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Artificial Bee Colony-Based Influence Maximization Approach for Social Coding Portal GitHub

Author

Listed:
  • Anuja Arora

    (Jaypee Institute of Information Technology, Noida, India)

  • Riyu Bana

    (Jaypee Institute of Information Technology, Meerut, India)

  • Habib Shah

    (King Khalid University, Abha, Saudi Arabia)

  • Divakar Yadav

    (Madan Mohan Malaviya University of Technology, Gorakhpur, India)

Abstract

Influence maximization is the main source of virality of any social media post/marketing activity. In recent trends, influence maximization has moved towards analytic approach instead of just being a suggestive metaphor for various social media paradigm. In this article, ego-centric approach and a bio-inspired algorithm is applied on social coding community, Github, for influence maximization. First, developers' and projects' egocentric network-based studies are conducted to find out influential developer and project based on varying social media measures. Second, artificial bee colony (ABC) bio-inspired algorithm is used to select social bees (i.e., developers set and projects set in rapid convergence towards an optimal solution to achieve influence maximization). Algorithm result ensures the best solution in terms of social community connectivity path optimization in less run time while finding most influential social bees.

Suggested Citation

  • Anuja Arora & Riyu Bana & Habib Shah & Divakar Yadav, 2019. "Artificial Bee Colony-Based Influence Maximization Approach for Social Coding Portal GitHub," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 10(2), pages 34-47, April.
    • Handle: RePEc:igg:jsir00:v:10:y:2019:i:2:p:34-47
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