Comparative Analysis of Graph Clustering Algorithms for Detecting Communities in Social Networks

  • Menta Sai Vineeth
  • Krishnappa RamKarthik
  • M. Shiva Phaneendra Reddy
  • Namala Surya
  • L. R. Deepthi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1097)


Community detection in social networks is often thought of a challenged domain that has not been explored completely. In today’s digital world, it is forever laborious to make a relationship between people or objects. Community detection helps us to find such relationships or build such relationships. It also can facilitate bound organizations to induce the opinion of their product from certain people. Many algorithms have emerged over the years which detect communities in the social networks. We performed a comparative analysis between six completely different bunch of algorithms for detecting communities in social network by taking into account parameters like run-time, cluster size, normalized mutual data , adjusted random score and average score.


Community Detection Clustering Agglomerative Divisive NMI ARS 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Menta Sai Vineeth
    • 1
  • Krishnappa RamKarthik
    • 1
  • M. Shiva Phaneendra Reddy
    • 1
  • Namala Surya
    • 1
  • L. R. Deepthi
    • 1
  1. 1.Department of Computer Science EngineeringAmrita Vishwa VidyapeethamAmritapuriIndia

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