LabelGen: An Anomaly Label Generative Framework for Enhanced Graph Anomaly Detection

LabelGen: An Anomaly Label Generative Framework for Enhanced Graph Anomaly Detection

Anomaly detection in graphs is increasingly used to reveal fraud, fakes, security attacks and unusual behaviours in networks, such as social networks, financial transaction networks and the Internet of Things. Accurately detecting such graph anomalies using deep learning approaches faces challenges...

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Podrobná bibliografie
Hlavní autoři: Siqi Xia, Sutharshan Rajasegarar, Lei Pan, Christopher Leckie, Sarah M. Erfani, Jeffrey Chan
Médium: Článek
Jazyk:English
Vydáno: IEEE 2024-01-01
Edice:IEEE Access
Témata:
Anomalies in graphs
generative adversarial networks
k-hop neighborhood sampling
deep learning
On-line přístup:https://ieeexplore.ieee.org/document/10662895/

Internet

https://ieeexplore.ieee.org/document/10662895/

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