EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection

Graph anomaly detection is a popular and vital task in various real-world scenarios, which has been studied for several decades. Recently, many studies extending deep learning-based methods have shown preferable performance on graph anomaly detection. However, existing methods lack efficiency that i...

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Bibliographic Details
Published in:IEEE intelligent systems Vol. 38; no. 2; pp. 55 - 63
Main Authors: Ren, Jing, Hou, Mingliang, Liu, Zhixuan, Bai, Xiaomei
Format: Journal Article
Language:English
Published: Los Alamitos IEEE 01-03-2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Summary:Graph anomaly detection is a popular and vital task in various real-world scenarios, which has been studied for several decades. Recently, many studies extending deep learning-based methods have shown preferable performance on graph anomaly detection. However, existing methods lack efficiency that is definitely necessary for embedded devices. Toward this end, we propose an Efficient Anomaly detection model on heterogeneous Graphs via contrastive LEarning (EAGLE) by contrasting abnormal nodes with normal ones in terms of their distances to the local context. The proposed method first samples instance pairs on meta-path level for contrastive learning. Then, a Graph AutoEncoder-based model is applied to learn informative node embeddings in an unsupervised way, which will be further combined with the discriminator to predict the anomaly scores of nodes. Experimental results show that EAGLE outperforms the state-of-the-art methods on three heterogeneous network datasets.
ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2022.3229147