IDEAS home Printed from https://ideas.repec.org/a/spr/advdac/v18y2024i3d10.1007_s11634-023-00556-4.html
   My bibliography  Save this article

An analytic strategy for data processing of multimode networks

Author

Listed:
  • Vincenzo Giuseppe Genova

    (University of Palermo)

  • Giuseppe Giordano

    (University of Salerno)

  • Giancarlo Ragozini

    (University of Naples Federico II)

  • Maria Prosperina Vitale

    (University of Salerno)

Abstract

Complex network data structures are considered to capture the richness of social phenomena and real-life data settings. Multipartite networks are an example in which various scenarios are represented by different types of relations, actors, or modes. Within this context, the present contribution aims at discussing an analytic strategy for simplifying multipartite networks in which different sets of nodes are linked. By considering the connection of multimode networks and hypergraphs as theoretical concepts, a three-step procedure is introduced to simplify, normalize, and filter network data structures. Thus, a model-based approach is introduced for derived bipartite weighted networks in order to extract statistically significant links. The usefulness of the strategy is demonstrated in handling two application fields, that is, intranational student mobility in higher education and research collaboration in European framework programs. Finally, both examples are explored using community detection algorithms to determine the presence of groups by mixing up different modes.

Suggested Citation

  • Vincenzo Giuseppe Genova & Giuseppe Giordano & Giancarlo Ragozini & Maria Prosperina Vitale, 2024. "An analytic strategy for data processing of multimode networks," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(3), pages 745-767, September.
  • Handle: RePEc:spr:advdac:v:18:y:2024:i:3:d:10.1007_s11634-023-00556-4
    DOI: 10.1007/s11634-023-00556-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11634-023-00556-4
    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/s11634-023-00556-4?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. Giuseppe Giordano & Ilaria Primerano & Pierluigi Vitale, 2021. "A Network-Based Indicator of Travelers Performativity on Instagram," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 631-649, August.
    2. Nicholas J Foti & James M Hughes & Daniel N Rockmore, 2011. "Nonparametric Sparsification of Complex Multiscale Networks," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-10, February.
    3. Magnani, Matteo & Wasserman, Stanley, 2017. "Introduction to the special issue on multilayer networks," Network Science, Cambridge University Press, vol. 5(2), pages 141-143, June.
    4. Mario A. Maggioni & Stefano Breschi & Pietro Panzarasa, 2013. "Multiplexity, Growth Mechanisms and Structural Variety in Scientific Collaboration Networks," Industry and Innovation, Taylor & Francis Journals, vol. 20(3), pages 185-194, April.
    5. Zsolt Tibor Kosztyán & Beáta Fehérvölgyi & Tibor Csizmadia & Kinga Kerekes, 2021. "Investigating collaborative and mobility networks: reflections on the core missions of universities," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3551-3564, April.
    6. Giuseppe Giordano & Maria Vitale, 2011. "On the use of external information in social network analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(2), pages 95-112, July.
    7. Vladimir Batagelj & Monika Cerinšek, 2013. "On bibliographic networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 845-864, September.
    8. Valentina Meliciani & Daniela Cagno & Andrea Fabrizi & Marco Marini, 2022. "Knowledge networks in joint research projects, innovation and economic growth across European regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 68(3), pages 549-586, June.
    9. Yue Ma & De Liu, 2017. "Introduction to the special issue on Crowdfunding and FinTech," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-4, December.
    10. Vincenzo G. Genova & Michele Tumminello & Fabio Aiello & Massimo Attanasio, 2021. "A network analysis of student mobility patterns from high school to master’s," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1445-1464, December.
    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. Termeh Shafie & David Schoch, 2021. "Multiplexity analysis of networks using multigraph representations," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1425-1444, December.
    2. Pavel N. Krivitsky & Laura M. Koehly & Christopher Steven Marcum, 2020. "Exponential-Family Random Graph Models for Multi-Layer Networks," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 630-659, September.
    3. Santana, Monica & Cobo, Manuel J., 2020. "What is the future of work? A science mapping analysis," European Management Journal, Elsevier, vol. 38(6), pages 846-862.
    4. Gangan Prathap & Somenath Mukherjee, 2020. "Letter to the Editor: Comments on the paper of Batagelj—on fractional approach to analysis of linked networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2717-2722, September.
    5. Tianlei Pi & Haoxuan Hu & Jingyi Lu & Xue Chen, 2022. "The Analysis of Fintech Risks in China: Based on Fuzzy Models," Mathematics, MDPI, vol. 10(9), pages 1-13, April.
    6. A. Velez-Estevez & P. García-Sánchez & J. A. Moral-Munoz & M. J. Cobo, 2022. "Why do papers from international collaborations get more citations? A bibliometric analysis of Library and Information Science papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7517-7555, December.
    7. Monika Cerinšek & Vladimir Batagelj, 2015. "Network analysis of Zentralblatt MATH data," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 977-1001, January.
    8. Carla Martínez-Climent & Ana Zorio-Grima & Domingo Ribeiro-Soriano, 2018. "Financial return crowdfunding: literature review and bibliometric analysis," International Entrepreneurship and Management Journal, Springer, vol. 14(3), pages 527-553, September.
    9. Miltos Ladikas & Julia Hahn & Lei Huang, 2022. "Assessing the Impact of Technology Assessment, Responsible Research and Innovation and Sustainability Research: Towards a Common Methodological Approach," Sustainability, MDPI, vol. 14(4), pages 1-16, February.
    10. Hayasaka, Satoru, 2016. "Explosive percolation in thresholded networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 1-9.
    11. Damianos P. Sakas & Ioannis Dimitrios G. Kamperos & Dimitrios P. Reklitis & Nikolaos T. Giannakopoulos & Dimitrios K. Nasiopoulos & Marina C. Terzi & Nikos Kanellos, 2022. "The Effectiveness of Centralized Payment Network Advertisements on Digital Branding during the COVID-19 Crisis," Sustainability, MDPI, vol. 14(6), pages 1-23, March.
    12. Wang, Tao & Xiao, Shiying & Yan, Jun & Zhang, Panpan, 2021. "Regional and sectoral structures of the Chinese economy: A network perspective from multi-regional input–output tables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    13. Ricardo Costa-Climent & Carla Martínez-Climent, 2018. "Sustainable profitability of ethical and conventional banking," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 12(4), December.
    14. Ion Georgiou & Joaquim Heck & Andrej Mrvar, 2019. "The Analysis of Interconnected Decision Areas: A Computational Approach to Finding All Feasible Solutions," Group Decision and Negotiation, Springer, vol. 28(3), pages 543-563, June.
    15. Chengkai Zhang & Yanjun Zhang & Yu Li & Shan Li, 2023. "Coupling Coordination between Fintech and Digital Villages: Mechanism, Spatiotemporal Evolution and Driving Factors—An Empirical Study Based on China," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    16. Daria Maltseva & Vladimir Batagelj, 2020. "Towards a systematic description of the field using keywords analysis: main topics in social networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 357-382, April.
    17. Deli Wang & Ke Peng & Kaiye Tang & Yewei Wu, 2022. "Does Fintech Development Enhance Corporate ESG Performance? Evidence from an Emerging Market," Sustainability, MDPI, vol. 14(24), pages 1-21, December.
    18. Pierre Barbillon & Sophie Donnet & Emmanuel Lazega & Avner Bar-Hen, 2017. "Stochastic block models for multiplex networks: an application to a multilevel network of researchers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 295-314, January.
    19. Monica Santana & Alvaro Lopez‐Cabrales, 2019. "Sustainable development and human resource management: A science mapping approach," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 26(6), pages 1171-1183, November.
    20. Zádor, Zsófia & Zhu, Zhen & Smith, Matthew & Gorgoni, Sara, 2022. "A weighted and normalized Gould–Fernandez brokerage measure," Greenwich Papers in Political Economy 37794, University of Greenwich, Greenwich Political Economy Research Centre.

    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:advdac:v:18:y:2024:i:3:d:10.1007_s11634-023-00556-4. 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.