Yifan Hong

ABNet: Mitigating Sample Imbalance in Anomaly Detection Within Dynamic Graphs

In dynamic graphs, detecting anomalous nodes faces challenges due to sample imbalance, stemming from the scarcity of anomalous samples and feature representation bias. Existing methods often use unsupervised or semi-supervised learning to extract anomalous samples from unlabeled data, but struggle t