Deep graph-level anomaly detection by glocal knowledge distillation

• Main Authors: MA, Rongrong, PANG, Guansong, CHEN, Ling, HENGEL, Anton Van Den
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Graph-level anomaly detection; Graph neural networks; Knowledge distillation; Deep learning; Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/7054; https://ink.library.smu.edu.sg/context/sis_research/article/8057/viewcontent/3488560.3498473.pdf
• Institution: Singapore Management University

Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes, as compared to other graphs. One of the challenges in GAD is to devise graph representations that enable the detection of both locally- and globally-...