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 this https URL

本研究采用图神经网络（GNN）解决经典的非线性回归问题 —— 网络定位，并发现 GNN 在精度、鲁棒性和计算时间方面潜在成为大规模网络定位的最佳解决方案，适当的通信范围阈值对其高性能至关重要。此外，本研究提出的基于 GNN 的方法通过数据聚合、非线性可见性去噪和低通滤波等技术使其在邻居选择阈值上的优化成为可能。仿真结果表明，该方法远优于先前的最新基准。