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GRAM: An interpretable approach for graph anomaly detection using gradient attention maps.

Yifei Yang1, Peng Wang2, Xiaofan He3

  • 1Electronic Information School, Wuhan University, Hubei, China; Data Science Research Center, Duke Kunshan University, Jiangsu, China.

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Summary
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This study introduces an interpretable graph anomaly detection method using attention maps from graph neural networks. The novel approach enhances detection performance and provides insights into decision-making processes.

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Anomaly detectionGraph neural networksInterpretability

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Area of Science:

  • Data Mining
  • Machine Learning
  • Network Analysis

Background:

  • Graph data analysis is vital for identifying unusual patterns.
  • Current anomaly detection methods lack consistent performance and interpretability.
  • Understanding anomaly detection decisions is crucial but often difficult.

Purpose of the Study:

  • To propose a novel, interpretable graph anomaly detection method.
  • To enhance anomaly detection performance through interpretability.
  • To provide insights into the decision-making process of anomaly detection.

Main Methods:

  • Extracting an attention map from graph neural network gradients.
  • Using the attention map for anomaly scoring.
  • Applying the method across various anomaly detection settings.

Main Results:

  • Theoretical analysis validated the method using synthetic data.
  • Extensive evaluations on real-world datasets showed superior performance.
  • The proposed method outperformed state-of-the-art graph anomaly detection techniques.

Conclusions:

  • The novel interpretable approach significantly improves graph anomaly detection.
  • Attention maps derived from GNN gradients offer effective anomaly scoring.
  • The method demonstrates flexibility and superior performance on diverse datasets.