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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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-authors (‘poor embedding’), high values suggest a wide and persistent co-authors network (‘strong embedding’). From the empirical sample studied it might be inferred that higher φ values—at least until a certain limit—may be accompanied with higher citation

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temporal sequence analysis (Barabási et al. 2002 ; Palla et al. 2007 ). Although temporal sequence analyses have been applied to the study of co-author networks (Barabási, et al. 2002 ; Palla et al. 2007 ), analysis of the evolutionary mechanisms of the

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Scientometrics
Authors: Katy Börner, Weixia Huang, Micah Linnemeier, Russell Duhon, Patrick Phillips, Nianli Ma, Angela Zoss, Hanning Guo, and Mark Price

Abstract  

The enormous increase in digital scholarly data and computing power combined with recent advances in text mining, linguistics, network science, and scientometrics make it possible to scientifically study the structure and evolution of science on a large scale. This paper discusses the challenges of this ‘BIG science of science’—also called ‘computational scientometrics’ research—in terms of data access, algorithm scalability, repeatability, as well as result communication and interpretation. It then introduces two infrastructures: (1) the Scholarly Database (SDB) (http://sdb.slis.indiana.edu), which provides free online access to 22 million scholarly records—papers, patents, and funding awards which can be cross-searched and downloaded as dumps, and (2) Scientometrics-relevant plug-ins of the open-source Network Workbench (NWB) Tool (http://nwb.slis.indiana.edu). The utility of these infrastructures is then exemplarily demonstrated in three studies: a comparison of the funding portfolios and co-investigator networks of different universities, an examination of paper-citation and co-author networks of major network science researchers, and an analysis of topic bursts in streams of text. The article concludes with a discussion of related work that aims to provide practically useful and theoretically grounded cyberinfrastructure in support of computational scientometrics research, education and practice.

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in nano science and engineering. We also study the structure of citation network of the papers authored by these scientists. The co-author network is a type of social network and the paper citation network is a type of information science network

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Rousseau 2002 ; Brent et al. 2011 ). UCINET, combined with NetDraw is an outstanding social network software used to analyze data and produce social network diagrams (Creswick and Westbrook 2010 ). Co-author network There are

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-funding network shows how the SciSIP program is situated within the larger funding landscape and how it relates to other fields of interest. Collaboration networks such as co-PI and co-author networks can instead be used to represent the interconnectedness within

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molecules (Peyrard/Bouvet/Gilson). Fig. 5 Co-authors network (detail). The size of the nodes is proportional to the number of articles of our database authored by the author. The width of the links indicates

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

results) to measure efficiency of co-author networks citation records is the single largest source of information about how knowledge is created, shared, and distributed among researchers. The choice of single source (i.e., Scopus) for co

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the national and global scientific community. An indication of the higher reputation and status of return scholars is their centrality within local collaboration and co-authoring networks (cf. Podolny et al. 1996 ; Powell et al. 1996 ). Due

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