Social network analysis: a powerful strategy, also for the information sciences

By Evelien Otte, R. Rousseau. Journal of information science, 2002

1,711 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/fb7cdd6bc35cb989b5093fdb36d7d386db22fe89

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In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Co-authorship networks in the digital library research community (2005, background): “, 2002), information systems (Cunningham and Dillon, 1997), and the field of social network analysis (Otte and Rousseau, 2002).”
Digital transformation in business and management research: An overview of the current status quo (2022, methodology): “While mapping the co-occurrence word analysis, the authors used social network theory to determine the position of each keyword in the network (Freeman, 1978; Otte & Rousseau, 2002).”
Informetrics at the beginning of the 21st century - A review (2008, background): “The basics of social network analysis (SNA) basics are clearly explained by Otte and Rousseau (2002), and its use is demonstrated for a network of authors in the field of social network analysis.”
Toward a basic framework for webometrics (2004, background): “Otte and Rousseau (2002) give an excellent overview of applications and potentials of social network analysis in the information sciences with regard to studies of, for example, citation and cocitation networks, collaboration structures and other forms of social interaction networks, including the…”
Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience (2009, methodology): “These measures can be interpreted in terms of network centralities, which were introduced in bibliometrics to study research communities (Otte and Rousseau 2002) and interdisciplinarity in journal sets (Leydesdorff 2007).”
Betweenness centrality as an indicator of the interdisciplinarity of scientific journals (2007, methodology): “These measures and their further elaboration into relevant statistics were conveniently combined in the software package UCINet that Freeman and his collaborators have developed since the 1980s (Bonacich, 1987; Borgatti et al., 2002; Otte & Rousseau, 2002).”
Betweenness Centrality in Some Classes of Graphs (2014, background): “Introduction Betweenness centrality plays an important role in analysis of social networks [1, 2], computer networks [3], andmany other types of network data models [4–9].”
Preserving integrity in online social networks (2020, background): “This body of work traces back to early work since the inception of social networks that considered social context for content classification [36, 75, 87, 111].”