Abstract
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 forunattributed nodes and the difficulty of learning discriminate knowledge from coupled spatial-temporal dynamic graphs. To overcomethese challenges, in this paper, we present a novelTransformer-basedAnomalyDetection framework forDYnamic graphs (TADDY).Our framework constructs a comprehensive node encoding strategy to better represent each node’s structural and temporal roles in anevolving graphs stream. Meanwhile, TADDY captures informative representation from dynamic graphs with coupled spatial-temporalpatterns via a dynamic graph transformer model. The extensive experimental results demonstrate that our proposed TADDY frameworkoutperforms the state-of-the-art methods by a large margin on six real-world datasets.
Yixin Liu
ARC Research Fellow
My research interests include machine learning, graph analysis and audio processing.
Shirui Pan
Professor and ARC Future Fellow
My research interests include data mining, machine learning, and graph analysis.