mlsanityData to 13 Oct 2026

Graph Anomaly Detection in Time Series: A Survey

arXiv 2302.00058 ↗First posted 31 Jan 2023cs.LGcited by 18

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Abstract (arXiv)

With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challenging as it requires considering both the intra-variable dependency (relationships within a variable over time) and the inter-variable dependency (relationships between multiple variables) existing in time-series data. Recent graph-based approaches have made impressive progress in tackling the challenges of this field. In this survey, we conduct a comprehensive and up-to-date review of TSAD using graphs, referred to as G-TSAD. First, we explore the significant potential of graph representation for time-series data and and its contributions to facilitating anomaly detection. Then, we review state-of-the-art graph anomaly detection techniques, mostly leveraging deep learning architectures, in the context of time series. For each method, we discuss its strengths, limitations, and the specific applications where it excels. Finally, we address both the technical and application challenges currently facing the field, and suggest potential future directions for advancing research and improving practical outcomes.

Key facts. Cited by 18 papers in the map (data through 13 Oct 2026). Builds on 64 of its 149 references that are in the map. First posted on arXiv 31 Jan 2023.

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64 of 149 references.

References · 64

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38 more
  1. 2020Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervisioncited by 237
  2. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 7; 2026: 7.

18 papers.

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  1. 2024A Generalizable Anomaly Detection Method in Dynamic Graphscited by 10
  2. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  3. 2023Entropy Causal Graphs for Multivariate Time Series Anomaly Detectioncited by 6
  4. 2025mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scalecited by 6
  5. 2026Towards Anomaly Detection on Relational Datacited by 5
  6. 2025Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructurescited by 4
  7. 2023Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Modelscited by 3
  8. 2025HLSAD: Hodge Laplacian-based Simplicial Anomaly Detectioncited by 2
  9. 2026FEATHer: Fourier-Efficient Adaptive Temporal Hierarchy Forecaster for Time-Series Forecastingcited by 1
  10. 2023A Comprehensive Python Library for Deep Learning-Based Event Detection in Multivariate Time Series Data and Information Retrieval in NLPcited by 0
  11. 2025Learning Intrinsic Water-Quality Dynamics with Rainfall for Data-Driven Forecastingcited by 0
  12. 2025CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detectioncited by 0
6 more
  1. 2025Diffuse to Detect: A Generalizable Framework for Anomaly Detection with Diffusion Models Applications to UAVs and Beyondcited by 0
  2. 2026IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detectioncited by 0
  3. 2026Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networkscited by 0
  4. 2026Foundation Models for Epileptogenic Zone Identification in Drug-Resistant Epilepsycited by 0
  5. 2026VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligencecited by 0
  6. 2026Graph-Spectral Flow Matching for Multivariate Time Series Anomaly Detectioncited by 0