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