Few-Shot Graph Anomaly Detection via Dual-Level Knowledge Distillation

Few-Shot Graph Anomaly Detection via Dual-Level Knowledge Distillation

Graph anomaly detection is crucial in many high-impact applications across diverse fields. In anomaly detection tasks, collecting plenty of annotated data tends to be costly and laborious. As a result, few-shot learning has been explored to address the issue by requiring only a few labeled samples t...

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Bibliographic Details
Main Authors: Xuan Li, Dejie Cheng, Luheng Zhang, Chengfang Zhang, Ziliang Feng
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Entropy
Subjects:
anomaly detection
graph neural network
cross entropy
knowledge distillation
Online Access:https://www.mdpi.com/1099-4300/27/1/28
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