EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection

doi:10.1109/MIS.2022.3229147

EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection

2023-03-01

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DOI：10.1109/MIS.2022.3229147

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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.

Keywords:

Anomaly detection

Task analysis

Representation learning

Intelligent systems

Semantics

Data models

Computational modeling

EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection

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