  The proliferation of web platforms has created incentives for online abuse.
Many graph-based anomaly detection techniques are proposed to identify the
suspicious accounts and behaviors. However, most of them detect the anomalies
once the users have performed many such behaviors. Their performance is
substantially hindered when the users' observed data is limited at an early
stage, which needs to be improved to minimize financial loss. In this work, we
propose Eland, a novel framework that uses action sequence augmentation for
early anomaly detection. Eland utilizes a sequence predictor to predict next
actions of every user and exploits the mutual enhancement between action
sequence augmentation and user-action graph anomaly detection. Experiments on
three real-world datasets show that Eland improves the performance of a variety
of graph-based anomaly detection methods. With Eland, anomaly detection
performance at an earlier stage is better than non-augmented methods that need
significantly more observed data by up to 15% on the Area under the ROC curve.
