Web: http://arxiv.org/abs/2209.10168

Sept. 22, 2022, 1:11 a.m. | Shuang Zhou, Xiao Huang, Ninghao Liu, Fu-Lai Chung, Long-Kai Huang

cs.LG updates on arXiv.org arxiv.org

Graph anomaly detection (GAD) is a vital task since even a few anomalies can
pose huge threats to benign users. Recent semi-supervised GAD methods, which
can effectively leverage the available labels as prior knowledge, have achieved
superior performances than unsupervised methods. In practice, people usually
need to identify anomalies on new (sub)graphs to secure their business, but
they may lack labels to train an effective detection model. One natural idea is
to directly adopt a trained GAD model to the …

anomaly anomaly detection arxiv augmentation data detection graph

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