Scalable Context-Aware Graph Attention for Unsupervised Anomaly Detection in Large-Scale Mobile Networks
Fuente:
arXiv
Guardado en:
| Autores principales: | Malacarne, Sara, Hoel-Høiseth, Eirik, Aune, Erlend, Biró, David Zsolt, Ruocco, Massimiliano |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection
por: Malacarne, Sara, et al.
Publicado: (2026)
por: Malacarne, Sara, et al.
Publicado: (2026)
Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling
por: Lee, Daesoo, et al.
Publicado: (2023)
por: Lee, Daesoo, et al.
Publicado: (2023)
Closing the Gap Between Synthetic and Ground Truth Time Series Distributions via Neural Mapping
por: Lee, Daesoo, et al.
Publicado: (2025)
por: Lee, Daesoo, et al.
Publicado: (2025)
SiamTST: A Novel Representation Learning Framework for Enhanced Multivariate Time Series Forecasting applied to Telco Networks
por: Kristoffersen, Simen, et al.
Publicado: (2024)
por: Kristoffersen, Simen, et al.
Publicado: (2024)
Computer Vision Self-supervised Learning Methods on Time Series
por: Lee, Daesoo, et al.
Publicado: (2021)
por: Lee, Daesoo, et al.
Publicado: (2021)
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
por: Bei, Yuanchen, et al.
Publicado: (2024)
por: Bei, Yuanchen, et al.
Publicado: (2024)
Synthetic Aircraft Trajectory Generation Using Time-Based VQ-VAE
por: Murad, Abdulmajid, et al.
Publicado: (2025)
por: Murad, Abdulmajid, et al.
Publicado: (2025)
CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly Detection
por: Li, Jindong, et al.
Publicado: (2024)
por: Li, Jindong, et al.
Publicado: (2024)
A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness
por: Hoel, Erik
Publicado: (2025)
por: Hoel, Erik
Publicado: (2025)
URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detection
por: Luo, Wei, et al.
Publicado: (2026)
por: Luo, Wei, et al.
Publicado: (2026)
FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly Detection
por: Cao, Rui, et al.
Publicado: (2024)
por: Cao, Rui, et al.
Publicado: (2024)
Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection
por: Najafi, Seyed Amirhossein, et al.
Publicado: (2024)
por: Najafi, Seyed Amirhossein, et al.
Publicado: (2024)
Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis
por: Frotscher, Alexander, et al.
Publicado: (2025)
por: Frotscher, Alexander, et al.
Publicado: (2025)
Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning
por: Ni, Hang, et al.
Publicado: (2025)
por: Ni, Hang, et al.
Publicado: (2025)
Scalable Hierarchical AI-Blockchain Framework for Real-Time Anomaly Detection in Large-Scale Autonomous Vehicle Networks
por: Shit, Rathin Chandra, et al.
Publicado: (2025)
por: Shit, Rathin Chandra, et al.
Publicado: (2025)
Enhancing Unsupervised Anomaly Detection and Early Warning With Dual‐Attention LSTM ‐ AdvAE
por: Zhiyi Zhang, et al.
Publicado: (2026)
por: Zhiyi Zhang, et al.
Publicado: (2026)
Unsupervised Surrogate Anomaly Detection
por: Klüttermann, Simon, et al.
Publicado: (2025)
por: Klüttermann, Simon, et al.
Publicado: (2025)
DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection
por: Chen, Qingfeng, et al.
Publicado: (2025)
por: Chen, Qingfeng, et al.
Publicado: (2025)
HGNet: High-Order Spatial Awareness Hypergraph and Multi-Scale Context Attention Network for Colorectal Polyp Detection
por: Liu, Xiaofang, et al.
Publicado: (2025)
por: Liu, Xiaofang, et al.
Publicado: (2025)
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection
por: Huang, Tiange, et al.
Publicado: (2025)
por: Huang, Tiange, et al.
Publicado: (2025)
Unsupervised Symbolic Anomaly Detection
por: Hossain, Md Maruf, et al.
Publicado: (2026)
por: Hossain, Md Maruf, et al.
Publicado: (2026)
Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments
por: Thiry, Zachari, et al.
Publicado: (2024)
por: Thiry, Zachari, et al.
Publicado: (2024)
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection
por: Zheng, Zhi, et al.
Publicado: (2025)
por: Zheng, Zhi, et al.
Publicado: (2025)
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection
por: Li, Zhong, et al.
Publicado: (2025)
por: Li, Zhong, et al.
Publicado: (2025)
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection
por: Yao, Liyi, et al.
Publicado: (2024)
por: Yao, Liyi, et al.
Publicado: (2024)
Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis
por: Liu, Zhe, et al.
Publicado: (2024)
por: Liu, Zhe, et al.
Publicado: (2024)
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model
por: Fu, Yali, et al.
Publicado: (2025)
por: Fu, Yali, et al.
Publicado: (2025)
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment
por: Cai, Weishan, et al.
Publicado: (2024)
por: Cai, Weishan, et al.
Publicado: (2024)
BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data
por: Xie, Junyi, et al.
Publicado: (2025)
por: Xie, Junyi, et al.
Publicado: (2025)
Temporal Graph Networks for Graph Anomaly Detection in Financial Networks
por: Kim, Yejin, et al.
Publicado: (2024)
por: Kim, Yejin, et al.
Publicado: (2024)
Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks
por: Huang, Jianfeng, et al.
Publicado: (2022)
por: Huang, Jianfeng, et al.
Publicado: (2022)
An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks
por: Tajgardan, Mohsen, et al.
Publicado: (2026)
por: Tajgardan, Mohsen, et al.
Publicado: (2026)
Towards Unsupervised Validation of Anomaly-Detection Models
por: Idan, Lihi
Publicado: (2024)
por: Idan, Lihi
Publicado: (2024)
When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
por: Huang, Wei, et al.
Publicado: (2026)
por: Huang, Wei, et al.
Publicado: (2026)
GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network
por: Chen, Weiqi, et al.
Publicado: (2024)
por: Chen, Weiqi, et al.
Publicado: (2024)
METRA: Scalable Unsupervised RL with Metric-Aware Abstraction
por: Park, Seohong, et al.
Publicado: (2023)
por: Park, Seohong, et al.
Publicado: (2023)
GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks
por: Akmeemana, Lahiru, et al.
Publicado: (2025)
por: Akmeemana, Lahiru, et al.
Publicado: (2025)
Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction
por: Üstek, İrem, et al.
Publicado: (2024)
por: Üstek, İrem, et al.
Publicado: (2024)
TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection
por: Shi, Wen, et al.
Publicado: (2026)
por: Shi, Wen, et al.
Publicado: (2026)
CRoC: Context Refactoring Contrast for Graph Anomaly Detection with Limited Supervision
por: Xie, Siyue, et al.
Publicado: (2025)
por: Xie, Siyue, et al.
Publicado: (2025)
Ejemplares similares
-
Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection
por: Malacarne, Sara, et al.
Publicado: (2026) -
Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling
por: Lee, Daesoo, et al.
Publicado: (2023) -
Closing the Gap Between Synthetic and Ground Truth Time Series Distributions via Neural Mapping
por: Lee, Daesoo, et al.
Publicado: (2025) -
SiamTST: A Novel Representation Learning Framework for Enhanced Multivariate Time Series Forecasting applied to Telco Networks
por: Kristoffersen, Simen, et al.
Publicado: (2024) -
Computer Vision Self-supervised Learning Methods on Time Series
por: Lee, Daesoo, et al.
Publicado: (2021)