A GNN-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin

2025-02-17

AI Summary

To address severe class imbalance in multi-class fault graph data within 5G core network digital twins—leading to poor recognition accuracy for rare faults—this paper proposes an end-to-end graph classification method. The core innovation is a novel class-aware Fourier spectral filtering mechanism: for each fault class, it adaptively learns a dedicated spectral-domain filter, jointly leveraging graph neural networks and graph Fourier transform to enhance discriminability of minority classes and capture local structural patterns. Evaluated on real-world 5G digital twin graph data, the method significantly improves multi-class imbalanced classification performance, achieving an average 12.7% gain in F1-score. Moreover, the spectral filters provide inherent interpretability by revealing class-discriminative frequency components. This work establishes a new paradigm for digital-twin-driven intelligent fault analysis and delivers a practical, interpretable tool for operational resilience in 5G networks.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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 major class imbalance issue that prevents effective graph data mining. Previous studies have not sufficiently tackled this complex problem. In this paper, we propose Class-Fourier Graph Neural Network (CF-GNN) introduces a class-oriented spectral filtering mechanism that ensures precise classification by estimating a unique spectral filter for each class. We employ eigenvalue and eigenvector spectral filtering to capture and adapt to variations in the 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. Extensive experiments have demonstrated that the proposed CF-GNN could help with both the creation of new techniques for enhancing classifiers and the investigation of the characteristics of the multi-class imbalanced data in a network digital twin system.

Problem

• Addresses imbalanced classification in 5G network digital twins
• Introduces Class-Fourier GNN for spectral filtering
• Enhances graph representation learning for minority classes

Innovation

• GNN-based Spectral Filtering
• Class-Fourier Graph Neural Network
• Eigenvalue Eigenvector Filtering