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
- 2. Themis Palpanas
- 3. Mohammed Meftah
- 4. Emmanuel Remy
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,029 | SAND: Streaming Subsequence Anomaly Detection | 2021 | VLDB | 9.740868e-05 |
| 2,290 | TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data | 2022 | VLDB | 9.0934125e-05 |
| 2,381 | TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection | 2022 | VLDB | 8.9327638e-05 |
| 2,644 | Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series | 2020 | VLDB | 8.3832357e-05 |
| 5,777 | ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection | 2024 | VLDB | 5.3308813e-05 |
| 8,157 | TOD: GPU-accelerated Outlier Detection via Tensor Operations | 2023 | VLDB | 4.5730908e-05 |
| 11,094 | Time-Series Anomaly Detection: Overview and New Trends | 2024 | VLDB | 4.1945683e-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 |
|---|---|---|---|---|
| 161 | LOF: Identifying Density-Based Local Outliers | 2000 | SIGMOD | 0.00039846974 |
| 2,629 | Online Outlier Detection in Sensor Data Using Non-Parametric Models | 2006 | VLDB | 8.4160309e-05 |
| 2,644 | Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series | 2020 | VLDB | 8.3832357e-05 |
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