Graph Neural Networking Challenges

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:

Please, use the following citation 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}
}