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Scientometrics
Authors: Shahadat Uddin, Liaquat Hossain, Alireza Abbasi, and Kim Rasmussen

-authorship. Co-authorship networks are an important class of social networks and have been analyzed extensively, both at network level and individual node level, to explore different statistical characteristics (Newman 2001 ) and behavioural patterns of

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; Beaver and Rosen 1978 ). Co-authorship network analysis has been widely used to study the cooperation of research groups in many disciplines. However, few researches have focused on the emergence of medical co-authorship networks in China. The

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461 – 473 . Kretschmer , H. 1997 Patterns of behaviour in co-authorship networks of invisible colleges Scientometrics 40

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Scientometrics
Authors: Zaida Chinchilla-Rodríguez, Anuska Ferligoj, Sandra Miguel, Luka Kronegger, and Félix de Moya-Anegón

collaboration networks in four subject areas. Building on these studies, the present research focuses on dynamic co-authorship networks in the specific field of information science in a Latin American country from 2001 to 2009. The blockmodeling approach

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Abstract  

We analyse the co-authorship networks of researchers affiliated at universities in Turkey by using two databases: the international SSCI database and the Turkish ULAKBIM database. We find that co-authorship networks are composed largely of isolated groups and there is little intersection between the two databases, permitting little knowledge diffusion. There seems to be two disparate populations of researchers. While some scholars publish mostly in the international journals, others target the national audience, and there is very little intersection between the two populations. The same observation is valid for universities, among which there is very little collaboration. Our results point out that while Turkish social sciences and humanities publications have been growing impressively in the last decade, domestic networks to ensure the dissemination of knowledge and of research output are very weak and should be supported by domestic policies.

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Scientometrics
Authors: Antonio Perianes-Rodríguez, Carlos Olmeda-Gómez, and Félix Moya-Anegón

Abstract  

The present paper proposes a method for detecting, identifying and visualizing research groups. The data used refer to nine Carlos III University of Madrid departments, while the findings for the Communication Technologies Department illustrate the method. Structural analysis was used to generate co-authorship networks. Research groups were identified on the basis of factorial analysis of the raw data matrix and similarities in the choice of co-authors. The resulting networks distinguished the researchers participating in the intra-departmental network from those not involved and identified the existing research groups. Fields of research were characterized by the Journal of Citation Report subject category assigned to the bibliographic references cited in the papers written by the author-factors. The results, i.e., the graphic displays of the structures of the socio-centric and co-authorship networks and the strategies underlying collaboration among researchers, were later discussed with the members of the departments analyzed. The paper constitutes a starting point for understanding and characterizing networking within research institutions.

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Summary  

Many studies have analyzed “direct” partnerships in co-authorship networks. On the other hand, the global network structure, including “indirect” links between researchers, has not yet been sufficiently studied. This study analyzes researchers' activities from the viewpoints considering their roles in the global structures of co-authorship networks, and compares the co-authorship networks between the theoretical and application areas in computer science. The modified HITS algorithm is used to calculate the two types of importance of researchers in co-authorship networks, i.e., the importance as the leader and that as the follower.

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Abstract  

Although there are many measures of centrality of individuals in social networks, and those centrality measures can be applied to the analysis of authors’ importance in co-authorship networks, the distribution of an author’s collaborative relationships among different communities has not been considered. This distribution or extensity is an important aspect of authors’ activity. In the present study, we will propose a new measure termed extensity centrality, taking into account the distribution of an author’s collaborative relationships. In computing the strength of collaborative ties, which is closely related to the extensity centrality, we choose Salton’s measure. We choose the ACM SIGKDD data as our testing data set, and analyze the result of authors’ importance from different points of view.

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Abstract  

This paper focuses on methods to study patterns of collaboration in co-authorship networks at the mesoscopic level. We combine qualitative methods (participant interviews) with quantitative methods (network analysis) and demonstrate the application and value of our approach in a case study comparing three research fields in chemistry. A mesoscopic level of analysis means that in addition to the basic analytic unit of the individual researcher as node in a co-author network, we base our analysis on the observed modular structure of co-author networks. We interpret the clustering of authors into groups as bibliometric footprints of the basic collective units of knowledge production in a research specialty. We find two types of coauthor-linking patterns between author clusters that we interpret as representing two different forms of cooperative behavior, transfer-type connections due to career migrations or one-off services rendered, and stronger, dedicated inter-group collaboration. Hence the generic coauthor network of a research specialty can be understood as the overlay of two distinct types of cooperative networks between groups of authors publishing in a research specialty. We show how our analytic approach exposes field specific differences in the social organization of research.

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Abstract  

About ten years ago a new research field called “webometrics” emerged. Similarities between methods used in webometrics and scientometrics or informetrics are evident from the literature. Are there also similarities between scientometric and Web indicators of collaboration for possible use in technology policy making? Usually, the bibliometric method used to study collaboration is the investigation of co-authorships. In this paper, Web hyperlinks and Web visibility indicators are examined to establish their usefulness as indicators of collaboration and to explore whether similarities exist between Web-based structures and bibliographic structures. Three empirical studies of collaboration between institutions and individual scientists show that hyperlink structures at the Web don’t reflect collaboration structures collected by bibliographic data. However Web visibility indicators of collaboration are different from hyperlinks and can be successfully used as Web indicators of collaboration.

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