SNA 17

The dataset has two parts. It is based on the SN5 data collected for the Viszards session at the Sunbelt 2008 (Batagelj et al. 2014), and contains all the records obtained for the query “social network*” and articles from the journal Social Networks, until 2007. We additionally searched for the works without full descriptions which were most frequently cited and papers on SNA of around 100 social networkers. The final version of SN5 contained 7950 works with full description (hits), 193,376 works (hits and cited only), 75,930 authors, 14,651 journals, and 29,267 keywords.

The SN5 data were extended in June 2018 using the same search scheme. Starting from 2007, 576 articles from Social Networks journal were added. Additionally, in 2018, all the articles from the network-related journals contained in the WoS were included—such as Network Science, Social Network Analysis and Mining, Journal of Complex Networks (in total, 431 articles). Other network-related journals—such as Computational Social Networks, Applied Network Science, Online Social Networks and Media, Journal of Social Structure, and Connections— were considered, but were not included since they are not abstracted in WoS. As terminal (cited only) works can be highly cited and in this sense important, we additionally collected full descriptions for works with high (at least 150) citation frequencies using WoS. If a description of work was not available in WoS, we constructed a corresponding description without CR data, searching for the work in Google Scholar (exporting it in the RIS bibliographic format and converting it into the WoS format). We also included manual descriptions of important works without the CR field from the dataset BM on blockmodeling (Batagelj et al. 2019). We should note that additional influential papers, usually published earlier, could be overlooked by our search queries because they do not use the contemporary terminology. Finally, our dataset included 70,792 WoS records with complete descriptions.

Using WoS2Pajek 1.5 (Batagelj 2007), we transformed our data into a collection of networks: one-mode citation network Cite on works (from the field CR of the WoS record) and two-mode networks—the authorship network WA on works × authors (from the field AU), the journal network WJ on works × journals (from the field CR or J9), and the keyword network WK on works × keywords (from the fields ID, DE or TI). After all iterations of cleaning, we finally constructed the dataset used for further analysis. We also removed multiple links and loops from the networks and labeled the obtained basic networks CiteN, WAn, WKn, and WJn.

For the cited only works (DC = 0) only partial descriptions are provided: we have information about the first author, the journal and the publication year, and we have no information on the keywords (as there are no titles in ISI names of terminal works). That is why for further analysis we constructed networks that contain only works with a complete description (DC = 1). All the link weights in the obtained networks were set to 1. We labeled these reduced networks CiteR, WAr, WKr, and WJr.

• NewNets 4. Nov 2018 (117M)

• ReducedNets 4. Nov 2018 (11M)

• cluN 14. Sep 2018 (1M)

• BoundaryNet 4. Nov 2018 (16M)

1. Maltseva, D., Batagelj, V.: Social network analysis as a field of invasions: bibliographic approach to study SNA development. Scientometrics, 121(2019)2, 1085-1128 online, 1-44. DOI 10.1007/s11192-019-03193-x
2. Maltseva, D., Batagelj, V.: Towards a systematic description of the field using keywords analysis: main topics in social networks. Scientometrics, 2020. on-line; DOI: 10.1007/s11192-020-03365-0
3. Batagelj, V., Maltseva, D.: Temporal bibliographic networks. Journal of Informetrics, Volume 14, Issue 1, February 2020, 101006. WWW, https://doi.org/10.1016/j.joi.2020.101006
4. Batagelj, V.: On fractional approach to analysis of linked networks. Scientometrics (2020) on-line; DOI: 10.1007/s11192-020-03383-y
5. Wouter De Nooy, Andrej Mrvar, Vladimir Batagelj: Exploratory Social Network Analysis with Pajek; Revised and Expanded Edition for Updated Software. Structural Analysis in the Social Sciences, CUP, Amazon, July 2018.
6. Vladimir Batagelj, Patrick Doreian, Anuška Ferligoj and Nataša Kejžar: Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network Evolution. Wiley Series in Computational and Quantitative Social Science. Wiley, October 2014.