{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-graph-neural-networking-challenge-a","title":"The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks","arxiv_id":"2107.12433","date":"2021-07-26","proceeding":null,"authors":["José Suárez-Varela","Miquel Ferriol-Galmés","Albert López","Paul Almasan","Guillermo Bernárdez","David Pujol-Perich","Krzysztof Rusek","Loïck Bonniot","Christoph Neumann","François Schnitzler","François Taïani","Martin Happ","Christian Maier","Jia Lei Du","Matthias Herlich","Peter Dorfinger","Nick Vincent Hainke","Stefan Venz","Johannes Wegener","Henrike Wissing","Bo Wu","Shihan Xiao","Pere Barlet-Ros","Albert Cabellos-Aparicio"],"abstract":"During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the \"ITU AI/ML in 5G challenge'', an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the \"Graph Neural Networking Challenge 2020''. We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.","url_abs":"https://arxiv.org/abs/2107.12433v1","url_pdf":"https://arxiv.org/pdf/2107.12433v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-graph-neural-networking-challenge-a","repo_url":"https://github.com/ITU-AI-ML-in-5G-Challenge/PS-014.2_GNN_Challenge_SalzburgResearch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}