Generative semi-supervised graph anomaly detection

• 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: Graph anomaly detection; GAD; Generative GAD; Anomaly nodes; Databases and Information Systems; Information Security
• 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
• Institution: Singapore Management University

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...