Graph Neural Networks in Anomaly Detection

Anomaly detection is an important task, which tackles the problem of discovering “different from normal” signals or patterns by analyzing a massive amount of data, thereby identifying and preventing major faults. Anomaly detection is applied to numerous high-impact applications in areas such as cyber-security, finance, e-commerce, social network, industrial monitoring, and many more mission-critical tasks. While multiple techniques have been developed in past decades in addressing unstructured collections of multi-dimensional data, graph-structure-aware techniques have recently attracted considerable attention. A number of novel techniques have been developed for anomaly detection by leveraging the graph structure. Recently, graph neural networks (GNNs), as a powerful deep-learning-based graph representation technique, has demonstrated superiority in leveraging the graph structure and been used in anomaly detection. In this chapter, we provide a general, comprehensive, and structured overview of the existing works that apply GNNs in anomaly detection.

Authors and Affiliations

1. Department of Computer Science, University of Illinois at Chicago, Chicago, USA

   Shen Wang & Philip S. Yu

Corresponding author

Correspondence to Shen Wang .

Editors and Affiliations

1. JD Silicon Valley Research Center, Mountain View, CA, USA

   Lingfei Wu

2. Tsinghua University, Beijing, China

   Peng Cui

3. Simon Fraser University, Burnaby, Canada

   Jian Pei

4. Emory University, Atlanta, USA

   Liang Zhao

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