Generative semi-supervised graph anomaly detection

This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph. We reveal that having access to the normal nodes, even just a...

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Bibliographic Details
Main Authors: QIAO, Hezhe, WEN, Qingsong, LI, Xiaoli, LIM, Ee-peng, PANG, Guansong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
GAD
Online Access:https://ink.library.smu.edu.sg/sis_research/9763
https://ink.library.smu.edu.sg/context/sis_research/article/10763/viewcontent/10275_Generative_Semi_supervis__1_.pdf
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Institution: Singapore Management University
Language: English