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

TL;DR

This paper discusses the organization and outcomes of the Graph Neural Networking Challenge 2020, a global competition aimed at advancing education and research in applying graph neural networks to network problems.

Contribution

It provides a detailed account of the challenge's setup, participant engagement, top solutions, and lessons learned, contributing valuable educational resources for ML in networks.

Findings

Over 1300 participants from 60+ countries engaged in the challenge.

The top solutions demonstrated innovative applications of graph neural networks.

The challenge generated open educational resources for ML in network contexts.

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.