=Paper=
{{Paper
|id=Vol-2268/paper17
|storemode=property
|title=Fourty Years of Network Science: Analysis of Journal Contribution to the Field
|pdfUrl=https://ceur-ws.org/Vol-2268/paper17.pdf
|volume=Vol-2268
|authors=Marina Kalinina,Vladimir Kuznetsov,Valentina Kuskova
|dblpUrl=https://dblp.org/rec/conf/aist/KalininaKK18
}}
==Fourty Years of Network Science: Analysis of Journal Contribution to the Field==
https://ceur-ws.org/Vol-2268/paper17.pdf
Fourty Years of Network Science:
Analysis of Journal Contribution to the Field
Marina Kalinina, Vladimir Kuznetsov, and Valentina Kuskova
National Research University Higher School of Economics, Russian Federation
Abstract. The goal of this study is to analyze the Social Networks
Journal contribution to the sphere of social network analysis and as a
result, improve the methodology that reflects the theoretical contribu-
tion of empirical articles within three dimensions: theory building, theory
testing and applied method. In addition, the paper includes the exami-
nation of journal co-evolution within the field of social network analysis.
In this study, we build a model of social network journals and identify
the place that Social Networks occupies within this network, with its
unique impact.
Keywords: Social Networks, citations, coevolution
1 Introduction
The idea of academic “impact” has been on the forefront of many fields for
a number of years. To illustrate, multiple studies, especially in the field of man-
agement, have looked at the scholarly impact. There were two levels of research
- on the author level: "What causes a management article to be cited - article,
author, or journal?" [2] , and on the journal level: "The influence of manage-
ment journals in the 1980s and 1990s" [3]. Nevertheless, at the current moment
there are no studies that have looked at the journal-field co-evolution. In this
paper, we present the preliminary results of an ongoing research project based
on data from a sample of 41 top-cited articles of the Social Network Journal are
presenting the dynamics of citation rates of the journal within the timeframe
and citation network of the Social Network Journal.
2 Methodology
The issue of journal impact is of great interest to researchers in many fields;
the field of social networks is no exception. Usually, the choice of a journal for
analysis is rather difficult, because usually several journals occupy a place of
The article was prepared within the framework of the Basic Research Program at
the National Research University Higher School of Economics (HSE) and supported
within the framework of a subsidy by the Russian Academic Excellence Project
’5-100’.
prominence and have different impact numbers reported in different databases.
Such ratings are often subjective. This is not the case with the field of social
networks; the Social Networks is the best journal to analyze, for a number of
reasons. It was founded in 1978 and for a number of years, was the only journal for
social network scholars to publish their research. Thus, it is possible to track the
influence of the journal on the field and the influence of the field on the journal.
Moreover, this journal is interdisciplinary and covers many subjects, such as
mathematical methods of network analysis, applications of SNA to different
subject matter areas, development and testing of new theory, etc.
To evaluate the impact of Social Networks Journal we examined the Eigen-
factor indices (Fig.1) [4]. To calculate these indices, journals are rated according
to the number of incoming citations, with citations from highly ranked journals
weighted to make a higher contribution to the Eigenfactor than those from lower
ranked journals. Article Influence is calculated by dividing the Eigenfactor score
by percentage of all articles recorded in the Journal Citation Reports published
in a specific journal. As is seen from the graph, these indices are influenced by
the number of articles published per year, thus, the higher is the number of arti-
cles, the higher is the measure of influence. Moreover, also obvious is the decline
in impact of Social Networks from 2013 to 2015. One possible explanation is an
appearance of a new journal aimed at studying the field of network analysis –
Network Science. In other words, such indicators of influence can hardly serve
as a reliable or meaningful source of information about the journal-field mutual
influence and co-evolution.
Fig. 1. Figure 1. Normalized Eigenfactor and Article Influence
For this study, we opted to use methods of network analysis to evaluate the
impact of a network-subject journal, build the network of studies and authors,
evaluate the longitudinal impact by building a co-evolutionary model of social
influence, and as a result, trace the development of the field and determine the
antecedents of scholarly influence. We created our measurement model on the
network that consists of 41 articles with 41 citations or more and a matched
set of top 200 articles with citations and without for trends in the following
categories:
– Methodology: improvement of an existing method, testing of an existing
method, application of an existing method to a new context;
– Proposition of a fundamentally new methodology;
– Field: Sociology (social influence, social selection), Management.
The next step in the study was to conduct the network analysis of the citing
articles and fields. The goal was to track the field that published the most studies
in Social Networks Journal and the influence of publications in that area. Our
network model includes 125 peer-reviewed journals that have strong connection
with Social Networks. Preliminary results indicate that each article from Social
Networks Journal is connected, on average, with six other articles from other
journals which cite the first article. In order to examine the research data, we
used the “Mendeley” reference manager; for building sample articles the data
was uploaded from Science Direct according to article citation level, and we
used the Polinode platform for visualization. The results obtained are presented
and discussed below.
3 Preliminary results
Results of the analysis of 41 top-sited articles are presented in Table 1.
As is evident from the table, high citation count is limited to specific topics
and issues, i.e. the Issue 34 (1) covers the topic of spatial processes (Figure 2).
Articles that test existing methodology are cited the most, however, the num-
ber of citations normalized by article type is higher for articles that improve
existing methodolody. Results are presented in Table 2.
Further analysis shows that the impact of the journal appears to reach most
academic fields (Figure 3). Articles from Social Network Journal are colored
blue, articles from the other journals are colored in accordance with the legend
on the graph. The principal number of citations come from fields of Management
(Academy of Management Journal, Academy of Management Review, etc.), Po-
litical Science (American Political Science Review), Sociology (American Journal
of Sociology, American Sociologist, etc.), Biology, Statistics, Computer Science
and a vast number of other fields. Thus, the Social Networks Journal seems to
connect different fields of research by its interdisciplinary nature.
Issue Sum of Year Improve New Con- Testing of Social In- Social Se- Management
Citations Existing text Existing fluence lection
Method Method
Social 980 2012 2 - 6 4 2 2
Networks
34(1)
Social 110 2012 - - - 1 1 1
Networks
34(2)
Social 313 2012 2 1 3 2 - -
Networks
34(3)
Social 472 2012 4 - 4 4 1 -
Networks
34 (4)
Social 209 2013 1 - - - 1 -
Networks
35 (1)
Social 389 2013 3 1 2 - - -
Networks
35 (2)
Social 89 2013 1 - 1 2 - -
Networks
35 (3)
Social 50 2013 - - - 1 - -
Networks
35 (4)
Social 106 2014 1 - 1 2 - -
Networks
36
Social 55 2014 - - 1 1 - -
Networks
37
Social 64 2014 - - 1 - - -
Networks
38
Social 43 2014 1 - - 1 - -
Networks
39
Social 43 61 2015 - - - - 1
Networks
Total 2941 - 15 2 19 18 6 3
Fig. 2. Figure 2. Sum of citations per issue
Type of New Improve No im- New con- Social in- social se- Management
Editorial
article* method existing prove- text fluence lection
method ment
Number - 15 2 19 18 6 3 1
of articles
Number - 766,5 66,5 559,5 726 677 95,5 51
of ci-
tations
normal-
ized by
article
type
Fig. 3. Figure 2. Network model of Social Networks Journal articles citation
4 Conclusion
Though the results are only preliminary, it is already apparent that Social
Networks as a journal has made a significant contribution to the development of
social network analysis field. Though the journal publishes many different types
of articles, those that apply new methods to new context appear to get cited
the most. The range of fields that cite Social Networks is also rather broad; it
appears that all social science fields are about equally affected by the journal’s
influence. We hope that further development of the model and testing of individ-
ual parameters will help understand the contribution that the journal, and all
authors who published in it, have made to the development of science in social
network analysis.
References
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testing: A five-decade study of the Academy of Management Journal. Academy of
Management Journal, 50(6), 1281-1303, 2007.
2. Judge, T. A., Cable, D. M., Colbert, A. E., and Rynes, S. L.: What causes a man-
agement article to be cited—article, author, or journal?. Academy of Management
Journal, 50(3), 491-506 (2007).
3. Podsakoff, P. M., Mackenzie, S. B., Bachrach, D. G., and Podsakoff, N. P. The
influence of management journals in the 1980s and 1990s. Strategic Management
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4. Elsevier website. The Annals of Thoracic Surgery. Article Influence and Eigenfactor
URL: https://journalinsights.elsevier.com/journals/0003-4975/article_influence
(Accessed: 01.03.2018)