Challenging the generalization capabilities of Graph Neural Networks for network modeling-Reference-Cited by-同舟云学术

Challenging the generalization capabilities of Graph Neural Networks for network modeling

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM SIGCOMM 2019 Conference Posters and Demos on - SIGCOMM Posters and Demos '19
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Author:

Suárez-Varela José¹,Carol-Bosch Sergi¹,Rusek Krzysztof²,Almasan Paul¹,Arias Marta³,Barlet-Ros Pere¹,Cabellos-Aparicio Albert¹

Affiliation:

1. Barcelona Neural Networking Center, Universitat Politècnica de Catalunya, Barcelona, Spain and Universitat Politècnica de Catalunya, Barcelona, Spain

2. Barcelona Neural Networking Center, Universitat Politècnica de Catalunya, Barcelona, Spain and AGH University of Science and Technology, Department of Telecommunications, Krakow, Poland

3. Universitat Politècnica de Catalunya, Barcelona, Spain

Publisher

ACM Press

Reference8 articles.

1. [n. d.]. Project Jupyter. https://jupyter.org/. Accessed: 2019-07-28.

2. X. Chen, J. Guo, Z. Zhu, R. Proietti, A. Castro, and S.J.B Yoo. 2018. Deep-RMSA: A Deep-Reinforcement-Learning Routing, Modulation and Spectrum Assignment Agent for Elastic Optical Networks. In Optical Fiber Communications Conference and Exposition (OFC).

3. Albert Mestres et al. 2017. Knowledge-defined networking. ACM SIGCOMM Computer Communication Review 47, 3 (2017), 2--10.

4. Albert Mestres, Eduard Alarcón, Yusheng Ji, and Albert Cabellos-Aparicio. 2018. Understanding the Modeling of Computer Network Delays using Neural Networks. In Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks. ACM, 46--52.

5. Krzysztof Rusek, José Suárez-Varela, Albert Mestres, Pere Barlet-Ros, and Albert Cabellos-Aparicio. 2019. In Proceedings of ACM Symposium on SDN Research (SOSR).

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