Network softwarization, which shifts hardware-centric functions to software implementations, is essential for enhancing the agility of cellular and non-cellular wireless networks. This change, while ...raising reliability concerns, also improves system monitoring through digital twins. One example is the Digital Twin Edge Networks (DITEN), which enhances real-time analysis and quick anomaly response in the unpredictable last-mile wireless edge network environment. Positioned close to the physical infrastructure, DITEN is effective in rapidly detecting and addressing network irregularities. This study presents an advanced anomaly detection method for DITEN, employing time-series data conversion to Visibility Graph (VG) and utilising Graph Neural Network (GNN), with a focus on addressing disruptions at the network’s physical layer. Our proposed method outperforms the State-of-the-art (SOTA) time series Deep Learning (DL) classification architecture by 13 percentage points and achieves ≈110 times higher computational efficiency. Furthermore, our method surpasses the classical Machine Learning (ML) model Hive-Cote2 by 2.2 percentage points while maintaining ≈5.9 times better computational efficiency. The model also outperforms the current best SOTA imaging model by up to 6 percentage points and the leading graph-based method by up to 10 percentage points, both with significantly lower Computational Complexity (CC) of ≈210-times and ≈4-times, respectively. Additionally, we show that when 1000 concurrent requests arrive, the proposed method achieves a mean response latency of less than or equal to 60 seconds across three setups. Finally, we demonstrate that the combination of Natural Visibility Graph (NVG) and the proposed GNN model provides interpretable insights by observing gradient changes.
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