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Challenging the generalization capabilities of Graph Neural Networks for network modeling
dc.contributor.author | Suárez-Varela Maciá, José Rafael |
dc.contributor.author | Carol Bosch, Sergi |
dc.contributor.author | Rusek, Krzysztof |
dc.contributor.author | Almasan Puscas, Felician Paul |
dc.contributor.author | Arias Vicente, Marta |
dc.contributor.author | Barlet Ros, Pere |
dc.contributor.author | Cabellos Aparicio, Alberto |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.date.accessioned | 2020-06-09T09:22:21Z |
dc.date.available | 2020-06-09T09:22:21Z |
dc.date.issued | 2019 |
dc.identifier.citation | Suárez-varela, J. [et al.]. Challenging the generalization capabilities of Graph Neural Networks for network modeling. A: ACM Conference on Special Interest Group on Data Communication. "SIGCOMM’19: proceedings of the 2019 ACM SIGCOMM Conference posters and demos: August 19-23, 2019, Beijing, China". New York: Association for Computing Machinery (ACM), 2019, p. 114-115. |
dc.identifier.isbn | 978-1-4503-6886-5 |
dc.identifier.uri | http://hdl.handle.net/2117/190287 |
dc.description.abstract | Today, network operators still lack functional network models able to make accurate predictions of end-to-end Key Performance Indicators (e.g., delay or jitter) at limited cost. Recently a novel Graph Neural Network (GNN) model called RouteNet was proposed as a cost-effective alternative to estimate the per-source/destination pair mean delay and jitter in networks. Thanks to its GNN architecture that operates over graph-structured data, RouteNet revealed an unprecedented ability to learn and model the complex relationships among topology, routing and input traffic in networks. As a result, it was able to make performance predictions with similar accuracy than resource-hungry packet-level simulators even in network scenarios unseen during training. In this demo, we will challenge the generalization capabilities of RouteNet with more complex scenarios, including larger topologies. |
dc.description.sponsorship | This work was supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE), the Catalan Institution for Research and Advanced Studies (ICREA) and the AGH University of Science and Technology grant, under contract no. 15.11.230.400. The research was also supported in part by PL-Grid Infrastructure. |
dc.format.extent | 2 p. |
dc.language.iso | eng |
dc.publisher | Association for Computing Machinery (ACM) |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Telecommunication -- Traffic |
dc.subject.other | Graph Neural Networks |
dc.subject.other | Network modeling |
dc.title | Challenging the generalization capabilities of Graph Neural Networks for network modeling |
dc.type | Conference report |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Telecomunicació -- Tràfic |
dc.contributor.group | Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla |
dc.contributor.group | Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
dc.identifier.doi | 10.1145/3342280.3342327 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://dl.acm.org/doi/10.1145/3342280.3342327 |
dc.rights.access | Open Access |
local.identifier.drac | 28609977 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2017-90034-C2-1-R/ES/DISEÑANDO UNA INFRAESTRUCTURA DE RED 5G DEFINIDA MEDIANTE CONOCIMIENTO HACIA LA PROXIMA SOCIEDAD DIGITAL/ |
local.citation.author | Suárez-varela, J.; Carol, S.; Rusek, K.; Almasan, F.; Arias, M.; Barlet, P.; Cabellos-Aparicio, A. |
local.citation.contributor | ACM Conference on Special Interest Group on Data Communication |
local.citation.pubplace | New York |
local.citation.publicationName | SIGCOMM’19: proceedings of the 2019 ACM SIGCOMM Conference posters and demos: August 19-23, 2019, Beijing, China |
local.citation.startingPage | 114 |
local.citation.endingPage | 115 |