Subject: [nmrg] Graph Neural Networking challenge 2021 - Creating a Scalable Network Digital Twin

From: Jose Suárez-Varela <[email-masked]>
Date: Thu, 5 Aug 2021 21:06:20 +0200

[ Apologies if you receive multiple copies of this email]

Dear Colleagues,

We are glad to announce the "*Graph Neural Networking challenge 2021*", an
international competition on Graph Neural Networks applied to computer
networks, organized as part of the "ITU AI/ML in 5G Challenge".

Challenge description: Creating a Scalable Network Digital Twin

*Registration is now open and free of charge for all participants (Deadline
Aug 31st).*

Registration link: https://bnn.upc.edu/challenge/gnnet2021/registration

Top-3 teams will have access to the global round of the "ITU AI/ML in 5G
Challenge". This competition attracted over 1,300 participants around the
world in the last edition, and there were attractive cash prizes for
winners.

We provide several resources and tips to easily start working on the
challenge (See “Main Resources”).

Challenge website: https://bnn.upc.edu/challenge/gnnet2021

Please, do not hesitate to contact us for questions or comments.

=====================================================

[Overview]

Graph Neural Networks (GNN) have produced groundbreaking applications in
many fields where data is fundamentally structured as graphs (e.g.,
chemistry, physics, biology, recommender systems). In the field of computer
networks, this new type of neural networks is being rapidly adopted for a
wide variety of use cases, particularly for those involving complex graphs
(e.g., performance modeling, routing optimization, resource allocation in
wireless networks).

The Graph Neural Networking challenge 2021 brings a fundamental limitation
of existing GNNs: their lack of generalization capability to larger graphs.
In order to achieve production-ready GNN-based solutions, we need models
that can be trained in network testbeds of limited size (e.g., at the
vendor’s networking lab), and then be directly ready to operate with
guarantees in real customer networks, which are often much larger in number
of nodes. In this challenge, participants are asked to design GNN-based
models that can be trained on small network scenarios (up to 50 nodes), and
after that scale successfully to larger networks not seen before, up to 300
nodes. Solutions with better scalability properties will be the winners.

[Problem statement]

The goal of this challenge is to create a scalable Network Digital Twin
based on neural networks, which can accurately estimate QoS performance
metrics given a network state snapshot. More in detail, solutions must
predict the per-path mean delay given: (i) a network topology, (ii) a
traffic matrix, and (iii) a routing configuration.

As a baseline, we provide RouteNet, a GNN model recently proposed to
estimate end-to-end performance metrics (e.g., delay, jitter, loss) in
networks. However, RouteNet does not scale well to networks considerably
larger than those observed during the training phase. As a result, it does
not perform well when applied to this challenge.

Participants are encouraged to update RouteNet or submit their own neural
network models.

[Main resources]

- Summary slides (with some tips for participants)

- Baseline GNN model: Open source implementation of RouteNet, including a
tutorial on how to use it and modify fundamental characteristics of the
model

- Training/validation datasets

- Python API to easily read and process the datasets

- Mailing list for participants and people interested (Link to subscribe:
https://mail.bnn.upc.edu/cgi-bin/mailman/listinfo/challenge2021).

[Timeline]

* Registration open to participants: *Deadline Aug 31st*
* Evaluation phase: Sep 16th
* Winners (top 3) official announcement: Oct 31st
* Final awards and presentation: Dec 2021

Best regards,

José Suárez-Varela

------------------------

Postdoctoral researcher

Barcelona Neural Networking center (BNN-UPC)

Universitat Politècnica de Catalunya

Contact: [email-masked]