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.

中文翻译：

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图神经网络挑战：全球人工智能/机器学习网络教育竞赛

在过去的十年中，机器学习 (ML) 越来越成为计算机网络领域的热门话题，并有望在实际部署中逐渐用于大量控制、监控和管理任务。这就需要依靠在 ML 应用于网络方面具有扎实背景的新一代学生、研究人员和从业者。2020 年，国际电信联盟 (ITU) 组织了“ITU AI/ML in 5G 挑战赛”，这是一项公开的全球竞赛，向广大观众介绍了网络 ML 当前的一些主要挑战。该倡议汇集了网络运营商、设备制造商和学术界提出的 23 项不同挑战，吸引了来自 60 多个国家的 1300 多名参与者。以及在整个旅程中学到的一些经验教训的总结。因此，这一挑战留下了一组精心策划的教育资源，可供对该主题感兴趣的任何人公开使用。以及在整个旅程中学到的一些经验教训的总结。因此，这一挑战留下了一组精心策划的教育资源，可供对该主题感兴趣的任何人公开使用。