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.
An increasing number of quantitative and qualitative methods have been used for future-oriented technology analysis (FTA) to develop understanding of situations, enable creativity, engage experts, and provide interaction. FTA practitioners have used frequently one or a suitable mixture of these methods for their activities. Changing policy and strategy making contexts as well as enabling technologies increased the need and possibility for performing adaptive Foresight studies in order to improve decision making about the future and using making better use of limited resources. This study performs a scientometric analysis of the publications in the major FTA journals with the aim of understanding the dynamics of using Foresight methods across time. Among the other branches of FTA, including forecasting, futures, and technology assessment, a special emphasis is given on Foresight as a systematic and inclusive way of exploring long term futures, developing visions and formulating policies for action. The study aims at detecting the key Trends and Weak Signals regarding the use of existing methods and emerging ones with potential uses for Foresight activities. Further implications will be achieved with the generation of networks for quantitative and qualitative methods. This will demonstrate the most frequently combined Foresight methods by researchers and practitioners. Where possible the methods will also be cross-fertilised with the key thematic areas to illustrate the relationships between policy domains and industrial sectors covered by the scope of study with methodological choice. This output is considered to be taken as a methodological guide for any researchers, practitioners or policy makers, who might embark upon or involved in a Foresight activity. Further outputs of the study will include the identification of centres of excellence in the use of Foresight methods and collaboration networks between countries, institutions and policy domains. Overall, the paper demonstrates how scientometric tools can be used to understand the dynamics of evolution in a research field. Thus, it provides an overview of the use of methods in Foresight, and how it is distinguished from the other FTA activities; the evolutionary characteristics of methodological design and factors influencing the choice of methods; and finally a discussion on the future potentials for new cutting-edge approaches.
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.