Cross-domain graph anomaly detection via anomaly-aware contrastive alignment

• Main Authors: WANG, Qizhou, PANG, Guansong, SALEHI, Mahsa, BUNTINE, Wray, LECKIE, Christopher
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8004; https://ink.library.smu.edu.sg/context/sis_research/article/9007/viewcontent/25591_Article_Text_29654_1_2_20230626.pdf
• Institution: Singapore Management University

Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive iss...