M. Eisen and A. Ribeiro, “Optimal wireless resource allocation with random edge graph neural networks,” IEEE Transactions on Signal Processing , vol. 68, pp. 2977–2991, 2020

2020

Later among the works it cites.

D. Pujol-Perich, J. Suárez-Varela, M. Ferriol, S. Xiao, B. Wu, A. Cabellos-Aparicio, and P. Barlet-Ros, “IGNNITION: bridging the gap between graph neural networks and networking systems,” IEEE Network , Nov. 2021

2021

Closest in time.