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Community detection in attributed networks for global transfer market

Author

Listed:
  • G. P. Clemente

    (Universitá Cattolica del Sacro Cuore, Largo Gemelli)

  • A. Cornaro

    (University of Milano - Bicocca)

Abstract

In this work we analyse the global soccer player transfer market providing a network approach that takes into account both the number of transfers and the related costs for football players in the world market. We propose a community detection methodology that considers different features of the network. We cluster countries according to similarities in their roles in the transfer market and to the presence of indirect connections due to common neighbours. Numerical results show a strict relation between the composition of clusters and the economic value of the football leagues of different countries. Indeed, we observe that, on average, leagues with a similar economic value belongs to the same cluster. The analysis has been also extended providing a comparison based on the world trade network. We observe that prominent European players in the economic trades are also relevant in the soccer transfer network.

Suggested Citation

  • G. P. Clemente & A. Cornaro, 2023. "Community detection in attributed networks for global transfer market," Annals of Operations Research, Springer, vol. 325(1), pages 57-83, June.
    • Handle: RePEc:spr:annopr:v:325:y:2023:i:1:d:10.1007_s10479-021-04439-9
    DOI: 10.1007/s10479-021-04439-9
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    References listed on IDEAS

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    1. Xiao Fan Liu & Yu-Liang Liu & Xin-Hang Lu & Qi-Xuan Wang & Tong-Xing Wang, 2016. "The Anatomy of the Global Football Player Transfer Network: Club Functionalities versus Network Properties," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-14, June.
    2. Clemente, G.P. & Grassi, R., 2018. "Directed clustering in weighted networks: A new perspective," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 26-38.
    3. Bothorel, Cecile & Cruz, Juan David & Magnani, Matteo & Micenkovã , Barbora, 2015. "Clustering attributed graphs: Models, measures and methods," Network Science, Cambridge University Press, vol. 3(3), pages 408-444, September.
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