mlsanityData to 7 Oct 2026

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

arXiv 2107.12433 ↗First posted 26 Jul 2021cs.NI cs.AI cs.GLcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 38 references that are in the map. First posted on arXiv 26 Jul 2021.

Builds on

13 of 38 references.

References · 13

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
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1 more
  1. 2017Machine Learning for Networking: Workflow, Advances and Opportunitiescited by 20

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 0; 2026: 1.

5 papers.

  1. 2022QT-Routenet: Improved GNN generalization to larger 5G networks by fine-tuning predictions from queueing theorycited by 2
  2. 2022Analyzing the Traffic of MANETs using Graph Neural Networkscited by 1
  3. 2023Low Complexity Adaptive Machine Learning Approaches for End-to-End Latency Predictioncited by 0
  4. 2023Low Complexity Approaches for End-to-End Latency Predictioncited by 0
  5. 2026From Simulation to Deep Learning: Survey on Network Performance Modeling Approachescited by 0