Metadata-Version: 2.1
Name: sfgad
Version: 0.1.2
Summary: A statistical framework for graph anomaly detection
Home-page: https://github.com/sudrich/sf-gad
Maintainer: Simon Sudrich
Maintainer-email: uzcyg@student.kit.edu
License: GPL-3.0
Download-URL: https://api.github.com/repos/sudrich/sf-gad/tarball/master
Description: ![SFGAD](https://raw.githubusercontent.com/sudrich/sf-gad/master/doc/img/logo.png)
        ---
        [![Travis](https://api.travis-ci.com/sudrich/sf-gad.svg?branch=master)](https://travis-ci.com/sudrich/sf-gad)
        
        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](https://raw.githubusercontent.com/sudrich/sf-gad/master/doc/img/sfgad.png)
        
        
        ### 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](https://pypi.org/project/sfgad) and on conda.
        
        ```sh
        conda install sfgad
        ```
        
        or 
        
        ```sh
        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:
        
        ```sh
        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).
        
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