The structure of scientific collaboration networks

Newman, M. E. J.

doi:10.1515/9781400841356.221

Cited by 135 publications

(169 citation statements)

References 6 publications

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“…It shows an increasing rate of new authors entering the fields. It further shows that in all three fields the relative size of the giant component converges to values about 90% and higher 3 (see Table 1), indicating that the 18-22 years time window is sufficient for most basic relations between actors to have developed (Newman 2001a;Barabasi et al 2002).…”

Section: Datamentioning

confidence: 80%

A new approach to analyzing patterns of collaboration in co-authorship networks: mesoscopic analysis and interpretation

2010

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This paper focuses on methods to study patterns of collaboration in coauthorship 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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Section: Datamentioning

confidence: 80%

A new approach to analyzing patterns of collaboration in co-authorship networks: mesoscopic analysis and interpretation

2010

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“…However, as the total number of connections increases, there comes a point at which a giant component forms-a large group of individuals who are all connected to one another by paths of intermediate acquaintances. Newman (2001a) reported that the collaboration networks for MEDLINE, Los Alamos Preprint Archive, SPIRES and NCSTRL possess giant components that capture roughly 80-90 percent of all authors: almost everyone in the community is connected to almost everyone else by some path of intermediate coauthors. Furthermore, Krichel and Bakkalbasi (2006) reported that the giant component of the RePEc network encompasses 83% of its total authors.…”

Section: Resultsmentioning

confidence: 99%

“…The average distance between coauthors in the giant component is 8.2. In comparison, the average distance is 4.4 for MEDLINE, 4.0 for SPIRES, 9.7 for NCSTRL and 5.9 in Los Alamos Preprint Archive (Newman 2001a). The average distance gives a measure of the ''connectedness'' of the network (Kretschmer 2004).…”

Section: Resultsmentioning

confidence: 99%

The structure of collaboration in the Journal of Finance

2010

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This paper studies the structure of collaboration in the Journal of Finance for the period 1980-2009 using publication data from the Social Sciences Citation Index (SSCI). There are 3,840 publications within this period, out of which 58% are collaborations. These collaborations form 405 components, with the giant component capturing approximately 54% of total coauthors (it is estimated that the upper limit of distinct JF coauthors is 2,536, obtained from the total number of distinct author keywords found within the study period). In comparison, the second largest component has only 13 members. The giant component has mean degree 3 and average distance 8.2. It exhibits power-law scaling with exponent a = 3.5 for vertices with degree C5. Based on the giant component, the degree, closeness and betweenness centralization score, as well as the hubs/authorities score is determined. The findings indicate that the most important vertex on the giant component coincides with Sheridan Titman based on his top ten ranking on all four scores.

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“…Several methods for studying networks were highlighted within this review. These included, for example, the mapping of co-author activity as a method for examining network structure (Newman, 2001). This principle could be used to compare different network structures and how they relate to research productivity.…”

Section: Infrastructurementioning

confidence: 99%