Towards a systematic description of the field using keywords analysis: main topics in social networks

Daria Maltseva () and Vladimir Batagelj ()
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Daria Maltseva: National Research University Higher School of Economics
Vladimir Batagelj: National Research University Higher School of Economics

Scientometrics, 2020, vol. 123, issue 1, No 17, 357-382

Abstract: Abstract This paper presents the results of the analysis of keywords used in Social Network Analysis (SNA) articles included in the WoS database and main SNA journals, from 1970 to 2018. 32,409 keywords were obtained from 70,792 works with complete descriptions. We provide a list of the most used keywords and show subgroups of keywords which are connected to each other. To go deeper, we place the keywords into the contexts of selected groups of authors and journals. We use temporal analysis to get an insight into some keyword usage. The distributions of the number of keyword types and tokens over time show fast growth starting from 2010s, which is the result of the growth in the number of articles on SNA topics and applications of SNA in various scientific fields. Even though the most frequently used keywords are trivial or general, the approaches used for the normalization of network link weights allow us to extract keywords representing substantive topics and methodological issues in SNA.

Keywords: Social network analysis; Bibliographic networks; Temporal network analysis; Keyword co-occurrence networks; Fractional approach; TF–IDF index (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11192-020-03365-0

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