Publication Network Analysis of an Academic Family in Information Systems

Jörn Grahl (grahl@uni-mainz.de), Bastian Sand (sand@bisor.de), Michael Schneider (schneider@bisor.de) and Michael Schwind (schwind@wiwi.uni-frankfurt.de)
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Jörn Grahl: Johannes Gutenberg-University Mainz
Bastian Sand: University of Kaiserslautern
Michael Schneider: University of Kaiserslautern
Michael Schwind: Goethe-University Frankfurt

A chapter in Theory-Guided Modeling and Empiricism in Information Systems Research, 2011, pp 1-13 from Springer

Abstract: Abstract The study of scientific collaboration through network analysis can give interesting conclusions about the publication habits of a scientific community. Co-authorship networks represent scientific collaboration as a graph: nodes correspond to authors, edges between nodes mark joint publications (Newman 2001a,b). Scientific publishing is decentralized. Choices of co-authors and research topics are seldomly globally coordinated. Still, the structure of co-authorship networks is far from random. Co-authorship networks are governed by principles that are similar in other complex networks such as social networks (Wasserman and Faust 1994), networks of citations between scientific papers (Egghe and Rousseau 1990), the World Wide Web (Albert and Barabási 1999, Kleinberg et al. 1999) or power grids (Watts and Strogatz 1998). It is therefore not astounding that scholars have studied co-authorship networks in considerable detail and in a variety of contexts, such as physics (Newman 2001a), evolutionary computation (Cotta and Merelo 2007) or computer supported cooperative work (Horn et al. 2004).

Keywords: Centrality Measure; Betweenness Centrality; Community Detection; Scientific Collaboration; Closeness Centrality (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/978-3-7908-2781-1_1

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