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

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.

Appendices

A Appendix: Synonymic referencing

Some problems associated with name recognition can occur in the database, when the same work is referred to by different short names. For example, the short names BOYD_D(2007)13 and BOYD_D(2008) 13:210 reference the same work of Danah Boyd, originally published in 2007, but in many cases it is referenced as being published in 2008. There were also cases when the short names were different due to discrepancies in the descriptions—such as GRANOVET_M(1973)78:1360 and GRANOVET_M(1973) 78:6, or COLEMAN_J(1988)94:95 and COLEMAN_J(1988)94:S95. The names of some authors were presented in a different way—for example, GRANOVET_M and GRANOVET_. We identified these cases for all works with large indegree frequencies in the Cite network.

To resolve these problems, we have to correct the data. There are two possibilities: (1) to make corrections in the local copy of original data (WoS file) or (2) to make an equivalence partition of nodes and shrink the set of works accordingly in all obtained networks. We used the second option (Batagelj et al. 2014, p.395–399). For the works with large frequencies we prepared lists of possible equivalents and manually determined equivalence classes. With a function in R we produced a Pajek’s partition of equivalent work names representing the same work. We used this partition to shrink the networks $\mathbf {Cite, WA, WJ}$, and $\mathbf {WK}$. The partitions $\mathbf {year, DC}$ and the vector $\mathbf {NP}$ were also shrunk.

B Appendix: Strong components

The citation network $\mathbf {CiteB}$ has 41 nontrivial strong components of different sizes, which are presented in Fig. 11. Different strong components are indicated by node colors. Edges (reciprocal, bidirectional links) are colored in blue, while arcs (directed links) are colored in pink. In the majority of cases, mutual referencing between the works is a characteristic of papers published in the same issue of a journal. For example, the first large strong component is combined of 12 works published in a special issue Social Networks: New Perspectives in Behavioral Ecology and Sociobiology (Volume 63, Issue 7, May 2009). Another example are the works BATAGELJ_V(1992)14:63 and BATAGELJ_V(1992)14:121, and FAUST_K(1992)14:5 and ANDERSON_C(1992)14:137 in the special issue on blockmodels in Social Networks (Volume 14, Issues 1-2, March-June 1992).

Other cases are connections due to the same author (TUMMINEL_M(2011):P01019 and TUMMINEL_M(2011)6:0017994, WILSON_A(2015)69:1617 and WILSON_A(2015)26:1577, PARSEGOV_S(2015):3475 and PARSEGOV_S(2017)62:2270) or journal (VEENSTRA_R(2013)23:399 and DAHL_V(2014)24:399). However, there are cases when the authors and journals of publications are different (ALMAHMOU_E(2015)33:152 and MOK_K(2017)35:463, XIA_W(2016) 3:46 and PROSKURN_A(2016)61:1524).

Fig. 11

CiteB net: Strong components

C Appendix: Main publications

See Table 3.

Table 3 Citation CiteT net: Overlapping of important subnetworks