Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection

arXiv cs.LG / 5/1/2026

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Key Points

The paper introduces C-MTAD-GAT, an unsupervised, context-aware graph-attention model for detecting anomalies in multivariate time series from mobile (telco) networks.
C-MTAD-GAT fuses graph attention with lightweight context embeddings and computes anomaly scores using a deterministic reconstruction head plus a multi-step forecaster.
The method calibrates anomaly-detection thresholds without labels by using validation residuals, keeping the overall pipeline fully unsupervised.
Experiments on the public TELCO dataset show consistent improvements over MTAD-GAT and a Telco-specific DC-VAE baseline, improving both event-level and pointwise F1 while reducing the number of alarms.
The model has been deployed in the core network of a national mobile operator, indicating robustness in real-world industrial conditions.

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.