Anomaly Detection in Dynamic Graphs via Transformer

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 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.

13 pages, 5 figures

References in corpus (4)

• DeepWalk: Online Learning of Social Representations
• A Survey on Knowledge Graphs: Representation, Acquisition and Applications
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
• Graph Learning: A Survey

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• Label Information Enhanced Fraud Detection against Low Homophily in Graphs
• Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs
• Trivial Graph Features and Classical Learning are Enough to Detect Random Anomalies