  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.
