=Paper=
{{Paper
|id=Vol-2577/paper18
|storemode=property
|title=Defining Potential Academic Expert Groups based on Joint Authorship Networks Using Decision Support Tools
|pdfUrl=https://ceur-ws.org/Vol-2577/paper18.pdf
|volume=Vol-2577
|authors=Iryna Balagura,Sergii Kadenko,Oleh Andriichuk,Ivan Gorbov
|dblpUrl=https://dblp.org/rec/conf/its2/BalaguraKAG19
}}
==Defining Potential Academic Expert Groups based on Joint Authorship Networks Using Decision Support Tools==
<pdf width="1500px">https://ceur-ws.org/Vol-2577/paper18.pdf</pdf>
<pre>
222


  Defining Potential Academic Expert Groups based on
Joint Authorship Networks Using Decision Support Tools

           © Iryna Balagura[0000-0001-9627-2091], © Sergii Kadenko[0000-0001-7191-5636],
          © Oleh Andriichuk[0000-0003-2569-2026] and © Ivan Gorbov[0000-0001-6888-0866]

      Institute for Information Recording of National Academy of Sciences of Ukraine, Kyiv,
                                              Ukraine
                                  balaguraira@sgmail.com



         Abstract. We consider a co-authorship network in “Information security” field.
         The network is constructed using Scopus data for the Ukrainian affiliated au-
         thors. We define the key centrality indicators: centrality degree, betweenness
         centrality and weighted centrality. We have made rankings of authors by cen-
         trality indicators and citations and aggregated this data with decision support
         methods. We demonstrate the methodology and possibility of defining expert
         groups and academic schools using the scientific database’s content. We pro-
         pose to use decision support methods to define most communicative and cited
         scientists within co-authorship networks and demonstrate the way of ordinal
         factorial analysis usage for defining the relative weights of different centrality
         indicators within complex networks. Empirical results, obtained in the paper,
         indicate that there are no strong connection between given author’s centrality
         indicators and the number of citations to the author’s works. However, some
         centrality indicators are more influential and significant than others. The ap-
         proaches suggested in the paper can be applied to detection of central nodes in
         complex networks in general.

         Keywords: Co-authorship Network, Scopus, Centrality, Information Security,
         Decision Support, Ranking, Ordinal Factorial Analysis.


1        Introduction.

Subsequent paragraphs, however, are indented. Rapid development and general evo-
lution of science, as well as increase of the number of publications of all kinds led to
the necessity of complex consideration and organization of a system for statistical
analysis of document information stream [1, 2]. Scientometrics, being a conceptually
new development stage of analytical processing of documentation and scientific-
statistical information, is targeted at resolution of such issues as the most rational
selection of effective information, methodology of its evaluation, and efficient ways
of its analysis. Necessary conditions of adequate functioning and development indi-
cate extreme relevance of the problem of scientifically grounded, balanced, and effi-
cient state policy in this area [2]. Scientometrics finds practical application in qualita-


Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
                                                                                        223


tive evaluation of academic publications and definition of development dynamics of
both separate academic directions and science in general.
   Interaction of academics from different research areas, particularly, within co-
authorship networks, is an important and essential part of research process. In aca-
demic cooperation studies (in addition to bibliometrics and scientometrics) social
network research and expert estimation methods are used [3]. Particularly, one of the
most common methods is based on co-authorship network usage, where the nodes
represent the authors while edges represent co-authorship links, proportional to the
number of joint authorships (publications) [4]. Co-authorship network represents a
tool for defining the functional structure of scientific research as a whole, helps us
understand and forecast the ways of scientific information dissemination and evolu-
tion of academic schools, as well as define the relevance degree of specific research
areas. Study of respective networks allows us to define the key publications, research
fields, and authorship clusters [5, 6].
   Academic co-authorship networks represent an example of complex networks; they
can be analyzed using respective quantitative topological indicators, and further inter-
preted from content viewpoint [7, 8]. For instance, defined co-authorship clusters can
represent expert groups and academic schools [9]. The relevance of expert group def-
inition is beyond any doubt, because only professional expert examination can pro-
vide thorough and objective estimate of research results, while scientometric indica-
tors in this case will be only the tools of decision-making support [10].
   Academic schools, in their turn, are an essential developmental component of sci-
entific cognition and educational processes. In spite of this important key role of an
academic school, it is not acknowledged at state level (at least, in Ukraine), as there
are no registration mechanisms and ways of legal certification of an academic part-
nership between supervisors, their students and associates. These factors have a nega-
tive imact upon authority, image, and reputation of academic schools [11]. As we can
see, definition of academic schools is essential for optimization of joint academic
research activity, particularly, for structuring of collegial intellectual creative process,
targeted at obtaining and application of conceptually innovative, original knowledge,
significant for respective scientific fields.
   During search for expert groups and academic schools, certain “important” nodes
will be located in the “centre” of respective clusters [12]. Thus, defining potential co-
authorship network centre is the necessary condition for definition of respective ex-
pert groups and academic schools.
   Detection of academic communities is a relevant task while choosing experts for
evaluation of scientific research works, solving topical problems in certain areas, and
searching for partners to cooperate with. Besides that, in scientometrics it is important
to understand the processes that take place during academic collaboration. Academic
community structure, intensity of interaction in it, its leaders: these and other aspects
led to emergence of a whole new research area – the Science of Team Science
(SciTS) [13]. In order to study the key trends of academic cooperation and detect
“rich people’s clubs” as well as the most highly communicative academics, co-
authorship networks are used [14]. Usage of social networks featuring specialists’
profiles, such as ResearchGate (https://www.researchgate.net/) and LinkedIn
224


(https://www.linkedin.com/), simplifies the task of looking up specific researchers
[15]. Scientific profiles can be found in Google scholar, Scopus, Web of science, and
other databases. Besides that, there are resources for unification of information on
academics from different databases, such as ORCID (https://orcid.org/), “Bibliomet-
rics of Ukrainian science” (http://www.nbuviap.gov.ua/bpnu/), “Scientists of
Ukraine” (http://irbis-nbuv.gov.ua), AMiner (https://aminer.org/), and others. Aca-
demic publication databases represent the most thorough resource to look for academ-
ic research groups.


2         Basic centrality indicators of a co-authorship network

Potential expert groups of academics are defined based on centrality indicators of co-
authorship networks. In complex network theory there are several types of such coef-
ficients, defined as the level of their centrality in a graph. Some of the concepts were
based on complex network theory, while others were derived from sociological re-
search results. There are several basic types of centrality, which are widely used in
network analysis: centrality degree, betweenness (mediation) centrality, eigenvector
centrality, and others [16–18].
   Centrality degree defines the number of other network agents a certain person
(agent, individual) is connected to; in co-authorship networks it can be interpreted as
the degree of academic interaction. In the simplest case this is the degree of a certain
node, which characterizes an author’s communicability and can be used to forecast
this author’s productivity. According to research data, this characteristic does not
correlate with average citation level and cannot completely represent all the aspects of
authors’ communicability [19]. The disadvantage of this indicator for communicative
property definition is its inability to take the weights of graph edges (i.e. the number
of joint publications of authors) into account.
   In [16] the weighted degree of centrality is proposed. It is suggested to calculate
centrality in a weighted graph for a specific node as follows:
                                    CDωα (i) = ki(1−α ) siα
                                                                                    (1)
                                N
                           k i =  mij
                                j =1
The indicator includes                    , i.e. the sum of links to other nodes and
      N
si =  ωij
      j =1
             , i.e., the sum of weights of respective connections, while α is a coeffi-

cient, adjusted for each specific case.
   Centrality in the context of mediation (betweenness centrality) defines a node,
connecting sub-graphs to each other. In the context of academic cooperation, media-
tion or betweenness allows us to define authors that connect academic schools:
                                   C B (i) =  g jk (i)
                                             j <k
                                                                                (2)
                                                                                     225

        g jk (i)
where         is the number of the shortest ways in a graph, which pass through node
       i; i ≠ j , k
number              [18].
   While defining the importance of network nodes, we should consider communica-
bility, significance of adjacent nodes, but we should not lose the information on the
general productivity of authors, as the number of co-authors does not directly influ-
ence the efficiency of their work [17]. Definition of important nodes is a relevant
problem, calling for in-depth study of research subject, as there are many measures,
reflecting vertex (node) characteristics of different nature, while the adequacy of their
usage is based on their correspondence to the respective experiment purposes.


3       Defining the centers of co-authorship networks for
        “information security” section of Scopus database

We propose to use decision support methods to define potential academic expert
groups and academic research schools in co-authorship networks, and demonstrate the
application of “ordinal factorial analysis-based” approach to calculation of relative
weights of different centrality indicators of complex networks. For this purpose we
use data from Scopus. We consider an example of unification of rankings of centrality
and citation measures for researchers in the area of information security. Additionally,
the approach allows us to verify the degree of dependence between different centrality
measures among themselves and in comparison with citation indicators.
   Scopus is one of the largest and most reputable abstract databases in the world. In
Ukraine there is a demand for publishing papers cited in the database, particularly,
such publications are necessary for obtaining of academic degrees and titles. Scien-
tometric analysis of information security abstracts could show the development of this
field in Ukraine in comparison with other countries. Authors of the paper [20] made
scientometric research of the “Information security” area in Scopus. Number of pub-
lished papers by year, countries’ ranking by published papers, and other information
are presented in [20].
   Data from Scopus where gathered by the following keywords: information securi-
ty, data security, cyber security, network security, cryptography, information assur-
ance, data encryption, computer security. The search query is: (TITLE-ABS-KEY
(information AND security) OR TITLE-ABS-KEY (data AND security) OR TITLE-
ABS-KEY (security AND of AND data) OR TITLE-ABS-KEY (cybersecurity) OR
TITLE-ABS-KEY (network AND security) OR TITLE-ABS-KEY (cryptography)
OR TITLE-ABS-KEY (information AND assurance) OR TITLE-ABS-KEY (data
AND encryption) OR TITLE-ABS-KEY (computer AND security) AND ( LIMIT-
TO ( AFFILCOUNTRY , "Ukraine" ) ). 1261 documents were obtained by the query
for Ukrainian affiliated authors in comparison with 492395 documents by selected
keywords for all countries. The number of papers has increased in recent years as a
result of legislation change, but it still amounts to just 0.26 % of the word output.
VOSviewer and Pajek software were used for analyzing obtained data. Co-word net-
work is shown on Fig.1. Categories of Scopus subject area where defined as Comput-
226


er Science (832), Engineering (572), Mathematics (247), Physics and Astronomy
(154), Energy (120), Social Sciences (110) and others.




      Fig. 1. Co-word network of Ukrainian affiliated authors papers in Information security.

We assume that there are authors’ profiles in Scopus and authors have to organize
their profiles themselves. Ukrainian rules for translating surnames into English
changed several times, so spelling of surnames and names, namesake names may add
inaccuracies to the data. On Fig.2 we can see that co-authored network is loosely
connected. And on Fig.3 a large network fragement is presented. Existence of aca-
demic schools in “Information security” area can be witnessed based on “cliques”,
which include a productive author with multiple joint publications and a considerable
number of “smaller” nodes – “students”. Fig. 3 illustrates a fragment of co-authorship
network, “built around” Kharchenko V., Sachenko A., Gnatyuk S., and others. The
size of a node is proportional to the number of its connections (see Table 1). Concen-
tration of co-authorship links around one or several leaders can indicate the emer-
gence of separate academic schools. Papers of Ukrainian affiliated authors are written
in coauthorship with authors from Poland (102), Kazakhstan (48), United States (47),
Russian Federation (31), United Kingdom (28), China (25), Germany (25), Slovakia
(20), Czech Republic (19), and others.
                                                                                            227




 Fig. 2. Co-authorship network of Ukrainian affiliated authors: papers on Information security

Surnames of authors with maximum numbers of connections and joint publications
are listed in Table 1. Rankings of authors according to the number of connections and
weighted centrality degree are different, because during initial processing of infor-
mation from the database the weights of connections are defined as proportionally
distributed among all authors of each publication. The weighted centrality degree (i.e.
the number of papers published in collaboration) reflects the volume of an author’s
work, while the number of connections characterizes the circle of co-authors of a
specific author.

  Table 1. Authors of research papers on Information security that have the largest centrality
                                          measures.

Degree                   Weighted centrality degree             Beetwenness centrality
 Kharchenko V.      17     Kharchenko V.        76            Karpinski M.            0.155710
   Gnatyuk S.       10      Sachenko A.         46               Potii O.             0.155269
  Karpinski M.      10       Gnatyuk S.         29            Gorbenko Y.             0.151355
      Hu Z.          9       Bykovyy P.         25           Kharchenko V.            0.132409
   Opirskyy I.       9       Kochan V.          23             Bykovyy P.             0.085545
  Sachenko A.        8       Komar M.           23           Gancarczyk T.            0.055159
    Vasiliu Y        7      Kuznetsov A.        19             Vasiliu Y.             0.044856
   Kochan V.         7       Lakhno V.          18             Opirskyy I.            0.044449
  Gorbenko Y.        7      Karpinski M.        17            Akhmetov B.             0.041871
   Bykovyy P.        6      Akhmetov B.         16             Gnatyuk S.             0.041140
  Akhmetov B.        6          Hu Z.           14                Hu Z.               0.032517
  Illiashenko O.     6      Gorbenko Y.         13              Kavun S.              0.031289
228




 Fig. 3. A large fragment of co-authorship network of Ukrainian affiliated authors: papers on
                                    Information security.

Table 1 show that the author ranks changed according to selected centrality indicator.
Authors with high centrality indicators are linked with their colleges and form sepa-
rate groups or academic schools weakly connected to each other. The lists of authors
from Table 1, sorted by weighted centrality degrees, betweenness centrality and de-
gree consist of mostly same persons. Each centrality indicator reflects a certain aspect
of the authors’ communicability.
   Also we can define the most productive and cited authors: Table 2 shows authors
ranking by number of papers of each author, number of citations, and h-index of se-
lected papers.
   We can apply one or several indicators, and combine them with other approaches
according to a task. We propose to use decision support methods for integration of
several indicators. Decision support methods are currently used for priority setting in
different areas, particularly, in weakly structured domains. Calculation of relative
criterion weights is an essential component of such processes as strategic planning
and resource allocation [21]. Areas of application of these methods range from sus-
tainable development and industry development strategies [21] to information security
[22].
                                                                                              229


    Table 2. Authors of research papers on Information security that have the largest number of
                                       papers and citations
     Number of papers          Number of citations                 H-index of papers
    Kharchenko V.     55        Kussul N.        328             Kussul N.                8
     Gnatyuk S.       26       Usenko V.         276            Usenko V.                 8
     Sachenko A.      25      Oliynykov R.       243          Kharchenko V.               8
    Kuznetsov A.      22     Kharchenko V.       183         Nazarkevych M.               8
     Lakhno V.        21      Kuznetsov A.       162           Kuznetsov A.               7
     Gorbenko I.      21       Gorbenko I.       139          Gancarczyk T.               7
    Karpinski M.      17       Lakhno V.         114            Lakhno V.                 6
     Usenko V.        14      Gorbenko Y.        90             Gorbenko I.               6
    Gorbenko Y.       13    Nazarkevych M.       89             Mukhin V.                 5
     Bykovyy P.       13      Sachenko A.        84            Oliynykov R.               5
      Kussul N.       13       Gnatyuk S.        69            Gorbenko Y.                5
    Oliynykov R.      12       Komar M.          65            Sachenko A.                5




4        Application of decision support methods to defining the
         central nodes of co-authorship networks

In the context of our current problem, in order to get some information about relative
significance of the two criteria, listed in Table 1 (weighted centrality and between-
ness), we can try to calculate their weights based on ordinal factorial analysis methods
[23-25]. One of the reasons why we are switching to rankings from actual values is
because they differ very significantly across different authors (especially, between-
ness and citation number, that we are going to use a global criterion below). For in-
stance, betweenness indicator of N.Kussul is below 0.000001, while the same indica-
tor for Y.Gorbenko is 0.151355. In order to apply the approach [23-25], we need
some global ranking of alternatives, i.e. authors. To get the ranking, we can ask one or
several specialists from the respective field (in our case, information security), to
name the leading authors, and rank the authors accordingly. Using these data and data
from Table 1, it is possible to calculate the weights of the two criteria. This will allow
us to get a rough estimate of their significance, i.e., how important they are for the
authors’ reputation (reflected in the global ranking based on expert references).
   Let us consider a hypothetical example: during an interview, the expert was asked
to name up to 10 top specialists in information security area. He placed the authors in
the following order: …, Kharchenko, V., …, Gnatyuk, S., …, Sachenko, A., …,
Lakhno, V., …, Kussul, N., …, Oliynykov, R., … (see last column of Table 3).
230


       Table 3. A hypothetical ranking of specialists in the area of information security
                     Ranking by weighted Ranking by be-             Hypothetical expert
          Authors
                       centrality degree   tweenness                     ranking
        Kharchenko, V.           1                1                            1
          Gnatyuk, S.               3                    2                      2
         Sachenko, A.               2                    3                      3
          Lakhno, V.                5                    4                      5
          Kussul, N.                4                    6                      4
        Oliynykov, R.               6                    5                      6


We use the approach described in [23-25]. For calculation the criterion weights, we
should build the respective system of inequalities. The searched weights are the “cen-
ters of mass” of the part of the simplex, delimitated by the solution region of the sys-
tem of inequalities. Strict problem statement and step-by-step solution algorithms can
be found in [23-25].
   Based on data from Table 3, we can obtain the following criterion weights:
weighted centrality (i.e. number of links) – 0.417; betweenness – 0.583. That is, for
our hypothetical expert, the number of papers, published jointly with other authors (or
number of links to other authors) is a bit less important than an author’s role in media-
tion between academic schools.
   We can also use information from Scopus database to obtain objective ranking of
authors. Particularly, we can build a ranking based on citation statistics of each author
[26]. So, we can define, whether different aspects of an author’s centrality actually
influence his or her international rating. Let us rank several authors from Table 1
according to the number of citations in Scopus database and define their ratings ac-
cording to several centrality aspects, such as weighted centrality degree and between-
ness centrality (Table 4). It would be problematic to rank authors by ordinary central-
ity degree (first column of Table 1), because many authors have equal values of this
indicator. That is why we focus on the other two centrality measures (second and third
columns of Table 1). Let us rank 6 authors, featured in both the second and the third
columns of Table 1 by weighted centrality and betweenness, as well as by their re-
spective numbers of citations (see Table 4).
   Academics with the largest number of papers in their research fields have large
centrality indicators and big number of citations [27]. However, we cannot say there
is a direct dependence between these factors. If we try to find the weights of centrality
indicators according to the algorithm described in [23, 24], then the solution area
turns out to be empty. If we perform a minimal permutation within the ranking (a real
expert or rater might agree to perform it) – place author Bykovy in front of Gnatyuk
in the global ranking – then the weights are: weighted centrality degree – 0.367; be-
tweenness centrality – 0.633.
                                                                                             231


    Table 4. Ranking of authors according to citations in Scopus and key centrality indicators

                       Ranking by number         Ranking by          Ranking by betweenness
         Authors
                          of citations        weighted centrality
     Kharchenko V.               1                   1                         2

     Gorbenko Y.                 2                     6                       1

     Gnatyuk, S.                 3                     2                       5

     Akhmetov B.                 4                     4                       4

     Hu Z.                       5                     5                       6

     Bykovyy P.                  6                     3                       3


These results (weights) can be interpreted as follows. Empty solution area means that,
centrality does not strongly influence the number of citations. That is, the number of
references to an author’s work only loosely depends on the number of his publication
collaborations and on his academic mediation. Moreover, we should keep in mind,
that we are only analyzing authors’ centrality in one particular topic, i.e. information
security, while citations cover all the topics authors publish articles on. If we still
compare relative weights of the two centrality factors (although, the dependence is
very weak in general), then it turns out that the number of joint publications in Scopus
(which is reflected by weighted centrality degree) is less significant (weight equals
0.367) than author’s mediation role (weight equals 0.633).
   Thus, in addition to separate centrality aspects, we can define some generalized
centrality indicator of an author. It can be calculated as arithmetic mean, geometric
mean, or weighted sum of normalized ratings of an author according to criteria from
Table 1. This approach to aggregation of data on several objects (in our case – au-
thors) is described in [28] and can be applied, because criteria are preferentially inde-
pendent (which is a necessary and sufficient condition of linear convolution applica-
bility [29])).


5       Conclusions

The search of authors with high centrality levels and their respective co-authors al-
lows detecting of academic schools and expert groups. However, for detailed study
we should also use textual (linguistic) analysis of paper abstracts and expert estimates.
These approaches would also allow us to outline academic fields more precisely, and
assess their development.
   We have demonstrated the ranking of publications’ authors in the field of "Infor-
mation security" according to several indicators. All the indicators provide versatile
object characteristics. We proposed to create an overall ranking of scientists using
whole number of citations per author, weighted centrality and betweeness centrality.
We have applied decision support methods for detection of potential expert groups of
academics and academic schools with co-authorship networks. The results of final
ranking detect simultaneously scientists with wide communication network and high
Hirsch-index, who could lead a team or be an reputable expert.
232


   Approaches developed for elicitation of co-authorship networks and elaborated in
the paper can and should be used for detection of important nodes in other complex
networks.


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