# Graph Neural Networks for Jamming Source Localization

arXiv: https://arxiv.org/abs/2506.03196 · HTML: https://mlsanity.com/paper/2506.03196/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 1 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Principal Neighbourhood Aggregation for Graph Nets](https://mlsanity.com/paper/2004.05718/) (2020)
- [Structure-Aware Transformer for Graph Representation Learning](https://mlsanity.com/paper/2202.03036/) (2022)
- [Random Features Strengthen Graph Neural Networks](https://mlsanity.com/paper/2002.03155/) (2020)
- [Nested Graph Neural Networks](https://mlsanity.com/paper/2110.13197/) (2021)
- [Data Augmentation for Deep Graph Learning: A Survey](https://mlsanity.com/paper/2202.08235/) (2022)
- [Everything is Connected: Graph Neural Networks](https://mlsanity.com/paper/2301.08210/) (2023)
- [Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision](https://mlsanity.com/paper/2104.02538/) (2021)
- [Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking](https://mlsanity.com/paper/2106.03772/) (2021)
- [GraphCrop: Subgraph Cropping for Graph Classification](https://mlsanity.com/paper/2009.10564/) (2020)
- [Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.00292/) (2021)
- [Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization](https://mlsanity.com/paper/2008.12578/) (2020)
- [Jamming Source Localization Using Augmented Physics-Based Model](https://mlsanity.com/paper/2212.08097/) (2022)
- [MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion](https://mlsanity.com/paper/2411.17781/) (2024)

## Built on by (most cited first, 1 of 1)

- [Bayesian Jammer Localization with a Hybrid CNN and Path-Loss Mixture of Experts](https://mlsanity.com/paper/2510.20666/) (2025)

## Abstract (arXiv)

Graph-based learning provides a powerful framework for modeling complex relational structures; however, its application within the domain of wireless security remains significantly underexplored. In this work, we introduce the first application of graph-based learning for jamming source localization, addressing the imminent threat of jamming attacks in wireless networks. Unlike geometric optimization techniques that struggle under environmental uncertainties and dense interference, we reformulate the localization as an inductive graph regression task. Our approach integrates structured node representations that encode local and global signal aggregation, ensuring spatial coherence and adaptive signal fusion. To enhance robustness, we incorporate an attention-based acGNN that adaptively refines neighborhood influence and introduces a confidence-guided estimation mechanism that dynamically balances learned predictions with domain-informed priors. We evaluate our approach under complex acRF environments with various sampling densities, network topologies, jammer characteristics, and signal propagation conditions, conducting comprehensive ablation studies on graph construction, feature selection, and pooling strategies. Results demonstrate that our novel graph-based learning framework significantly outperforms established localization baselines, particularly in challenging scenarios with sparse and obfuscated signal information. Our code is available at https://github.com/tiiuae/gnn-jamming-source-localization.