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Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning by Duan, Jingcan; Zhang, Pei; Wang, Siwei; Hu, Jingtao; Jin, Hu; Zhang, Jiaxin; Zhou, Haifang; Liu, Xinwang is a scholarly article available to read on EtoBox.

What is Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning about?

Graph anomaly detection (GAD) has attracted increasing attention in machine learning and data mining. Recent works have mainly focused on how to capture richer information to improve the quality of node embeddings for GAD. Despite their significant advances in detection performance, there is still a relative dearth of research on the properties of the task. GAD aims to discern the anomalies that deviate from most nodes. However, the model is prone to learn the pattern of normal samples which make up the majority of samples. Meanwhile, anomalies can be easily detected when their behaviors differ from normality. Therefore, the performance can be further improved by enhancing the ability to learn the normal pattern. To this end, we propose a normality learning-based GAD framework via multi-scale contrastive learning networks (NLGAD for abbreviation). Specifically, we first initialize the model with the contrastive networks on different scales. To provide sufficient and reliable normal nodes for normality learning, we design an effective hybrid strategy for normality selection. Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimat

Author

Duan, Jingcan; Zhang, Pei; Wang, Siwei; Hu, Jingtao; Jin, Hu; Zhang, Jiaxin; Zhou, Haifang; Liu, Xinwang

Published

2023

Language

EN