31 May
2021
31 May
'21
2:53 p.m.
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 <https://aiforgood.itu.int/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
<https://bnn.upc.edu/challenge/gnnet2021>
Registration: 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(a)bnn.upc.edu <mailto:gnnetchallenge@bnn.upc.edu>
=====================================================
[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
<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(a)mail.knowledgedefinednetworking.org
<mailto: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(a)mail.bnn.upc.edu <mailto: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(a)bnn.upc.edu
<mailto:gnnetchallenge@bnn.upc.edu>. Otherwise, we will add all members
by default.
Regards,
José Suárez-Varela
------------------------
Barcelona Neural Networking center (BNN-UPC)
Universitat Politècnica de Catalunya