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

TL;DR

This paper introduces CF-GNN, a spectral filtering-based graph neural network designed to improve imbalance classification in network digital twins, specifically for 5G core network failure detection.

Contribution

It proposes a class-oriented spectral filtering mechanism in GNNs to better handle class imbalance in multiclass graph classification tasks.

Findings

CF-GNN improves classification accuracy on imbalanced network data

Spectral filtering captures class-specific features effectively

Enhanced minority class detection in digital twin systems

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 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.