MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection. (arXiv:2305.10668v1 [cs.LG])

Graph anomaly detection has long been an important problem in various domains
pertaining to information security such as financial fraud, social spam,
network intrusion, etc. The majority of existing methods are performed in an
unsupervised manner, as labeled anomalies in a large scale are often too
expensive to acquire. However, the identified anomalies may turn out to be data
noises or uninteresting data instances due to the lack of prior knowledge on
the anomalies. In realistic scenarios, it is often …

anomaly detection detection domains etc financial financial fraud fraud important information information security intrusion large meta network problem scale security social spam