Graph Neural Networking Challenge

UPDATE: The Graph Neural Networking Challenge 2023: Creating a Network Digital Twin with Real Network Data​ is now open! Find you can download the repository and start the challenge here!

Organized as part of "ITU AI/ML in 5G challenge"

Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management. Early works using GNN have demonstrated an unprecedented capability to learn from different network characteristics that are fundamentally represented as graphs, such as the topology, the routing configuration, or the traffic that flows along a series of nodes in the network. In contrast to previous ML-based solutions, GNN enables to produce accurate predictions even in networks unseen during the training phase. Nowadays, GNN is a hot topic in the Machine Learning field and, as such, we are witnessing great efforts to leverage its potential in many different fields (e.g., chemistry, physics, social networks). The Graph Neural Networking challenge is an annual competition that brings fundamental challenges on the application of GNN to networking applications. Check out all the editions:

• Graph Neural Networking challenge 2023: Creating a Network Digital Twin with Real Network Data​
  • GNN Baseline and data preprocessing
• Graph Neural Networking challenge 2022: Improving Network Digital Twins through Data-centric AI
  • GNN Model and notebooks
• Graph Neural Networking challenge 2021: Creating a Scalable Network Digital Twin
  • iGNNition Baseline
  • Tensorflow Baseline
• Graph Neural Networking challenge 2020: Modeling QoS-aware queue scheduling policies at networks
  • Tensorflow Baseline

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

Credits

This project would not have been possible without the contribution of:

• Miquel Ferriol-Galmés - Barcelona Neural Networking center, Universitat Politècnica de Catalunya
• Jose Suárez-Varela - Barcelona Neural Networking center, Universitat Politècnica de Catalunya
• David Pujol Perich - Barcelona Neural Networking center, Universitat Politècnica de Catalunya
• Krzysztof Rusek - Barcelona Neural Networking center, AGH University of Science and Technology
• Albert López - Barcelona Neural Networking center, Universitat Politècnica de Catalunya
• Paul Almasan - Barcelona Neural Networking center, Universitat Politècnica de Catalunya
• Adrián Manco Sánchez - Barcelona Neural Networking center, Universitat Politècnica de Catalunya
• Víctor Sendino Garcia - Barcelona Neural Networking center, Universitat Politècnica de Catalunya
• Pere Barlet Ros - Barcelona Neural Networking center, Universitat Politècnica de Catalunya
• Albert Cabellos Aparicio - Barcelona Neural Networking center, Universitat Politècnica de Catalunya

Mailing List

If you have any doubts, or want to discuss anything related to this repository, you can send an email to the mailing list [email protected]). Please, note that you need to subscribe to the mailing list before sending an email link.

License

See LICENSE for full of the license text.

Copyright Copyright 2021 Universitat Politècnica de Catalunya

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

  http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.