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Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
Liu, Yixin1; Li, Zhao2; Pan, Shirui1; Gong, Chen3,4; Zhou, Chuan5; Karypis, George6
AbstractAnomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this article, we present a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation). Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network (GNN)-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multiround predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the GNN module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark data sets.
KeywordAnomaly detection Task analysis Graph neural networks Unsupervised learning Predictive models Pattern matching Training Anomaly detection attributed networks contrastive self-supervised learning graph neural networks (GNNs) unsupervised learning
Indexed BySCI
Funding ProjectNational Natural Science Foundation (NSF) of China[61973162] ; National Natural Science Foundation (NSF) of China[61872360] ; Fundamental Research Funds for the Central Universities[30920032202] ; China Computer Federation (CCF)-Tencent Open Fund[RAGR20200101] ; Hong Kong Scholars Program[XJ2019036]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000733511400001
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Document Type期刊论文
Corresponding AuthorPan, Shirui
Affiliation1.Monash Univ, Fac Informat Technol, Dept Data Sci & AI, Clayton, Vic 3800, Australia
2.Alibaba Grp, Hangzhou 310000, Peoples R China
3.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Key Lab Intelligent Percept & Syst High Dimens In, PCA Lab,Minist Educ, Nanjing 210094, Peoples R China
4.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100093, Peoples R China
6.Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA
Recommended Citation
GB/T 7714
Liu, Yixin,Li, Zhao,Pan, Shirui,et al. Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:15.
APA Liu, Yixin,Li, Zhao,Pan, Shirui,Gong, Chen,Zhou, Chuan,&Karypis, George.(2021).Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15.
MLA Liu, Yixin,et al."Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):15.
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