# Beyond 5G Network Failure Classification for Network Digital Twin Using Graph Neural Network

arXiv: https://arxiv.org/abs/2406.06595 · HTML: https://mlsanity.com/paper/2406.06595/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 68 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 26 of 26)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Diffusion-Convolutional Neural Networks](https://mlsanity.com/paper/1511.02136/) (2015)
- [Real Time Image Saliency for Black Box Classifiers](https://mlsanity.com/paper/1705.07857/) (2017)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks](https://mlsanity.com/paper/2103.08826/) (2021)
- [Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection](https://mlsanity.com/paper/2005.00625/) (2020)
- [XFake: Explainable Fake News Detector with Visualizations](https://mlsanity.com/paper/1907.07757/) (2019)
- [Towards Explanation of DNN-based Prediction with Guided Feature Inversion](https://mlsanity.com/paper/1804.00506/) (2018)
- [Deriving Neural Architectures from Sequence and Graph Kernels](https://mlsanity.com/paper/1705.09037/) (2017)
- [RouteNet-Fermi: Network Modeling with Graph Neural Networks](https://mlsanity.com/paper/2212.12070/) (2022)
- [On Attribution of Recurrent Neural Network Predictions via Additive Decomposition](https://mlsanity.com/paper/1903.11245/) (2019)
- [Multi-dimensional Graph Fourier Transform](https://mlsanity.com/paper/1712.07811/) (2017)
- [Neural Network Based Undersampling Techniques](https://mlsanity.com/paper/1908.06487/) (2019)
- [Omni SCADA Intrusion Detection Using Deep Learning Algorithms](https://mlsanity.com/paper/1908.01974/) (2019)
- [iCapsNets: Towards Interpretable Capsule Networks for Text Classification](https://mlsanity.com/paper/2006.00075/) (2020)
- [5G Applications: Requirements, Challenges, and Outlook](https://mlsanity.com/paper/1810.06057/) (2018)
- [A Data-Driven Digital Twin Network Architecture in the Industrial Internet of Things (IIoT) Applications](https://mlsanity.com/paper/2312.14930/) (2023)

## Abstract (arXiv)

Fifth-generation (5G) core networks in network digital twins (NDTs) are complex systems with numerous components, generating considerable data. Analyzing these data can be challenging due to rare failure types, leading to imbalanced classes in multiclass classification. To address this problem, we propose a novel method of integrating a graph Fourier transform (GFT) into a message-passing neural network (MPNN) designed for NDTs. This approach transforms the data into a graph using the GFT to address class imbalance, whereas the MPNN extracts features and models dependencies between network components. This combined approach identifies failure types in real and simulated NDT environments, demonstrating its potential for accurate failure classification in 5G and beyond (B5G) networks. Moreover, the MPNN is adept at learning complex local structures among neighbors in an end-to-end setting. Extensive experiments have demonstrated that the proposed approach can identify failure types in three multiclass domain datasets at multiple failure points in real networks and NDT environments. The results demonstrate that the proposed GFT-MPNN can accurately classify network failures in B5G networks, especially when employed within NDTs to detect failure types.