IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i5d10.1007_s13198-022-01633-1.html
   My bibliography  Save this article

An ensemble model to optimize modularity in dynamic bipartite networks

Author

Listed:
  • Neelu Chaudhary

    (Manav Rachna University)

  • Hardeo Kumar Thakur

    (Manav Rachna University)

  • Rinky Dwivedi

    (Maharaja Surajmal Institute of Technology)

Abstract

Distinct non-random quantitative interactions at diverse timestamps formulate real-world dynamic complex networks. The most frequently used class of methods for discovering communities in dynamic networks is modularity optimization that evaluates the quality of the partition of network nodes into distinct communities. The bipartite networks have bipartite modularity and bipartite modularity optimization respectively. Newman's modularity is a consistently used algorithm to evaluate modules of unipartite networks yet it is ineffective for assessing the division of bipartite networks with two types of vertices. Many community detection methods suggest bipartite modularity to accommodate this issue. They usually employ information about the existence or lack of interactions between nodes. In quantitative networks, weighted modularity is a potential approach for measuring the quality of community partitions (Lu et al. IEEE, 179–184, 2013). This study offers an ensemble model for detecting one-mode communities and optimizing modularity in dynamic bipartite weighted networks. By using collaborative weighted projection, bipartite networks get projected into two weighted one-mode networks. The results of experiments both on real-world dynamic network data and synthetic data demonstrate that the modularity of the method is significantly greater than that of current techniques and the communities discovered contain vertices of comparable kinds exhibiting the suggested algorithm's performance is ample.

Suggested Citation

  • Neelu Chaudhary & Hardeo Kumar Thakur & Rinky Dwivedi, 2022. "An ensemble model to optimize modularity in dynamic bipartite networks," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2248-2260, October.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:5:d:10.1007_s13198-022-01633-1
    DOI: 10.1007/s13198-022-01633-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-022-01633-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-022-01633-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Diana Purwitasari & Chastine Fatichah & Surya Sumpeno & Christian Steglich & Mauridhi Hery Purnomo, 2020. "Identifying collaboration dynamics of bipartite author-topic networks with the influences of interest changes," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1407-1443, March.
    2. Zhang, Peng & Wang, Jinliang & Li, Xiaojia & Li, Menghui & Di, Zengru & Fan, Ying, 2008. "Clustering coefficient and community structure of bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6869-6875.
    3. Alessandro Chessa & Irene Crimaldi & Massimo Riccaboni & Luca Trapin, 2014. "Cluster analysis of weighted bipartite networks: a new copula-based approach," Working Papers 3/2014, IMT School for Advanced Studies Lucca, revised Apr 2014.
    4. Zhou, Cangqi & Feng, Liang & Zhao, Qianchuan, 2018. "A novel community detection method in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1679-1693.
    5. Henriette Heer & Lucas Streib & Ralf B Schäfer & Stefan Ruzika, 2020. "Maximising the clustering coefficient of networks and the effects on habitat network robustness," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-16, October.
    6. Arthur, Rudy, 2020. "Modularity and projection of bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    7. 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.
    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. Camacho-Villa, Tania Carolina & Zepeda-Villarreal, Ernesto Adair & Díaz-José, Julio & Rendon-Medel, Roberto & Govaerts, Bram, 2023. "The contribution of strong and weak ties to resilience: The case of small-scale maize farming systems in Mexico," Agricultural Systems, Elsevier, vol. 210(C).
    2. Ramadiah, Amanah & Caccioli, Fabio & Fricke, Daniel, 2020. "Reconstructing and stress testing credit networks," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    3. Yubo Peng & Bofeng Zhang & Furong Chang, 2021. "Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density," Future Internet, MDPI, vol. 13(4), pages 1-21, March.
    4. Arthur, Rudy, 2023. "Discovering block structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    5. Yang, Xin & Wen, Shigang & Zhao, Xian & Huang, Chuangxia, 2020. "Systemic importance of financial institutions: A complex network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    6. Zhai, Li & Yan, Xiangbin, 2022. "A directed collaboration network for exploring the order of scientific collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    7. Le Song & Guilong Zhu & Xiao Yin, 2024. "Evaluating the wisdom of scholar crowds from the perspective of knowledge diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5103-5139, September.
    8. Long, Yong-Shang & Jia, Zhen & Wang, Ying-Ying, 2018. "Coarse graining method based on generalized degree in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 655-665.
    9. Pei Chen & Shan Gao & Fan Jiang & Yifang Ma, 2024. "Measuring the labor market outcomes of universities: evidence from China’s listed company executives," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5715-5730, September.
    10. Maihami, Vafa & Yaghmaee, Farzin, 2018. "Automatic image annotation using community detection in neighbor images," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 123-132.
    11. Cui, Yaozu & Wang, Xingyuan, 2016. "Detecting one-mode communities in bipartite networks by bipartite clustering triangular," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 307-315.
    12. Fessina, Massimiliano & Zaccaria, Andrea & Cimini, Giulio & Squartini, Tiziano, 2024. "Pattern-detection in the global automotive industry: A manufacturer-supplier-product network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    13. Cui, Yaozu & Wang, Xingyuan, 2014. "Uncovering overlapping community structures by the key bi-community and intimate degree in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 7-14.
    14. Liebig, Jessica & Rao, Asha, 2016. "Predicting item popularity: Analysing local clustering behaviour of users," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 523-531.
    15. Zhang, Dawei & Xie, Fuding & Zhang, Yong & Dong, Fangyan & Hirota, Kaoru, 2010. "Fuzzy analysis of community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(22), pages 5319-5327.
    16. Xiaomei Bai & Fuli Zhang & Jinzhou Li & Zhong Xu & Zeeshan Patoli & Ivan Lee, 2021. "Quantifying scientific collaboration impact by exploiting collaboration-citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7993-8008, September.
    17. Qiao, Jian & Meng, Ying-Ying & Chen, Hsinchun & Huang, Hong-Qiao & Li, Guo-Ying, 2016. "Modeling one-mode projection of bipartite networks by tagging vertex information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 270-279.
    18. Guo, Liying & Wang, Yang & Li, Meiling, 2024. "Exploration, exploitation and funding success: Evidence from junior scientists supported by the Chinese Young Scientists Fund," Journal of Informetrics, Elsevier, vol. 18(2).
    19. Arthur, Rudy, 2020. "Modularity and projection of bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    20. Li, Kaiwen & Liu, Kai & Wang, Ming, 2021. "Robustness of the Chinese power grid to cascading failures under attack and defense strategies," International Journal of Critical Infrastructure Protection, Elsevier, vol. 33(C).

    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:spr:ijsaem:v:13:y:2022:i:5:d:10.1007_s13198-022-01633-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.