# Graph Neural Network for Large-Scale Network Localization

arXiv: https://arxiv.org/abs/2010.11653 · HTML: https://mlsanity.com/paper/2010.11653/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [Graph-based Deep Learning for Communication Networks: A Survey](https://mlsanity.com/paper/2106.02533/) (2021)
- [Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks](https://mlsanity.com/paper/2108.13178/) (2021)
- [Space-Time Graph Neural Networks with Stochastic Graph Perturbations](https://mlsanity.com/paper/2210.16270/) (2022)
- [Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization](https://mlsanity.com/paper/2311.16856/) (2023)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks](https://mlsanity.com/paper/2301.11166/) (2023)
- [MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion](https://mlsanity.com/paper/2411.17781/) (2024)
- [UBiGTLoc: A Unified BiLSTM-Graph Transformer Localization Framework for IoT Sensor Networks](https://mlsanity.com/paper/2601.10743/) (2026)
- [Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints](https://mlsanity.com/paper/2504.04829/) (2025)
- [Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios](https://mlsanity.com/paper/2511.14076/) (2025)
- [Mobility-Induced Graph Learning for WiFi Positioning](https://mlsanity.com/paper/2311.08271/) (2023)

## Abstract (arXiv)

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.