Dear Colleagues, [As you registered in previous editions of the Graph Neural Networking challenge, we believe this information may be of your interest] We are glad to announce the *3rd edition of the Graph Neural Networking challenge*, organized as part of the "ITU AI/ML in 5G Challenge". *Title:* Improving Network Digital Twins through Data-centric AI *Website*: https://bnn.upc.edu/challenge/gnnet2022 *Registration is now open and free of charge for all participants *(use the link below). Registration form: https://bnn.upc.edu/challenge/gnnet2022/registration Please contact us at the following email if you have any questions or comments: gnnetchallenge(a)bnn.upc.edu [INCENTIVES AND AWARDS] ======================= This year we will organize the *1st GNNet workshop*, *co-located with ACM CoNEXT* (December 2022). Top teams will be invited to present their solutions there. Please, find more details about this workshop below: https://bnn.upc.edu/workshops/gnnet2022 The winning team of the Graph Neural Networking challenge will receive a* cash prize of 1,000 CHF,* if the Judges Panel from the ITU AI/ML in 5G Challenge determines that the solution satisfies the judging criteria. Also, *top-3 teams* *will advance to the Grand Challenge Finale* of the "ITU AI/ML in 5G Challenge <https://aiforgood.itu.int/ai-ml-in-5g-challenge/>". Winners of the finale will receive the following prizes: · 1st prize: 3,000 CHF · Runner-up: 2,000 CHF [OVERVIEW] ========== In recent years, the networking community has produced robust Graph Neural Networks (GNN) that can accurately mimic complex network environments. Modern GNN architectures enable building lightweight and accurate Network Digital Twins that can operate in real time. However, the quality of ML-based models depends on two main components: the model architecture, and the training dataset. In this context, very little research has been done on the impact of training data on the performance of network models. The 3rd edition of the Graph Neural Networking challenge focuses on a fundamental problem of current ML-based solutions applied to networking: how to generate a good dataset. We invert the format of traditional ML competitions, which follow a model-centric approach. Instead, we propose to explore a data-centric approach for building accurate Network Digital Twins. [PROBLEM STATEMENT] =================== Participants will be given a state-of-the-art GNN model for network performance evaluation (RouteNet-Fermi), and a packet-level network simulator to generate datasets. They will be tasked with producing a training dataset that results in better performance for the target GNN model. [TIMELINE] ================ * Open registration: until Sep 30th 2022 * Evaluation phase: Oct 1st-Oct 15th 2022 * Final ranking and official announcement of top-3 teams: Nov 2022 * Award ceremony and presentations: Dec 2022 Best regards, José Suárez-Varela ------------------------ Postdoctoral Researcher Barcelona Neural Networking center (BNN-UPC) Universitat Politècnica de Catalunya