EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection

TL;DR

EAGLE introduces a contrastive learning approach for efficient graph anomaly detection on heterogeneous graphs, utilizing node embeddings and a graph autoencoder to outperform existing methods while being suitable for embedded devices.

Contribution

The paper proposes EAGLE, a novel contrastive learning framework that enhances efficiency and effectiveness in detecting anomalies in heterogeneous graphs.

Findings

EAGLE outperforms state-of-the-art methods on three datasets.

The method is efficient enough for embedded devices.

Unsupervised learning with contrastive loss improves anomaly detection.

Abstract

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 are lack of efficiency that is definitely necessary for embedded devices. Towards 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.