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        "description": "![SFGAD](https://raw.githubusercontent.com/sudrich/sf-gad/master/doc/img/logo.png)\n---\n[![Travis](https://api.travis-ci.com/sudrich/sf-gad.svg?branch=master)](https://travis-ci.com/sudrich/sf-gad)\n\nSFGAD is a tool for detecting anomalies in **graph** and **graph streams** with python.\n\n\nI provides:\n\n* Efficient computation of graph **features**\n* Statistical models for detecting **anomalous behavior**\n* Graph scanning to detect **connected graph anomalies**\n* A customizable detection framework with **6** components\n* Several pre-defined **configurations**\n\n### Process\n---\n\n![Process](https://raw.githubusercontent.com/sudrich/sf-gad/master/doc/img/sfgad.png)\n\n\n### Installation\n---\n\n#### Dependencies\n\n* Python: 3.5 or higher\n* NumPy: 1.8.2 or higher\n* SciPy: 0.13.3 or higher\n* Pandas: 0.22.0 or higher\n* NetworkX: 1.11.0 or higher\n\n#### Installation (coming soon)\n\nInstallation of the latest release is available at the [Python\npackage index](https://pypi.org/project/sfgad) and on conda.\n\n```sh\nconda install sfgad\n```\n\nor \n\n```sh\npip install sfgad\n```\n\nThe source code is currently available on GitHub:\nhttps://github.com/sudrich/sf-gad\n\n#### Testing\n\nFor testing use pytest from the source directory:\n\n```sh\npytest sfgad\n```\n\n## Acknowledgements\n\nThis 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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