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Scientometrics

, Volume 121, Issue 2, pp 1085–1128 | Cite as

Social network analysis as a field of invasions: bibliographic approach to study SNA development

  • Daria MaltsevaEmail author
  • Vladimir Batagelj
Article

Abstract

In this paper, the results of a study on the development of social network analysis (SNA) and its evolution over time, using the analysis of bibliographic networks are presented. The dataset consists of articles from the Web of Science Clarivate Analytics database obtained by searching for the keyword “social network*” and those published in the main journals in the field (in total 70,000+ publications). From the data, we constructed several networks. In this paper, the focus is on the analysis of the citation network. Analyzing the obtained network, we evaluated the SNA field’s growth and identified the most cited works. Using the normalized Search path count weights, we extracted the main path, key-route paths, and link islands in the citation network. Based on the probabilistic flow node values, we also identified the most important articles. Our results show that the number of published papers almost doubles each 3 years. We confirmed the finding that the authors from the social sciences, who were most active through the whole history of the field development, experienced the “invasion” of physicists from the 2000s. However, starting from the 2010s, a new very active group of animal social network analysts took the leading position.

Keywords

Development of scientific fields Social network analysis Citation network Search path count Probabilistic flow Web of science 

Mathematics Subject Classification

91Dxx 91D30 01A90 90B10 

JEL Classification

C45 C55 D85 

Notes

Acknowledgements

We would like to express our special thanks of gratitude to our collegues professor Anuška Ferligoj (University of Ljubljana and International Laboratory for Applied Network Research, Moscow) and associate professor Valentina Kuskova (International Laboratory for Applied Network Research, Moscow) for their advice and comments that greatly improved the manuscript. We are also very thankful to the anonymous reviewer of this paper for his/her comments. This work is supported in part by the Slovenian Research Agency (research program P1-0294 and research Projects J1-9187 and J7-8279) and by Russian Academic Excellence Project ‘5-100’.

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsMoscowRussia
  2. 2.Institute of Mathematics, Physics and MechanicsLjubljanaSlovenia
  3. 3.Andrej Marušič InstituteUniversity of PrimorskaKoperSlovenia

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