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Institutional and country collaboration in an online service of scientific profiles: Google Scholar Citations

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  • Ortega, José Luis
  • Aguillo, Isidro F.

Abstract

The purpose of this paper is to analyse and describe the topological properties of the institutional and national collaboration network from the profiles extracted from Google Scholar Citations (GSC). 19,912 unique profiles with “co-authors” were obtained from a web crawl performed in March 2012. Several statistical and network analysis techniques were used to map and analyse these collaboration relationships at the country and institution level. Results show that The United States dominates the world scientific map and that every research institution is grouped by national, geographical and cultural criteria. A clustering phenomenon based on the self-similarity and fractal properties of scale-free networks is also observed. We conclude that GSC is a suitable tool for collaboration studies only at macro level between countries and institutions.

Suggested Citation

  • Ortega, José Luis & Aguillo, Isidro F., 2013. "Institutional and country collaboration in an online service of scientific profiles: Google Scholar Citations," Journal of Informetrics, Elsevier, vol. 7(2), pages 394-403.
  • Handle: RePEc:eee:infome:v:7:y:2013:i:2:p:394-403
    DOI: 10.1016/j.joi.2012.12.007
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    5. Alberto Martín-Martín & Enrique Orduna-Malea & Emilio Delgado López-Cózar, 2018. "A novel method for depicting academic disciplines through Google Scholar Citations: The case of Bibliometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1251-1273, March.
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    7. Weiwei Yan & Qian Liu & Ruoyu Chen & Shengwei Yi, 2020. "Social networks formed by follower–followee relationships on academic social networking sites: an examination of corporation users," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2083-2101, September.
    8. Yun Liu & Yijie Cheng & Zhe Yan & Xuanting Ye, 2018. "Multilevel Analysis of International Scientific Collaboration Network in the Influenza Virus Vaccine Field: 2006–2013," Sustainability, MDPI, vol. 10(4), pages 1-19, April.
    9. Chen Yang & Tingting Liu & Xiaohong Chen & Yiyang Bian & Yuewen Liu, 2020. "HNRWalker: recommending academic collaborators with dynamic transition probabilities in heterogeneous networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 429-449, April.

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