Graph Neural Networking 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.

Check out all the editions:

• Graph Neural Networking challenge 2023: Creating a Network Digital Twin with Real Network Data
• Graph Neural Networking challenge 2022: Improving Network Digital Twins through Data-centric AI
• Graph Neural Networking challenge 2021: Creating a Scalable Network Digital Twin
• Graph Neural Networking challenge 2020: Modeling more realistic networks with QoS-aware queue scheduling policies

Stay tuned on new editions:

• Subscribe to the challenge mailing list [here]
• Follow the official social media channels of the Barcelona Neural Networking center:
  • Linkedin [here]
  • Twitter [here]

Please, use the citation below to refer to this competition.

Plain text (IEEE format):

J. Suárez-Varela, et al., “The Graph Neural Networking challenge: A world-wide competition for education in AI/ML for networks,” ACM SIGCOMM Computer Communication Review, vol. 51, no. 3, pp. 9–16, 2021.

BibTEX:

@article{suarez2021graph,
  title={The graph neural networking challenge: a worldwide competition for education in AI/ML for networks},
  author={Su{\'a}rez-Varela, Jos{\'e} and others},
  journal={ACM SIGCOMM Computer Communication Review},
  volume={51},
  number={3},
  pages={9--16},
  year={2021}
}