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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.27172 |
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| _version_ | 1866909001608855552 |
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| author | Malacarne, Sara Hoel-Høiseth, Eirik Aune, Erlend Biro, David Zsolt Ruocco, Massimiliano |
| author_facet | Malacarne, Sara Hoel-Høiseth, Eirik Aune, Erlend Biro, David Zsolt Ruocco, Massimiliano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27172 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection Malacarne, Sara Hoel-Høiseth, Eirik Aune, Erlend Biro, David Zsolt Ruocco, Massimiliano Machine Learning 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. |
| title | Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.27172 |