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Detecting Community Structures Within Complex Networks Using a Discrete Unconscious Search Algorithm

Author

Listed:
  • Ehsan Ardjmand

    (Ohio University, USA)

  • William A. Young II

    (Ohio University, USA)

  • Najat E. Almasarwah

    (Ohio University, USA)

Abstract

Detecting the communities that exist within complex social networks has a wide range of application in business, engineering, and sociopolitical settings. As a result, many community detection methods are being developed by researchers in the academic community. If the communities within social networks can be more accurately detected, the behavior or characteristics of each community within the networks can be better understood, which implies that better decisions can be made. In this paper, a discrete version of an unconscious search algorithm was applied to three widely explored complex networks. After these networks were formulated as optimization problems, the unconscious search algorithm was applied, and the results were compared against the results found from a comprehensive review of state-of-the-art community detection methods. The comparative study shows that the unconscious search algorithm consistently produced the highest modularity that was discovered through the comprehensive review of the literature.

Suggested Citation

  • Ehsan Ardjmand & William A. Young II & Najat E. Almasarwah, 2021. "Detecting Community Structures Within Complex Networks Using a Discrete Unconscious Search Algorithm," International Journal of Operations Research and Information Systems (IJORIS), IGI Global Scientific Publishing, vol. 12(2), pages 15-32, April.
    • Handle: RePEc:igg:joris0:v:12:y:2021:i:2:p:15-32
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    References listed on IDEAS

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    6. Moradi, Mehdi & Parsa, Saeed, 2019. "An evolutionary method for community detection using a novel local search strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 457-475.
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