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On the relationship between the structural and socioacademic communities of a coauthorship network

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  • Rodriguez, Marko A.
  • Pepe, Alberto

Abstract

This article presents a study that compares detected structural communities in a coauthorship network to the socioacademic characteristics of the scholars that compose the network. The coauthorship network was created from the bibliographic record of a multi-institution, interdisciplinary research group focused on the study of sensor networks and wireless communication. Four different community detection algorithms were employed to assign a structural community to each scholar in the network: leading eigenvector, walktrap, edge betweenness and spinglass. Socioacademic characteristics were gathered from the scholars and include such information as their academic department, academic affiliation, country of origin, and academic position. A Pearson’s χ2test, with a simulated Monte Carlo, revealed that structural communities best represent groupings of individuals working in the same academic department and at the same institution. A generalization of this result suggests that, even in interdisciplinary, multi-institutional research groups, coauthorship is primarily driven by departmental and institutional affiliation.

Suggested Citation

  • Rodriguez, Marko A. & Pepe, Alberto, 2008. "On the relationship between the structural and socioacademic communities of a coauthorship network," Journal of Informetrics, Elsevier, vol. 2(3), pages 195-201.
    • Handle: RePEc:eee:infome:v:2:y:2008:i:3:p:195-201
    DOI: 10.1016/j.joi.2008.04.002
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    Cited by:

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    6. Sahatqija, Kosovare & Kadriu, Arbana, 2019. "Exploring Gender Role in Co-Authorship Networks for Computing Books: A Case Study in DBLP," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2019), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, pages 33-39, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    7. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2020. "Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-26, June.
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    16. Alberto Pepe & Marko A. Rodriguez, 2010. "Collaboration in sensor network research: an in-depth longitudinal analysis of assortative mixing patterns," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 687-701, September.
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    18. Bilicz, Dávid, 2021. "A hálózatok és a kapcsolatok szerepe az innovációban és a tudás áramlásában. Szisztematikus szakirodalmi áttekintés [The role of networks and partnerships in innovation and knowledge flow - a syste," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 674-698.
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    21. Yan, Xiangbin & Zhai, Li & Fan, Weiguo, 2013. "C-index: A weighted network node centrality measure for collaboration competence," Journal of Informetrics, Elsevier, vol. 7(1), pages 223-239.

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