Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (8): 374-384.doi: 10.11896/jsjkx.241000140

• Information Security • Previous Articles     Next Articles

Cross-domain Graph Anomaly Detection Via Dual Classification and Reconstruction

SU Shiyu 1, YU Jiong 2, LI Shu3, JIU Shicheng1   

  1. 1 School of Software Engineering,Xinjiang University,Urumqi 830000,China
    2 College of Information Science and Engineering,Xinjiang University,Urumqi 830000,China
    3 School of Computer Science and Technology,Xinjiang University,Urumqi 830000,China
  • Received:2024-10-28 Revised:2025-03-12 Online:2025-08-15 Published:2025-08-08
  • About author:SU Shiyu,born in 1998,postgraduate,is a member of CCF(No.W8728G).His main research interests include anomaly detection and attribute graphs.
    YU Jiong,born in 1964,Ph.D,professor,Ph.D supervisor.His main research interests include network security and distributed computing.
  • Supported by:
    National Natural Science Foundation of China(62262064).

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Abstract: Cross-domain graph anomaly detection improves the accuracy of detecting anomalous nodes by leveraging labeled source graphs to assist in detecting anomalies in unlabeled target graphs,effectively reducing the high false positive rate in unsupervised detection.Aligning features between source and target graphs remain challenging due to the complex relationships between graph topology and node attributes,and the diversity of anomalous nodes further complicates detection.To address this,a Dual Classification and Reconstruction Network(DCRN) is proposed.DCRN employs a reconstruction-based strategy for domain adaptation,optimizing shared structure and attribute encoders,anomaly classifiers,and decoders.This enables the model to capture complex topological and attribute relationships between source and target graphs,achieving effective feature alignment and knowledge transfer.DCRN combines classifier and decoder results to identify both shared and unique anomalies in the target graph,enhancing detection accuracy and robustness.Experiments on four real-world datasets show that DCRN outperforms 10 baseline algorithms,with an average improvement of 4.5% in AUC-ROC,20.5% in AUC-PR,and a 16.13% reduction in FAR,demonstrating its effectiveness in detecting anomalous nodes in target graphs.

Key words: Anomaly detection, Attribute graph, Domain adaptation, Graph neural network, Knowledge transfe

CLC Number: 

  • TP391
[1]ZHANG Y Y,QIAN Y R,TAO W B,et al.Survey of Attribute Graph Anomaly Detection Based on Deep Learning[J].ComputerEngineering and Applications,2022,58(19):1-13.
[2]HAJLI N,SAEED U,TAJVIDI M,et al.Social bots and thespread of disinformation in social media:the challenges of artificial intelligence[J].British Journal of Management,2022,33(3):1238-1253.
[3]LU Y J,SHENG L K,ZHANG Y L.Research on structural diversity of citation network oriented to evaluation of literature [J].Application Research of Computers,2024,41(2):408-414.
[4]LIU H L,LIU Y X,XU J Y,et al.Research Progress in Application of Graph Anomaly Detection in Financial Anti-Fraud[J].Computer Engineering and Applications,2022,58(22):41-53.
[5]LI Z,JIN X L,WANG Y J,et al.Anomaly Node DetectionMethod Based on Variational Graph Auto-Encoders in Attri-bute Networks[J].Pattern Recognition and Artificial Intelligence,2022,35(1):17-25.
[6]XU X,YURUK N,FENG Z,et al.Scan:a structural clustering algorithm for networks[C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2007:824-833.
[7]LI S,YU J,LU Y,et al.Self-supervised enhanced denoising diffusion for anomaly detection[J].Information Sciences,2024,669:120612.
[8]ZHUANG Y S,LIN S L,LIN Z X,et al.Study on Generative Adversarial Network for Data Anomaly Detection[J].Computer Engineering and Applications,2022,58(4):143-149.
[9]DING K,SHU K,SHAN X,et al.Cross-domain graph anomaly detection[J].IEEE Transactions on Neural Networks and Learning Systems,2021,33(6):2406-2415.
[10]LI Y,YANG X,GAO Q,et al.Cross-regional Fraud Detection via Continual Learning with Knowledge Transfer[J].IEEE Transactions on Knowledge and Data Engineering,2024,36(12):7865-7877.
[11]ZHANG F,FAN H,WANG R,et al.Deep dual support vector data description for anomaly detection on attributed networks[J].International Journal of Intelligent Systems,2022,37(2):1509-1528.
[12]BANDYOPADHYAY S,LOKESH N,MURTY M N.Outlieraware network embedding for attributed networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019:12-19.
[13]WANG Q,PANG G,SALEHI M,et al.Cross-domain graphanomaly detection via anomaly-aware contrastive alignment[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2023:4676-4684.
[14]LI Z,LIANG S,SHI J,et al.Cross-domain graph level anomaly detection[J].IEEE Transactions on Knowledge and Data Engineering,2024,36(12):7839-7850.
[15]QU C,JI F,QIU M,et al.Learning to selectively transfer:Reinforced transfer learning for deep text matching[C]//Procee-dings of the twelfth ACM International Conference on Web Search and Data Mining.2019:699-707.
[16]TZENG E,HOFFMAN J,SAENKO K,et al.Adversarial dis-criminative domain adaptation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:7167-7176.
[17]GUO J Y,LI R H,ZHANG Y,et al.Graph neural networkbased anomaly detection in dynamic networks[J].Journal of Software,2020,31(3):748-762.
[18]CHEN B F,LI J D,LU X J,et al.Survey of Deep Learning Based Graph Anomaly Detection Methods[J].Journal of Computer Research and Development,2021,58(7):1436-1455
[19]MA X,WU J,XUE S,et al.A comprehensive survey on graph anomaly detection with deep learning[J].IEEE Transactions on Knowledge and Data Engineering,2021,35(12):12012-12038.
[20]LIU N,HUANG X,HU X.Accelerated Local Anomaly Detection via Resolving Attributed Networks[C]//IJCAI.2017:2337-2343.
[21]LI J,DANI H,HU X,et al.Radar:Residual analysis for anomaly detection in attributed networks[C]//IJCAI.2017:2152-2158.
[22]WU L,HU X,MORSTATTER F,et al.Adaptive spammer detection with sparse group modeling[C]//Proceedings of the International AAAI Conference on Web and Social Media.2017:319-326.
[23]DING K,LI J,BHANUSHALI R,et al.Deep anomaly detection on attributed networks[C]//Proceedings of the 2019 SIAM International Conference on Data Mining.Society for Industrial and Applied Mathematics,2019:594-602.
[24]FAN H,ZHANG F,LI Z.Anomalydae:Dual autoencoder for anomaly detection on attributed networks[C]//2020 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP 2020).IEEE,2020:5685-5689.
[25]LUO X,WU J,BEHESHTI A,et al.Comga:Community-aware attributed graph anomaly detection[C]//Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining.2022:657-665.
[26]LIU Y,LI Z,PAN S,et al.Anomaly detection on attributed networks via contrastive self-supervised learning[J].IEEE transactions on neural networks and learning systems,2021,33(6):2378-2392.
[27]ZHENG Y,JIN M,LIU Y,et al.Generative and contrastive self-supervised learning for graph anomaly detection[J].IEEE Transactions on Knowledge and Data Engineering,2021,35(12):12220-12233.
[28]ZHANG J,WANG S,CHEN S.Reconstruction enhanced multi-view contrastive learning for anomaly detection on attributed networks[J].arXiv:2205.04816,2022.
[29]HAN X,YUAN S.Unsupervised cross-system log anomaly detection via domain adaptation[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management.2021:3068-3072.
[30]SU Y,TAN Y,AN S,et al.Semantic-driven dual consistency learning for weakly supervised video anomaly detection[J].Pattern Recognition,2025,157:110898.
[31]LONG M,CAO Y,WANG J,et al.Learning transferable features with deep adaptation networks[C]//International Confe-rence on Machine Learning.PMLR,2015:97-105.
[32]ZELLINGER W,GRUBINGER T,LUGHOFER E,et al.Central moment discrepancy(CMD) for domain-invariant representation learning[J].arXiv:1702.08811,2017.
[33]KANG G,JIANG L,YANG Y,et al.Contrastive adaptation network for unsupervised domain adaptation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:4893-4902.
[34]FLAMARY R,COURTY N,TUIA D,et al.Optimal transport for domain adaptation[J].arXiv:1507.00504,2016.
[35]GHIFARY M,KLEIJN W B,ZHANG M,et al.Deep recon-struction-classification networks for unsupervised domain adaptation[C]//Computer Vision-ECCV 2016:14th European Conference,Amsterdam,The Netherlands,October 11-14,2016,Proceedings,Part IV 14.Springer International Publishing,2016:597-613.
[36]BOUSMALIS K,TRIGEORGIS G,SILBERMAN N,et al.Domain separation networks[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.2016:343-351.
[37]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial nets[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems.2014:2672-2680.
[38]GANIN Y,USTINOVA E,AJAKAN H,et al.Domain-adversarial training of neural networks[J].Journal of Machine Lear-ning Research,2016,17(59):1-35.
[39]LONG M,CAO Z,WANG J,et al.Conditional adversarial do-main adaptation[C]//Proceedings of the 32nd International Conference on Neural Information processing Systems.2018:1647-1657.
[40]KONG L C,LIU G Z.Review of Outlier Detection Algorithms[J].Computer Science,2024,51(8):20-33.
[41]PENG Z,LUO M,LI J,et al.ANOMALOUS:A Joint Modeling Approach for Anomaly Detection on Attributed Networks[C]//IJCAI.2018:3513-3519.
[42]BANDYOPADHYAY S,LOKESH N,VIVEK S V,et al.Outlier resistant unsupervised deep architectures for attributed network embedding[C]//Proceedings of the 13th International Conference on Web Search and Data Mining.2020:25-33.
[43]WANG X,JIN B,DU Y,et al.One-class graph neural networks for anomaly detection in attributed networks[J].Neural Computing and Applications,2021,33:12073-12085.
[44]DIEDERIK P K.Adam:A method for stochastic optimization[J].arXiv:1412.6980,2014.
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