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Graph neural networks are gaining attention in fifth-generation (5G) corenetwork digital twins, which are data-driven complex systems with numerouscomponents. Analyzing these data can be challenging due to rare failure types,leading to imbalanced classification in multiclass settings. Digital twins of5G networks increasingly employ graph classification as the main method foridentifying failure types. However, the skewed distribution of failureoccurrences is a significant class-imbalance problem that prevents practicalgraph data mining. Previous studies have not sufficiently addressed thiscomplex problem. This paper, proposes class-Fourier GNN (CF-GNN) thatintroduces a class-oriented spectral filtering mechanism to ensure preciseclassification by estimating a unique spectral filter for each class. This workemploys eigenvalue and eigenvector spectral filtering to capture and adapt tovariations in minority classes, ensuring accurate class-specific featurediscrimination, and adept at graph representation learning for complex localstructures among neighbors in an end-to-end setting. The extensive experimentsdemonstrate that the proposed CF-GNN could help create new techniques forenhancing classifiers and investigate the characteristics of the multiclassimbalanced data in a network digital twin system.

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