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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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  1. Ma X, Wu J, Xue S, Yang J, Zhou C, Sheng QZ, Xiong H, Akoglu L (2021) A comprehensive survey on graph anomaly detection with deep learning. IEEE Trans Knowl Data Eng
  2. Anand K, Kumar J, Anand K (2017) Anomaly detection in online social network: A survey. In: 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), pp 456–459. IEEE · DOI
  3. Zhang G, Li Z, Huang J, Wu J, Zhou C, Yang J (2018) efraudcom: An e-commerce fraud detection system via competitive graph neural networks. ACM Trans. Inf, Syst
  4. Akoglu L, Tong H, Koutra D (2015) Graph based anomaly detection and description: a survey. Data Mining Knowl Disc 29(3):626–688 · DOI · Paper page
  5. Cai L, Chen Z, Luo C, Gui J, Ni J, Li D, Chen H (2021) Structural temporal graph neural networks for anomaly detection in dynamic graphs. In: the ACM International Conference on Information and Knowledge Management, pp 3747–3756 · DOI · Paper page
  6. Narayan A, Roe PH (2018) Learning graph dynamics using deep neural networks. IFAC-PapersOnLine 51(2):433–438 · DOI
  7. Ji T, Yang D, Gao J (2013) Incremental local evolutionary outlier detection for dynamic social networks. In: the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp 1–15. Springer · DOI
  8. Yu Y, Wang W, Wu N, Liu H, Shao M (2022) Iisd: Integrated interaction subgraph detection for event mining. Knowl-Based Syst (Mar.15), 240 · DOI
  9. Tang C, Xu L, Yang B, Tang Y, Zhao D (2023) Gru-based interpretable multivariate time series anomaly detection in industrial control system. Comput Secur 127:103094 · DOI
  10. Li G, Jung JJ (2023) Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges. Inf Fusion 91:93–102 · DOI
  11. Yu Y, Wang W, Wu N, Liu H, Shao M (2022) Iisd: Integrated interaction subgraph detection for event mining. Knowl-Based Syst 240:108080 · DOI
  12. Ding K, Li J, Bhanushali R, Liu H (2019) Deep anomaly detection on attributed networks. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp 594–602. SIAM · DOI
  13. Liu Y, Pan S, Wang YG, Xiong F, Wang L, Chen Q, Lee VC (2021) Anomaly detection in dynamic graphs via transformer. IEEE Trans Knowl Data Eng
  14. Jin M, Liu Y, Zheng Y, Chi L, Li Y-F, Pan S (2021) Anemone: Graph anomaly detection with multi-scale contrastive learning. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp 3122–3126 · DOI
  15. Pareja A, Domeniconi G, Chen J, Ma T, Suzumura T, Kanezashi H, Kaler T, Schardl T, Leiserson C (2020) Evolvegcn: Evolving graph convolutional networks for dynamic graphs. In: the AAAI Conference on Artificial Intelligence, vol 34, pp 5363–5370 · DOI
  16. Yoon M, Hooi B, Shin K, Faloutsos C (2019) Fast and accurate anomaly detection in dynamic graphs with a two-pronged approach. In: the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 647–657 · DOI
  17. Xue B, Gao X, Zhai F, Li B, Yu J, Fu S, Chen L, Meng Z (2023) A contrastive autoencoder with multi-resolution segment-consistency discrimination for multivariate time series anomaly detection. Appl Intell 53(23):28655–28674 · DOI
  18. Teng X, Lin Y-R, Wen X (2017) Anomaly detection in dynamic networks using multi-view time-series hypersphere learning. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 827–836 · DOI
  19. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv:1406.1078 · DOI · Paper page
  20. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 · arXiv
  21. Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv:1710.10903 · arXiv
  22. Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks? arXiv:1810.00826 · arXiv
  23. Geisler S, Zügner D, Günnemann S (2020) Reliable graph neural networks via robust aggregation. Adv Neural Inf Process Syst 33:13272–13284
  24. Zhu S, Pan S, Zhou C, Wu J, Cao Y, Wang B (2020) Graph geometry interaction learning. Adv Neural Inf Process Syst 33:7548–7558
  25. Wu M, Pan S, Du L, Zhu X (2021) Learning graph neural networks with positive and unlabeled nodes. ACM Trans Knowl Disc Data (TKDD) 15(6):1–25 · DOI
  26. Kale R, Thing VLL (2023) Few-shot weakly-supervised cybersecurity anomaly detection. Comput Secur 130:103194 · DOI
  27. Gao Y, Yu X, Zhang H (2022) Graph clustering using triangle-aware measures in large networks. Inf Sci 584:618–632 · DOI
  28. Gao Y, Yu X, Zhang H (2021) Overlapping community detection by constrained personalized pagerank. Exp Syst Appl 173:114682 · DOI
  29. Chen J, Pi D, Wang X (2024) A two-stage adversarial transformer based approach for multivariate industrial time series anomaly detection. Appl Intell, 1–20 · DOI
  30. Xue L, Chen Y, Luo M, Peng Z, Liu J (2020) An anomaly detection framework for time-evolving attributed networks. Neurocomput 407:39–49 · DOI
  31. Goel R, Kazemi SM, Brubaker M, Poupart P (2020) Diachronic embedding for temporal knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 3988–3995 · DOI · Paper page
  32. Sharma K, Trivedi R, Sridhar R, Kumar S (2023) Temporal dynamics-aware adversarial attacks on discrete-time dynamic graph models. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 2023–2035 · DOI
  33. Yu Y, Wang W, Shao M, Wu N, Sun Y, Sun Y, Tian Q (2022) Multi-users interaction anomalous subgraph detection for event mining. Neurocomput 509:34–45 · DOI
  34. Guo D, Liu Z, Li R (2023) Regraphgan: A graph generative adversarial network model for dynamic network anomaly detection. Neural Netw 166:273–285 · DOI
  35. Diro A, Kaisar S, Vasilakos AV, Anwar A, Nasirian A, Olani G (2024) Anomaly detection for space information networks: A survey of challenges, techniques, and future directions. Comput Secur 139:103705 · DOI
  36. Sricharan K, Das K (2014) Localizing anomalous changes in time-evolving graphs. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp 1347–1358 · DOI
  37. Zheng L, Li Z, Li J, Li Z, Gao J (2019) Addgraph: Anomaly detection in dynamic graph using attention-based temporal gcn. In: IJCAI, pp 4419–4425 · DOI · Paper page
  38. Yu W, Cheng W, Aggarwal CC, Zhang K, Wang W (2018) Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks. In: the 24th ACM SIGKDD International Conference · DOI
  39. Yang C, Zhou L, Wen H, Zhou Z, Wu Y (2020) H-vgrae: A hierarchical stochastic spatial-temporal embedding method for robust anomaly detection in dynamic networks. arXiv:2007.06903 · arXiv
  40. Chen A, Fu Y, Zheng X, lu G (2022) An efficient network behavior anomaly detection using a hybrid dbn-lstm network. Comput Secur 114:102600 · DOI
  41. Zuo Y, Liu G, Lin H, Guo J, Hu X, Wu J (2018) Embedding temporal network via neighborhood formation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2857–2866 · DOI
  42. Gao Y, Zhang H, Yu X (2023) Higher-order community detection: On information degeneration and its elimination. IEEE/ACM Trans Netw 31(2):891–903 · DOI
  43. Kong X, Zhang W, Wang H, Hou M, Chen X, Yan X, Das SK (2024) Federated graph anomaly detection via contrastive self-supervised learning. IEEE Trans Neural Netw Learn Syst, 1–14
  44. Tong, H., Faloutsos, C., Pan, J.-Y.: Fast random walk with restart and its applications. In: Sixth International Conference on Data Mining (ICDM’06), pp. 613–622 (2006). IEEE · DOI
  45. Desmier E, Plantevit M, Robardet C, Boulicaut J-F (2012) Cohesive co-evolution patterns in dynamic attributed graphs. In: International Conference on Discovery Science, pp 110–124. Springer · DOI
  46. Zheng Y, Jin M, Liu Y, Chi L, Phan KT, Chen Y-PP (2021) Generative and contrastive self-supervised learning for graph anomaly detection. IEEE Trans Knowl Data Eng

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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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CoChat. (2026). Citation check: Temporal subgraph contrastive learning for anomaly detection on dynamic attributed graphs. https://app.cochat.ai/papers/10.1007/s10489-025-06402-8
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Yu, Y., Li, X., Shao, M., Sun, Y., & Wang, W. (2025). Temporal subgraph contrastive learning for anomaly detection on dynamic attributed graphs. Applied Intelligence. https://doi.org/10.1007/s10489-025-06402-8