Abubakar Isah

Abubakar Isah, Ibrahim Aliyu, Sulaiman Muhammad Rashid et al.

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.

Abubakar Isah, Ibrahim Aliyu, Jaechan Shim et al.

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.