Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection

• URL: http://arxiv.org/abs/2604.27172v1
• Date: Wed, 29 Apr 2026 20:20:04 GMT
• Title: Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection
• Abstract summary: C-MTAD-GAT is an emphunsupervised, emphcontext-aware graph-attention model for anomaly detection from mobile networks.<n>On the public TELCO dataset, C-MTAD-GAT consistently outperforms MTAD-GAT and the Telco-specific DC-VAE, in both event-level and pointwise F1.<n>C-MTAD-GAT is also deployed in the Core network of a national mobile operator, demonstrating its resilience in real industrial settings.
• Score: 0.5219568203653523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose C-MTAD-GAT, an \emph{unsupervised}, \emph{context-aware} graph-attention model for anomaly detection in multivariate time series from mobile networks. C-MTAD-GAT combines graph attention with lightweight context embeddings, and uses a deterministic reconstruction head and multi-step forecaster to produce anomaly scores. Detection thresholds are calibrated \emph{without labels} from validation residuals, keeping the pipeline fully unsupervised. On the public TELCO dataset, C-MTAD-GAT consistently outperforms MTAD-GAT and the Telco-specific DC-VAE, two state-of-the-art baselines, in both event-level and pointwise F1, while triggering substantially fewer alarms. C-MTAD-GAT is also deployed in the Core network of a national mobile operator, demonstrating its resilience in real industrial settings.

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