Dear Colleagues,

We are glad to announce the second edition of the "Graph Neural Networking challenge", organized as part of the "ITU AI/ML in 5G Challenge".

Challenge description: Creating a Scalable Network Digital Twin

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

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.

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

Website: https://bnn.upc.edu/challenge/gnnet2021

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

If you have any questions or comments, please do not hesitate to contact us at: gnnetchallenge@bnn.upc.edu

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[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

Important notice: You have received this email because you are subscribed to the challenge-kdn@mail.knowledgedefinednetworking.org mailing list. From June 15^th this list will be removed, and all members will be moved to a new mailing list related to the Graph Neural Networking challenge: challenges@mail.bnn.upc.edu

Please, let us know if you do not want to be included in this new list before June 15^th by sending an email to gnnetchallenge@bnn.upc.edu. Otherwise, we will add all members by default.

Regards,

José Suárez-Varela

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Barcelona Neural Networking center (BNN-UPC)

Universitat Politècnica de Catalunya