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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. 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:. If you have any doubts, or want to discuss anything related to this repository, you can send an email to the mailing list challenge bnn. Please, note that you need to subscribe to the mailing list before sending an email [ link ]. Skip to content. Notifications Fork 39 Star Branches Tags.
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