Abstract
Anomaly detection in dynamic graphs becomes
very critical in many different application scenarios, e.g., recommender systems, while it also raises
huge challenges due to the high flexible nature of
anomaly and lack of sufficient labelled data. It is
better to learn the anomaly patterns by considering all possible hints including the structural, content and temporal features, rather than utilizing
heuristic rules over the partial features. In this paper, we propose AddGraph, a general end-to-end
anomalous edge detection framework using an extended temporal GCN (Graph Convolutional Network) with an attention model, which can capture
both long-term patterns and the short-term patterns
in dynamic graphs. In order to cope with insuffi-
cient explicit labelled data, we employ a selective
negative sampling and margin loss in training of
AddGraph in a semi-supervised fashion. We conduct extensive experiments on real-world datasets,
and illustrate that AddGraph can outperform the
state-of-the-art competitors in anomaly detection
significantly