The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks

• URL: http://arxiv.org/abs/2107.12433v1
• Date: Mon, 26 Jul 2021 18:52:00 GMT
• Title: The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks
• Authors: Jos\'e Su\'arez-Varela, Miquel Ferriol-Galm\'es, Albert L\'opez, Paul Almasan, Guillermo Bern\'ardez, David Pujol-Perich, Krzysztof Rusek, Lo\"ick Bonniot, Christoph Neumann, Fran\c{c}ois Schnitzler, Fran\c{c}ois Ta\"iani, 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 summary: The International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge'', an open global competition. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020'' As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.
• Score: 3.312457624805193
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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