Anomaly Detection in Dynamic Graphs via Transformer

• URL: http://arxiv.org/abs/2106.09876v1
• Date: Fri, 18 Jun 2021 02:27:19 GMT
• Title: Anomaly Detection in Dynamic Graphs via Transformer
• Authors: Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, Vincent CS Lee
• Abstract summary: We present a novel Transformer-based Anomaly Detection framework for DYnamic graph (TADDY) Our framework constructs a comprehensive node encoding strategy to better represent each node's structural and temporal roles in an evolving graphs stream. Our proposed TADDY framework outperforms the state-of-the-art methods by a large margin on four real-world datasets.
• Score: 30.926884264054042
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity. The 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 graph (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 four real-world datasets.

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