[nmrg] Graph Neural Networking challenge 2022 – Improving Network Digital Twins through Data-centric AI

[José Suárez-Varela <[e-mail]>]

Dear Colleagues,

[Apologies if you receive multiple copies of this message]

We are glad to announce the *3rd edition of the Graph Neural Networking
challenge*, the only competition in the world on Graph Neural Networks
applied to networking.

This competition is 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:

[e-mail]





[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 at the link
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