Corpus ID: 224803683

Early Anomaly Detection by Learning and Forecasting Behavior

@article{Zhao2020EarlyAD,
  title={Early Anomaly Detection by Learning and Forecasting Behavior},
  author={Tong Zhao and Bo Ni and Wenhao Yu and Meng Jiang},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.10016}
}
Graph anomaly detection systems aim at identifying suspicious accounts or behaviors on social networking sites and e-commercial platforms. Detecting anomalous users at an early stage is crucial to minimize financial loss. When a great amount of observed behavior data are available, existing methods perform effectively though it may have been too late to avoid the loss. However, their performance would become unsatisfactory when the observed data are quite limited at the early stage. In this… Expand
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