Chapter26: Graph Neural Networks in Anomaly Detection

Shen Wang, University of Illinois at Chicago,
Philip S. Yu, University of Illinois at Chicago,


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.


  • Introduction
  • Issues
    • Data-specific issues
    • Task-specific issues
    • Model-specific issues
  • Pipeline
    • Graph Construction and Transformation
    • Graph Representation Learning
    • Prediction
  • Taxonomy
  • Case Studies
    • Case Study: Graph Embeddings for Malicious Accounts Detection
    • Case Study: Hierarchical Attention Mechanism based Cash-out User Detection
    • Case Study: Attention Heterogeneous Graph Neural Network for Malicious Program Detection
    • Case Study: Graph Matching Framework to Learn the Program Representation and Similarity Metric via Graph Neural Network for Unknown Malicious Program Detection
    • Case Study: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN
    • Case Study: GCN-based Anti-Spam for Spam Review Detection
  • Future Directions


author = "Wang, Shen and Yu, S. Philip",
editor = "Wu, Lingfei and Cui, Peng and Pei, Jian and Zhao, Liang",
title = "Graph Neural Networks in Anomaly Detection",
booktitle = "Graph Neural Networks: Foundations, Frontiers, and Applications",
year = "2022",
publisher = "Springer Singapore",
address = "Singapore",
pages = "557--578",

S. Wang and S. P. Yu, “Graph neural networks in anomaly detection,” in Graph Neural Networks: Foundations, Frontiers, and Applications, L. Wu, P. Cui, J. Pei, and L. Zhao, Eds. Singapore: Springer Singapore, 2022, pp. 557–578.