The graph neural networking challenge
- 11 July 2021
- journal article
- research article
- Published by Association for Computing Machinery (ACM) in ACM SIGCOMM Computer Communication Review
- Vol. 51 (3), 9-16
- https://doi.org/10.1145/3477482.3477485
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.Keywords
This publication has 18 references indexed in Scilit:
- A Survey on Deep LearningACM Computing Surveys, 2018
- Learning and Generating Distributed Routing Protocols Using Graph-Based Deep LearningPublished by Association for Computing Machinery (ACM) ,2018
- A comprehensive survey on machine learning for networking: evolution, applications and research opportunitiesJournal of Internet Services and Applications, 2018
- Experience-driven Networking: A Deep Reinforcement Learning based ApproachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Machine Learning for Networking: Workflow, Advances and OpportunitiesIEEE Network, 2017
- Expect the unexpected: Sub-second optimization for segment routingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Perspectives on network calculusACM SIGCOMM Computer Communication Review, 2012
- ImageNet: A large-scale hierarchical image databasePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- The Graph Neural Network ModelIEEE Transactions on Neural Networks, 2008
- AN OVERVIEW OF THE OMNeT++ SIMULATION ENVIRONMENTPublished by European Alliance for Innovation n.o. ,2008