Data-efficient GNN models of communication networks using beta-distribution-based sample ranking

Machine learning models for tasks in communication networks often require large datasets to be trained. This training is cost intensive, and solutions to reduce these costs are required. It is not clear what the best approach to solve this problem is. Here we show an approach that is able to create a minimally-sized training dataset while maintaining high predictive power of the model. We apply our approach to a state-of-the-art graph neural network model for performance prediction in communication networks. Our approach is limited to a dataset of 100 samples with reduced sizes and achieves an MAPE of 9.79% on a test dataset containing significantly larger problem sizes, compared to a baseline approach which achieved an MAPE of 37.82%. We think this approach can be useful to create high-quality datasets of communication networks and decrease the time needed to train graph neural network models on performance prediction tasks.