https://github.com/vishwass/QUAC

Parallel Graph processing algorithm in Apache Spark (pySpark)to find the Maximal Gamma Quasi cliques used for community detection in graphs of low to medium edge density .This project was designed using Map reduce framework for huge graphs.Because of the exponential complexity of the algorithm, we preferred a big data platform like Apache spark. The implementation has its application in social network to cluster similar profiles.Skills:-Python, Apache Spark(pySpark).
https://github.com/vishwass/QUAC

Last synced: 21 days ago
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Parallel Graph processing algorithm in Apache Spark (pySpark)to find the Maximal Gamma Quasi cliques used for community detection in graphs of low to medium edge density .This project was designed using Map reduce framework for huge graphs.Because of the exponential complexity of the algorithm, we preferred a big data platform like Apache spark. The implementation has its application in social network to cluster similar profiles.Skills:-Python, Apache Spark(pySpark).

• Host: GitHub
• URL: https://github.com/vishwass/QUAC
• Owner: vishwass
• License: mit
• Created: 2015-06-18T22:39:05.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2019-10-05T22:09:13.000Z (over 4 years ago)
• Last Synced: 2024-02-24T19:38:43.409Z (4 months ago)
• Language: Python
• Homepage:
• Size: 4.49 MB
• Stars: 2
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• community-detection-awesome-provided - [Python Reference
• awesome-community-detection - [Python Reference

README

        
# QUAC

Parallel Graph processing algorithm in Apache Spark (pySpark)to find the Maximal  Gamma Quasi cliques used for community detection in graphs of low to medium edge density .This project was designed using Map reduce framework for huge graphs.Because of the exponential complexity of the algorithm, we preferred a big data platform like Apache spark. The implementation has its application in social network to cluster similar profiles.  

The link to the IEEE document for the algorithm implemented can be found [here](https://ieeexplore.ieee.org/abstract/document/6012859)