  Detecting anomalies for dynamic graphs has drawn increasing attention due to
their wide applications in social networks, e-commerce, and cybersecurity.
Recent deep learning-based approaches have shown promising results over shallow
methods. However, they fail to address two core challenges of anomaly detection
in dynamic graphs: the lack of informative encoding for unattributed nodes and
the difficulty of learning discriminate knowledge from coupled spatial-temporal
dynamic graphs. To overcome these challenges, in this paper, we present a novel
Transformer-based Anomaly Detection framework for DYnamic graphs (TADDY). Our
framework constructs a comprehensive node encoding strategy to better represent
each node's structural and temporal roles in an evolving graphs stream.
Meanwhile, TADDY captures informative representation from dynamic graphs with
coupled spatial-temporal patterns via a dynamic graph transformer model. The
extensive experimental results demonstrate that our proposed TADDY framework
outperforms the state-of-the-art methods by a large margin on six real-world
datasets.
