Graph neural networks for communication networks: context, use cases and opportunities

Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured...

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Authors: Suárez-Varela Maciá, José Rafael|||0000-0002-7141-3414, Almasan Puscas, Felician Paul|||0000-0003-3903-6759, Ferriol Galmés, Miquel|||0000-0002-7806-2979, Rusek, Krzysztof, Geyer, Fabien, Cheng, Xiangle, Shi, Xiang, Xiao, Shihan, Scarselli, Franco, Cabellos Aparicio, Alberto|||0000-0001-9329-7584, Barlet Ros, Pere|||0000-0001-7837-0886
Format: article
Publication Date:2023
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/385051
Online Access:https://hdl.handle.net/2117/385051
https://dx.doi.org/10.1109/MNET.123.2100773
Access Level:Open access
Keyword:Neural networks (Computer science)
Artificial neural networks
Data models
Computational modeling
Communication networks
Network topology
Biological system modeling
Training
Xarxes neuronals (Informàtica)
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
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oai_identifier_str oai:upcommons.upc.edu:2117/385051
network_acronym_str ES
network_name_str España
repository_id_str
spelling Graph neural networks for communication networks: context, use cases and opportunitiesSuárez-Varela Maciá, José Rafael|||0000-0002-7141-3414Almasan Puscas, Felician Paul|||0000-0003-3903-6759Ferriol Galmés, Miquel|||0000-0002-7806-2979Rusek, KrzysztofGeyer, FabienCheng, XiangleShi, XiangXiao, ShihanScarselli, FrancoCabellos Aparicio, Alberto|||0000-0001-9329-7584Barlet Ros, Pere|||0000-0001-7837-0886Neural networks (Computer science)Artificial neural networksData modelsComputational modelingCommunication networksNetwork topologyBiological system modelingTrainingXarxes neuronals (Informàtica)Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificialÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadorsGraph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g., topology, routing, signal interference). This position article presents GNNs as a fundamental tool for modeling, control and management of communication networks. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real-world networks. As a result, these models can be applied to a wide variety of networking use cases, such as planning, online optimization, or troubleshooting. The main advantage of GNNs over traditional neural networks lies in their unprecedented generalization capabilities when applied to other networks and configurations unseen during training. This is a critical feature for achieving practical data-driven solutions for networking. This article starts with a brief tutorial on GNNs and some potential applications to communication networks. Then, it presents two state-of-the-art GNN models respectively applied to wired and wireless networks. Lastly, it delves into the key open challenges and opportunities yet to be explored in this novel research area.This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund.Peer Reviewed20232023-05-0120232023-03-16journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/385051https://dx.doi.org/10.1109/MNET.123.2100773reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-118011GB-C21 INVESTIGACION EN FUTURAS REDES TOTALMENTE OPTIMIZADAS MEDIANTE INTELIGENCIA ARTIFICIAL - Aopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3850512026-05-27T15:37:01Z
dc.title.none.fl_str_mv Graph neural networks for communication networks: context, use cases and opportunities
title Graph neural networks for communication networks: context, use cases and opportunities
spellingShingle Graph neural networks for communication networks: context, use cases and opportunities
Suárez-Varela Maciá, José Rafael|||0000-0002-7141-3414
Neural networks (Computer science)
Artificial neural networks
Data models
Computational modeling
Communication networks
Network topology
Biological system modeling
Training
Xarxes neuronals (Informàtica)
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
title_short Graph neural networks for communication networks: context, use cases and opportunities
title_full Graph neural networks for communication networks: context, use cases and opportunities
title_fullStr Graph neural networks for communication networks: context, use cases and opportunities
title_full_unstemmed Graph neural networks for communication networks: context, use cases and opportunities
title_sort Graph neural networks for communication networks: context, use cases and opportunities
dc.creator.none.fl_str_mv Suárez-Varela Maciá, José Rafael|||0000-0002-7141-3414
Almasan Puscas, Felician Paul|||0000-0003-3903-6759
Ferriol Galmés, Miquel|||0000-0002-7806-2979
Rusek, Krzysztof
Geyer, Fabien
Cheng, Xiangle
Shi, Xiang
Xiao, Shihan
Scarselli, Franco
Cabellos Aparicio, Alberto|||0000-0001-9329-7584
Barlet Ros, Pere|||0000-0001-7837-0886
author Suárez-Varela Maciá, José Rafael|||0000-0002-7141-3414
author_facet Suárez-Varela Maciá, José Rafael|||0000-0002-7141-3414
Almasan Puscas, Felician Paul|||0000-0003-3903-6759
Ferriol Galmés, Miquel|||0000-0002-7806-2979
Rusek, Krzysztof
Geyer, Fabien
Cheng, Xiangle
Shi, Xiang
Xiao, Shihan
Scarselli, Franco
Cabellos Aparicio, Alberto|||0000-0001-9329-7584
Barlet Ros, Pere|||0000-0001-7837-0886
author_role author
author2 Almasan Puscas, Felician Paul|||0000-0003-3903-6759
Ferriol Galmés, Miquel|||0000-0002-7806-2979
Rusek, Krzysztof
Geyer, Fabien
Cheng, Xiangle
Shi, Xiang
Xiao, Shihan
Scarselli, Franco
Cabellos Aparicio, Alberto|||0000-0001-9329-7584
Barlet Ros, Pere|||0000-0001-7837-0886
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Neural networks (Computer science)
Artificial neural networks
Data models
Computational modeling
Communication networks
Network topology
Biological system modeling
Training
Xarxes neuronals (Informàtica)
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
topic Neural networks (Computer science)
Artificial neural networks
Data models
Computational modeling
Communication networks
Network topology
Biological system modeling
Training
Xarxes neuronals (Informàtica)
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
description Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g., topology, routing, signal interference). This position article presents GNNs as a fundamental tool for modeling, control and management of communication networks. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real-world networks. As a result, these models can be applied to a wide variety of networking use cases, such as planning, online optimization, or troubleshooting. The main advantage of GNNs over traditional neural networks lies in their unprecedented generalization capabilities when applied to other networks and configurations unseen during training. This is a critical feature for achieving practical data-driven solutions for networking. This article starts with a brief tutorial on GNNs and some potential applications to communication networks. Then, it presents two state-of-the-art GNN models respectively applied to wired and wireless networks. Lastly, it delves into the key open challenges and opportunities yet to be explored in this novel research area.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-05-01
2023
2023-03-16
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/385051
https://dx.doi.org/10.1109/MNET.123.2100773
url https://hdl.handle.net/2117/385051
https://dx.doi.org/10.1109/MNET.123.2100773
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-118011GB-C21 INVESTIGACION EN FUTURAS REDES TOTALMENTE OPTIMIZADAS MEDIANTE INTELIGENCIA ARTIFICIAL - A
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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