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Journals publishing social network analysis

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Abstract

This paper presents the analysis of journals publishing articles on social network analysis (SNA). The dataset consists of articles from the Web of Science database obtained by searching for “social network*”, works intensively cited, written by the most prominent authors, and published in the main SNA journals up to July 2018. There were 8943 journals in 70,792 articles with complete descriptions. Using a two-mode network linking publications with journals and a one-mode network of citations between articles, we constructed and analysed the networks of citations and bibliographic coupling among journals. Based on the analysis of these networks, we identify the most prominent journals publishing SNA and reveal their relationships to each other. Special attention is given to the position of journal Social Networks among other journals in the field. We trace the development of some relationships through time and look at their distributions for selected journals. The results show that the field is growing, which can be seen by the annual rise of the number of journals publishing papers in SNA, and the average number of papers on SNA per journal (almost 3 in recent years). The journals which are currently present in the field belong to social and natural sciences. The social sciences group is represented mainly by journals from sociology and management. Other journals mainly come from the fields of physics, computer science, or are general scientific journals. While journals from social and computer sciences are connected with journals from the same fields, physics journals Physica A and Physical Review E have developed their own niche. SNA’s main outlet Social Networks takes a very coherent and important position. It had explicit primacy up to the 2000s; in recent years its relative input has declined significantly due to the large number of papers published in other journals in the field.

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Acknowledgements

We would like to express our special gratitude to our colleague, professor Anuška Ferligoj (University of Ljubljana and the International Laboratory for Applied Network Research, Higher School of Economics, Moscow) for her advice and comments which greatly improved the manuscript. This work is prepared within the framework of the HSE University Basic Research Program, and supported in part by the Slovenian Research Agency (research program P1-0294 and research projects J1-9187, J5-2557, and J7-8279) and COSTNET (COST Action CA15109). We appreciate the help of David Connolly (Academic Writing Center, Higher School of Economics, Moscow) with the proofreading of the article. All computations were performed using the program for large network analysis and visualization Pajek (De Nooy et al. 2018) and Python code based on library Nets (Batagelj 2020a). Visualizations of distributions and temporal quantities were produced in R.

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Authors and Affiliations

  1. National Research University Higher School of Economics, 11 Pokrovsky Bulvar, Pokrovka Complex, Moscow, 101000, Russian Federation

    Daria Maltseva & Vladimir Batagelj

  2. Institute of Mathematics, Physics and Mechanics, Jadranska 19, 1000, Ljubljana, Slovenia

    Vladimir Batagelj

  3. Andrej Marušič Institute, University of Primorska, 6000, Koper, Slovenia

    Vladimir Batagelj

Authors
  1. Daria Maltseva
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  2. Vladimir Batagelj
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Correspondence to Daria Maltseva.

Appendix: synonymic referencing

Appendix: synonymic referencing

Some problems associated with name recognition can occur in the dataset. The original network \(\mathbf {WJ}\) had 70,425 journals. Due to inconsistencies in journal titles in different descriptions, it contained sets of nodes denoting the same journal. To get the list of these nodes, we constructed for each journal title its short code, which was formed out of the first two letters of each word in the journal’s title,—such as SONEANANMI for SOCIAL NETWORK ANALYSIS AND MINING,—and then sorted them so that the journals with the same code were grouped together. We manually inspected all the journals with at least one of their names cited at least 200 times. To get these numbers we computed in Pajek the 2-mode network Cite*WJc and determined the vector wIndegJ.vec with weighted indegrees for journals. We obtained a list of candidates for inspection with 5482 titles. To additionally reduce the number of titles to inspect we considered only titles that appeared in at least 3 citations. This gave a list journalK100.csv with 3714 titles, that were manually inspected. After checking, this list was reduced to 1688 titles. Some examples of the journal titles grouped according to their codes are presented in Fig. 13.

However, some journal titles can also appear in an abbreviated form based on their initials—for example, the Journal of the American Statistical Association could be coded as JAMSTAS according to its short title J AM STAT ASS and as JA according to its abbreviation JASA. That is why we also produced a list of frequent journal names of at most 5 letters, and chose all the cases that could be considered as abbreviations, such as CACM, JACM, JASA, LNCS, NIPS, JASSS, IJCAI, BMJ, JOSS and performed a manual search for the abbreviations of these journals in the original list of 70,425 journals. We grouped all the journal titles which included the same abbreviations—some examples are presented in Fig. 14 (there were different codes generated for different titles). The results of the search were added to the first list, and finally a list and the corresponding partition for network shrinking were produced. This resulted in a reduced list of 69,146 journals.

Fig. 13
figure 13

Examples of different journal title writing

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Fig. 14
figure 14

Examples of different journal title writings with abbreviations

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Maltseva, D., Batagelj, V. Journals publishing social network analysis. Scientometrics 126, 3593–3620 (2021). https://doi.org/10.1007/s11192-021-03889-z

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