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ANIMATE: Unsupervised Attributed Graph Anomaly Detection with Masked Graph Transformers.

Jingtao Hu1, Yi Zhang2, Chengzhang Zhu1

  • 1Academy of Military Sciences, Beijing 100091, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces ANIMATE, a novel unsupervised graph anomaly detection method using Graph Transformers. ANIMATE effectively identifies abnormal patterns in attributed graphs, enhancing IoT and sensor reliability.

Area of Science:

  • Graph Neural Networks
  • Machine Learning
  • Data Science

Background:

  • Attributed graphs are crucial for representing real-world sensor data.
  • Unsupervised graph anomaly detection (UGAD) identifies abnormal nodes without labels, vital for IoT and sensor reliability.
  • Traditional Graph Neural Networks (GNNs) face limitations like local aggregation and over-smoothing, hindering anomaly detection.

Purpose of the Study:

  • To introduce a novel unsupervised attributed graph anomaly detection method.
  • To overcome the limitations of traditional GNNs in capturing global graph structures.
  • To enhance the detection of anomalies in class-imbalanced datasets.

Main Methods:

  • Proposed unsupervised attributed graph Anomaly detectioN wIth Masked grAph TransformErs (ANIMATE).
Keywords:
graph anomaly detectionmasked autoencodertransformersunsupervised graph representation learning

Related Experiment Videos

  • Utilized Graph Transformers (GTs) for a global receptive field to capture distinguishable abnormal characteristics.
  • Employed masked auto-encoders for node feature reconstruction, focusing the model on normal patterns.
  • Implemented a self-paced enhancement scheme tailored for UGAD tasks.

    • Main Results:

    • ANIMATE demonstrated effectiveness on real-world benchmark datasets with organic anomalies.
    • The method outperformed state-of-the-art competitors in unsupervised graph anomaly detection.
    • Global perspective from Graph Transformers improved discrimination capacity compared to traditional GNNs.

    Conclusions:

    • ANIMATE offers a robust solution for unsupervised attributed graph anomaly detection.
    • The integration of Graph Transformers and masked auto-encoders enhances anomaly identification.
    • The proposed method contributes to improving the reliability of intelligent sensor systems.