# Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin

arXiv: https://arxiv.org/abs/2502.11505 · HTML: https://mlsanity.com/paper/2502.11505/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 57 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 23 of 23)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [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)
- [GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks](https://mlsanity.com/paper/2103.08826/) (2021)
- [Data Augmentation for Deep Graph Learning: A Survey](https://mlsanity.com/paper/2202.08235/) (2022)
- [FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective](https://mlsanity.com/paper/2311.06190/) (2023)
- [Gradient Reweighting: Towards Imbalanced Class-Incremental Learning](https://mlsanity.com/paper/2402.18528/) (2024)
- [Deriving Neural Architectures from Sequence and Graph Kernels](https://mlsanity.com/paper/1705.09037/) (2017)
- [Node-oriented Spectral Filtering for Graph Neural Networks](https://mlsanity.com/paper/2212.03654/) (2022)
- [RouteNet-Fermi: Network Modeling with Graph Neural Networks](https://mlsanity.com/paper/2212.12070/) (2022)
- [Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection](https://mlsanity.com/paper/2303.17486/) (2023)
- [Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification](https://mlsanity.com/paper/2105.11625/) (2021)
- [Graph Neural Networks with Diverse Spectral Filtering](https://mlsanity.com/paper/2312.09041/) (2023)
- [Multi-dimensional Graph Fourier Transform](https://mlsanity.com/paper/1712.07811/) (2017)
- [GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection](https://mlsanity.com/paper/2303.17334/) (2023)
- [Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering](https://mlsanity.com/paper/2401.09071/) (2024)
- [5G Applications: Requirements, Challenges, and Outlook](https://mlsanity.com/paper/1810.06057/) (2018)

## Abstract (arXiv)

Graph neural networks are gaining attention in fifth-generation (5G) core network digital twins, which are data-driven complex systems with numerous components. Analyzing these data can be challenging due to rare failure types, leading to imbalanced classification in multiclass settings. Digital twins of 5G networks increasingly employ graph classification as the main method for identifying failure types. However, the skewed distribution of failure occurrences is a significant class-imbalance problem that prevents practical graph data mining. Previous studies have not sufficiently addressed this complex problem. This paper, proposes class-Fourier GNN (CF-GNN) that introduces a class-oriented spectral filtering mechanism to ensure precise classification by estimating a unique spectral filter for each class. This work employs eigenvalue and eigenvector spectral filtering to capture and adapt to variations in minority classes, ensuring accurate class-specific feature discrimination, and adept at graph representation learning for complex local structures among neighbors in an end-to-end setting. The extensive experiments demonstrate that the proposed CF-GNN could help create new techniques for enhancing classifiers and investigate the characteristics of the multiclass imbalanced data in a network digital twin system.