short-paper

ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning

Authors:

Shirui PanAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 3122 - 3126

https://doi.org/10.1145/3459637.3482057

Published: 30 October 2021 Publication History

Abstract

Anomaly detection on graphs plays a significant role in various domains, including cybersecurity, e-commerce, and financial fraud detection. However, existing methods on graph anomaly detection usually consider the view in a single scale of graphs, which results in their limited capability to capture the anomalous patterns from different perspectives. Towards this end, we introduce a novel graph anomaly detection framework, namely ANEMONE, to simultaneously identify the anomalies in multiple graph scales. Concretely, ANEMONE first leverages a graph neural network backbone encoder with multi-scale contrastive learning objectives to capture the pattern distribution of graph data by learning the agreements between instances at the patch and context levels concurrently. Then, our method employs a statistical anomaly estimator to evaluate the abnormality of each node according to the degree of agreement from multiple perspectives. Experiments on three benchmark datasets demonstrate the superiority of our method.

References

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Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu. 2019. Deep anomaly detection on attributed networks. In SDM. SIAM, 594--602.

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Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, and Shirui Pan. 2021. Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. In IJCAI.

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Jundong Li, Harsh Dani, Xia Hu, and Huan Liu. 2017. Radar: Residual Analysis for Anomaly Detection in Attributed Networks. In IJCAI. 2152--2158.

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Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, and George Karypis. 2021 a. Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning. IEEE TNNLS (2021).

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Yixin Liu, Shirui Pan, Ming Jin, Chuan Zhou, Feng Xia, and Philip S. Yu. 2021 b. Graph Self-Supervised Learning: A Survey. arXiv preprint arXiv:2103.00111 (2021).

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Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, and Vincent Lee. 2021 c. Anomaly Detection in Dynamic Graphs via Transformer. arXiv preprint arXiv:2106.09876 (2021).

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Zhen Peng, Minnan Luo, Jundong Li, Huan Liu, and Qinghua Zheng. 2018. ANOMALOUS: A Joint Modeling Approach for Anomaly Detection on Attributed Networks. In IJCAI. 3513--3519.

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William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2019. Deep Graph Infomax. In ICLR.

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Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE TNNLS, Vol. 32, 1 (2020), 4--24.

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Miao Zhang, Steven Su, Shirui Pan, Xiaojun Chang, Ehsan Abbasnejad, and Reza Haffari. 2021. iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients. arXiv preprint arXiv:2106.10784 (2021).

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Cited By

Han XCui SQin JLiu SJiang BDong CLu ZLiu BChua TNgo CKa-Wei Lee RKumar RLauw H(2024)ContraMTD: An Unsupervised Malicious Network Traffic Detection Method based on Contrastive LearningProceedings of the ACM on Web Conference 202410.1145/3589334.3645479(1680-1689)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645479
Kong XLin HJiang RShen G(2024)Anomalous Sub-Trajectory Detection With Graph Contrastive Self-Supervised LearningIEEE Transactions on Vehicular Technology10.1109/TVT.2024.338268573:7(9800-9811)Online publication date: Jul-2024
https://doi.org/10.1109/TVT.2024.3382685
Xu BWang JZhao ZLin HXia F(2024)Unsupervised Anomaly Detection on Attributed Networks With Graph Contrastive Learning for Consumer Electronics SecurityIEEE Transactions on Consumer Electronics10.1109/TCE.2024.335512270:1(4062-4072)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2024.3355122
Show More Cited By

Index Terms

ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Anomaly detection
    2. Machine learning approaches
      1. Neural networks
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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Published In

cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

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Published: 30 October 2021

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Cited By

Han XCui SQin JLiu SJiang BDong CLu ZLiu BChua TNgo CKa-Wei Lee RKumar RLauw H(2024)ContraMTD: An Unsupervised Malicious Network Traffic Detection Method based on Contrastive LearningProceedings of the ACM on Web Conference 202410.1145/3589334.3645479(1680-1689)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645479
Kong XLin HJiang RShen G(2024)Anomalous Sub-Trajectory Detection With Graph Contrastive Self-Supervised LearningIEEE Transactions on Vehicular Technology10.1109/TVT.2024.338268573:7(9800-9811)Online publication date: Jul-2024
https://doi.org/10.1109/TVT.2024.3382685
Xu BWang JZhao ZLin HXia F(2024)Unsupervised Anomaly Detection on Attributed Networks With Graph Contrastive Learning for Consumer Electronics SecurityIEEE Transactions on Consumer Electronics10.1109/TCE.2024.335512270:1(4062-4072)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2024.3355122
Wu JQiu ZZeng ZXiao RRida IZhang S(2024)Graph Autoencoder Anomaly Detection for E-Commerce Application by Contextual Integrating Contrast With Reconstruction and ComplementarityIEEE Transactions on Consumer Electronics10.1109/TCE.2024.335218670:1(1623-1630)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2024.3352186
Ye MZhang QXue XWang YJiang QQiu H(2024)A Novel Self-Supervised Learning-Based Anomalous Node Detection Method Based on an Autoencoder for Wireless Sensor NetworksIEEE Systems Journal10.1109/JSYST.2023.334743518:1(256-267)Online publication date: Mar-2024
https://doi.org/10.1109/JSYST.2023.3347435
Xu YPeng ZShi BHua XDong B(2024)Learning dynamic graph representations through timespan view contrastsNeural Networks10.1016/j.neunet.2024.106384176(106384)Online publication date: Aug-2024
https://doi.org/10.1016/j.neunet.2024.106384
Jin WMa HZhang YLi ZChang L(2024)Multi-view discriminative edge heterophily contrastive learning network for attributed graph anomaly detectionExpert Systems with Applications10.1016/j.eswa.2024.124460255(124460)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124460
Zuo EYan JAysa AChen CChen CMa HLv XUbul K(2024)SUCOLAEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107016126:PCOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107016
Hu YChen CDeng BLai YLin HZheng ZBian J(2024)Decoupling Anomaly Discrimination and Representation Learning: Self-supervised Learning for Anomaly Detection on Attributed GraphData Science and Engineering10.1007/s41019-024-00249-8Online publication date: 4-May-2024
https://doi.org/10.1007/s41019-024-00249-8
Lee BSu YKong QZhang T(2024)Enhanced multi-view anomaly detection on attribute networks by truncated singular value decompositionInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02211-6Online publication date: 24-May-2024
https://doi.org/10.1007/s13042-024-02211-6
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