The graph neural networking challenge

IF 2.2 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
J. Suárez-Varela, Miquel Ferriol Galmés, Albert Lopez, Paul Almasan, G. Bernárdez, David Pujol-Perich, Krzysztof Rusek, Loïck Bonniot, C. Neumann, François Schnitzler, François Taïani, Martin Happ, Christian Maier, J. Du, Matthias Herlich, P. Dorfinger, N. Hainke, Stefan Venz, John A. Wegener, H. Wissing, Bo-Xi Wu, Shihan Xiao, P. Barlet-Ros, A. Cabellos-Aparicio
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引用次数: 15

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.
图神经网络的挑战
在过去的十年中,机器学习(ML)日益成为计算机网络领域的热门话题,并有望逐渐被用于实际部署中的大量控制、监控和管理任务。这就需要依靠新一代的学生、研究人员和实践者,他们在机器学习应用于网络方面有着坚实的背景。2020年期间,国际电信联盟(ITU)组织了“国际电联5G AI/ML挑战赛”,这是一项开放的全球竞赛,向广大受众介绍了当前网络ML面临的一些主要挑战。这一大型倡议汇集了网络运营商、设备制造商和学术界提出的23个不同挑战,吸引了来自60多个国家的1300多名参与者。本文叙述了我们组织提出的挑战之一的经验:“图神经网络挑战2020”。我们描述了向参与者提出的问题、提供的工具和资源、一些组织方面和参与统计数据、前3名获奖解决方案的大纲,以及在整个过程中获得的一些经验教训的总结。因此,这一挑战为任何对该主题感兴趣的人留下了一套开放的教育资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Sigcomm Computer Communication Review
ACM Sigcomm Computer Communication Review 工程技术-计算机:信息系统
CiteScore
6.90
自引率
3.60%
发文量
20
审稿时长
4-8 weeks
期刊介绍: Computer Communication Review (CCR) is an online publication of the ACM Special Interest Group on Data Communication (SIGCOMM) and publishes articles on topics within the SIG''s field of interest. Technical papers accepted to CCR typically report on practical advances or the practical applications of theoretical advances. CCR serves as a forum for interesting and novel ideas at an early stage in their development. The focus is on timely dissemination of new ideas that may help trigger additional investigations. While the innovation and timeliness are the major criteria for its acceptance, technical robustness and readability will also be considered in the review process. We particularly encourage papers with early evaluation or feasibility studies.
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