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A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?

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  • Kristel Vignery
  • Wim Laurier

Abstract

In order to understand and represent the importance of nodes within networks better, most of the studies that investigate graphs compute the nodes’ centrality within their network(s) of interest. In the literature, the most frequent measures used are degree, closeness and/or betweenness centrality, even if other measures might be valid candidates for representing the importance of nodes within networks. The main contribution of this paper is the development of a methodology that allows one to understand, compare and validate centrality indices when studying a particular network of interest. The proposed methodology integrates the following steps: choosing the centrality measures for the network of interest; developing a theoretical taxonomy of these measures; identifying, by means of Principal Component Analysis (PCA), latent dimensions of centrality within the network of interest; verifying the proposed taxonomy of centrality measures; and identifying the centrality measures that best represent the network of interest. Also, we applied the proposed methodology to an existing graph of interest, in our case a real friendship student network. We chose eighteen centrality measures that were developed in SNA and are available and computed in a specific library (CINNA), defined them thoroughly, and proposed a theoretical taxonomy of these eighteen measures. PCA showed the emergence of six latent dimensions of centrality within the student network and saturation of most of the centrality indices on the same categories as those proposed by the theoretical taxonomy. Additionally, the results suggest that indices other than the ones most frequently applied might be more relevant for research on friendship student networks. Finally, the integrated methodology that we propose can be applied to other centrality indices and/or other network types than student graphs.

Suggested Citation

  • Kristel Vignery & Wim Laurier, 2020. "A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-32, December.
  • Handle: RePEc:plo:pone00:0244377
    DOI: 10.1371/journal.pone.0244377
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    1. Daniel Laurentino de Jesus Xavier & João Gilberto Mendes dos Reis & André Henrique Ivale & Aparecido Carlos Duarte & Gabriel Santos Rodrigues & Jonatas Santos de Souza & Paula Ferreira da Cruz Correia, 2023. "Agricultural International Trade by Brazilian Ports: A Study Using Social Network Analysis," Agriculture, MDPI, vol. 13(4), pages 1-13, April.
    2. Paula Ferreira da Cruz Correia & João Gilberto Mendes dos Reis & Pedro Sanches Amorim & Jaqueline Severino da Costa & Márcia Terra da Silva, 2024. "Impacts of Brazilian Green Coffee Production and Its Logistical Corridors on the International Coffee Market," Logistics, MDPI, vol. 8(2), pages 1-12, April.

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