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Cited by 2Advanced Graph Neural NetworksAnomaly Detection Techniques and ApplicationsGraph Theory and Algorithms

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Temporal subgraph contrastive learning for anomaly detection on dynamic attributed graphs

Yu, Yang, Li, Xin, Shao, Minglai, Sun, Ying, Wang, Wenjun·2025·Applied Intelligence
DOI 10.1007/s10489-025-06402-8
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Metadata

Authors
Yu, Y., Li, X., Shao, M., Sun, Y., & Wang, W.
Affiliations
  • China People's Public Security University (CN)
  • Tianjin University (CN)
Corresponding author
Li, Xin
ORCID
Venue
Applied Intelligence
Publisher
Springer Science and Business Media LLC
Published
2025-04-17
Volume
55
Issue
7
ISSN
  • 0924-669X (ISSN-L)
  • 1573-7497
DOI
10.1007/s10489-025-06402-8
OpenAlex
W4409489554
Cited by
2
References
2
Citations by year
2026: 2
FWCI
0.81
Type
journal article
Language
en
Open access
closed
Licence
Locations
Applied Intelligence (publishedVersion)
Funders
Fundamental Research Funds for the Central Universities (2024JKF02ZK10)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
Keywords
anomaly detection, node representation learning, temporal contrastive learning

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