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A statistical framework for graph anomaly detection

Project description

SFGAD

Travis

SFGAD is a tool for detecting anomalies in graph and graph streams with python.

I provides:

  • Efficient computation of graph features
  • Statistical models for detecting anomalous behavior
  • Graph scanning to detect connected graph anomalies
  • A customizable detection framework with 6 components
  • Several pre-defined configurations

Process


Process

Installation


Dependencies

  • Python: 3.5 or higher
  • NumPy: 1.8.2 or higher
  • SciPy: 0.13.3 or higher
  • Pandas: 0.22.0 or higher
  • NetworkX: 1.11.0 or higher

Installation (coming soon)

Installation of the latest release is available at the Python package index and on conda.

conda install sfgad

or

pip install sfgad

The source code is currently available on GitHub: https://github.com/sudrich/sf-gad

Testing

For testing use pytest from the source directory:

pytest sfgad

Acknowledgements

This work originated from the QuestMiner project (grant no. 01IS12051) and was partially funded by the German Federal Ministry of Education and Research (BMBF).

Project details


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