GraphAn: Graph-based Subsequence Anomaly Detection
Summary: GraphAn: graph-based system for subsequence anomaly detection on Series2Graph, using low-dimensional embeddings. Unsupervised detection of single and recurrent anomalies without prior knowledge, with accuracy and fast performance on large datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Paul Boniol (Université de Paris; Électricité de France)
- 2. Themis Palpanas (Université de Paris)
- 3. Mohammed Meftah (Université de Paris; Électricité de France)
- 4. Emmanuel Remy (Université de Paris; Électricité de France)
BibTeX Citation
@article{boniol_vldb20,
title = {{GraphAn: Graph-based Subsequence Anomaly Detection}},
author = {Boniol, Paul and Palpanas, Themis and Meftah, Mohammed and Remy, Emmanuel},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {2941--2944},
doi = {10.14778/3415478.3415514},
url = {https://doi.org/10.14778/3415478.3415514},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,629 | SAND: Streaming Subsequence Anomaly Detection | 2021 | VLDB | 0.00010036401 |
| 1,740 | TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data | 2022 | VLDB | 9.7439205e-05 |
| 1,937 | TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection | 2022 | VLDB | 9.3387043e-05 |
| 2,375 | Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series | 2020 | VLDB | 8.555841e-05 |
| 5,559 | ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection | 2024 | VLDB | 6.0849722e-05 |
| 8,400 | TOD: GPU-accelerated Outlier Detection via Tensor Operations | 2023 | VLDB | 5.3395906e-05 |
| 9,488 | Time-Series Anomaly Detection: Overview and New Trends | 2024 | VLDB | 5.1708619e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 142 | LOF: Identifying Density-Based Local Outliers | 2000 | SIGMOD | 0.00029202746 |
| 2,359 | Online Outlier Detection in Sensor Data Using Non-Parametric Models | 2006 | VLDB | 8.5813849e-05 |
| 2,375 | Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series | 2020 | VLDB | 8.555841e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 1,629 | SAND: Streaming Subsequence Anomaly Detection | 2021 | VLDB |
| 2 | 9,488 | Time-Series Anomaly Detection: Overview and New Trends | 2024 | VLDB |
| 3 | 1,740 | TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data | 2022 | VLDB |
| 4 | 6,073 | AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data | 2024 | VLDB |
| 5 | 11,451 | MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection | 2025 | VLDB |
| 6 | 13,777 | SAND in Action: Subsequence Anomaly Detection for Streams | 2021 | VLDB |
| 7 | 8,874 | Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability | 2024 | SIGMOD |
| 8 | 6,268 | An Experimental Evaluation of Anomaly Detection in Time Series | 2024 | VLDB |
| 9 | 1,005 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB |
| 10 | 2,375 | Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series | 2020 | VLDB |