Graph Neural Networking challenge 2023: Building a Network Digital Twin using data from real networks
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In recent years, the networking community has produced robust Graph Neural Networks (GNN) that can accurately mimic complex network environments. Modern GNN architectures enable building lightweight and accurate Network Digital Twins that can operate in real-time. However, and as a consequence of the lack of real-world data, current ML-based models have been mainly developed and trained using simulated data. This has strongly limited our understanding on how existing models perform in a real network.
This talk will introduce the Graph Neural Networking challenge 2023, entitled “Building a Network Digital Twin using data from real networks”. In this problem statement, participants will be challenged to develop, for the first time ever, a GNN-based Network Digital Twin using a dataset from a real-network. In addition, we will provide key insights on how to approach the challenge.