IDEAS home Printed from https://ideas.repec.org/a/igg/joris0/v12y2021i2p15-32.html
   My bibliography  Save this article

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, vol. 12(2), pages 15-32, April.
    • Handle: RePEc:igg:joris0:v:12:y:2021:i:2:p:15-32
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJORIS.20210401.oa2
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sun, H.J. & Gao, Z.Y., 2007. "Dynamical behaviors of epidemics on scale-free networks with community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 381(C), pages 491-496.
    2. 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.
    3. Zhou, HongFang & Li, Jin & Li, JunHuai & Zhang, FaCun & Cui, YingAn, 2017. "A graph clustering method for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 551-562.
    4. Barigozzi, Matteo & Fagiolo, Giorgio & Mangioni, Giuseppe, 2011. "Identifying the community structure of the international-trade multi-network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2051-2066.
    5. Bilal, Saoud & Abdelouahab, Moussaoui, 2017. "Evolutionary algorithm and modularity for detecting communities in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 89-96.
    6. Dabaghi Zarandi, Fataneh & Kuchaki Rafsanjani, Marjan, 2018. "Community detection in complex networks using structural similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 882-891.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marco Dueñas & Giorgio Fagiolo, 2013. "Modeling the International-Trade Network: a gravity approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 155-178, April.
    2. Juan Lucio & Raúl Mínguez & Asier Minondo & Francisco Requena, 2016. "Networks and the Dynamics of Firms' Export Portfolio: Evidence for Mexico," The World Economy, Wiley Blackwell, vol. 39(5), pages 708-736, May.
    3. Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2015. "Structure of global buyer-supplier networks and its implications for conflict minerals regulations," Papers 1505.02274, arXiv.org.
    4. Nicole Palan & Nadia Simoes & Nuno Crespo, 2021. "Measuring fifty years of trade globalisation," The World Economy, Wiley Blackwell, vol. 44(6), pages 1859-1884, June.
    5. Wu, Jianshe & Li, Xiaoxiao & Jiao, Licheng & Wang, Xiaohua & Sun, Bo, 2013. "Minimum spanning trees for community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2265-2277.
    6. Kyle F Davis & Paolo D'Odorico & Francesco Laio & Luca Ridolfi, 2013. "Global Spatio-Temporal Patterns in Human Migration: A Complex Network Perspective," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-8, January.
    7. Ali Kharrazi & Brian D. Fath & Harald Katzmair, 2016. "Advancing Empirical Approaches to the Concept of Resilience: A Critical Examination of Panarchy, Ecological Information, and Statistical Evidence," Sustainability, MDPI, vol. 8(9), pages 1-17, September.
    8. Li, Yuke & Wu, Tianhao & Marshall, Nicholas & Steinerberger, Stefan, 2017. "Extracting geography from trade data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 205-212.
    9. Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2013. "Null models of economic networks: the case of the world trade web," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 75-107, April.
    10. Yuichi Ikeda & Hiroshi Iyetomi, 2018. "Trade network reconstruction and simulation with changes in trade policy," Evolutionary and Institutional Economics Review, Springer, vol. 15(2), pages 495-513, December.
    11. Alessandro Chessa & Irene Crimaldi & Massimo Riccaboni & Luca Trapin, 2014. "Cluster Analysis of Weighted Bipartite Networks: A New Copula-Based Approach," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-12, October.
    12. Zhong, Weiqiong & An, Haizhong & Shen, Lei & Fang, Wei & Gao, Xiangyun & Dong, Di, 2017. "The roles of countries in the international fossil fuel trade: An emergy and network analysis," Energy Policy, Elsevier, vol. 100(C), pages 365-376.
    13. Guo, Yajuan & Yang, Licai & Hao, Shenxue & Gao, Jun, 2019. "Dynamic identification of urban traffic congestion warning communities in heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 98-111.
    14. Wang, Tao & Chen, Shanshan & Wang, Xiaoxia & Wang, Jinfang, 2020. "Label propagation algorithm based on node importance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    15. Adelaide Baronchelli & Teodora Erika Uberti, 2021. "International Economic Integration: Comparing Exports and FDI Networks in the New Millennium," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 13(11), pages 1-30, November.
    16. Gao, Cuixia & Sun, Mei & Shen, Bo, 2015. "Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis," Applied Energy, Elsevier, vol. 156(C), pages 542-554.
    17. Zhong, Weiqiong & An, Haizhong & Shen, Lei & Dai, Tao & Fang, Wei & Gao, Xiangyun & Dong, Di, 2017. "Global pattern of the international fossil fuel trade: The evolution of communities," Energy, Elsevier, vol. 123(C), pages 260-270.
    18. Jalili, Mahdi, 2017. "Spike phase synchronization in multiplex cortical neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 325-333.
    19. Hao, Xiaoqing & An, Haizhong & Jiang, Meihui & Sun, Xiaoqi, 2024. "Supply shock propagation in the multi-layer network of global steel product chain: Additive effect of trade and production," Resources Policy, Elsevier, vol. 89(C).
    20. John J Bartholdi & Pisit Jarumaneeroj & Amar Ramudhin, 2016. "A new connectivity index for container ports," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 18(3), pages 231-249, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:joris0:v:12:y:2021:i:2:p:15-32. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.