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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)

Paper ID
12358
Venue
VLDB
Year
2020
Pagerank
5.9730493e-05
Overall Rank
6,109 | 58.09%
DOI
10.14778/3415478.3415514

Incoming Non-self Citations Over Time

Authors

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)

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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 0.0002962566
2,340 Online Outlier Detection in Sensor Data Using Non-Parametric Models 2006 VLDB 8.7246796e-05
2,346 Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series 2020 VLDB 8.7168932e-05
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