  Graph neural networks (GNNs) are popular to use for classifying structured
data in the context of machine learning. But surprisingly, they are rarely
applied to regression problems. In this work, we adopt GNN for a classic but
challenging nonlinear regression problem, namely the network localization. Our
main findings are in order. First, GNN is potentially the best solution to
large-scale network localization in terms of accuracy, robustness and
computational time. Second, proper thresholding of the communication range is
essential to its superior performance. Simulation results corroborate that the
proposed GNN based method outperforms all state-of-the-art benchmarks by far.
Such inspiring results are theoretically justified in terms of data
aggregation, non-line-of-sight (NLOS) noise removal and low-pass filtering
effect, all affected by the threshold for neighbor selection. Code is available
at https://github.com/Yanzongzi/GNN-For-localization.
