Improving Network Digital Twins through Data-centric AI
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In recent years, the networking community has produced robust Graph Neural Network (GNN) architectures that can accurately mimic complex network environments. Modern GNN-based architectures enable building lightweight and accurate Network Digital Twins that can operate in real time. However, the quality of Machine Learning-based models depends on two main components: the model architecture, and the training dataset. In this context, very little research has been done on the impact of training data on the performance of these network models.
This talk will introduce the Graph Neural Networking challenge 2022, entitled “Improving Network Digital Twins through Data-centric AI”. In this problem statement, participants will be given a state-of-the-art GNN model for network performance evaluation, and an accurate packet-level network simulator to generate training datasets. They will be tasked with producing a training dataset that results in better performance for the target GNN model.
This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.