Co-occurrence matrices and their applications in information science: Extending ACA to the Web environment

By Loet Leydesdorff, Liwen Qiu Vaughan. J. Assoc. Inf. Sci. Technol., 2006

407 citations recorded by the source corpus.

Co-occurrence matrices, such as co-citation, co-word, and co-link matrices, have been used widely in the information sciences. However, confusion and controversy have hindered the proper statistical analysis of this data. The underlying problem, in our opinion, involved understanding the nature of various types of matrices. This paper discusses the difference between a symmetrical co-citation matrix and an asymmetrical citation matrix as well as the appropriate statistical techniques that can be applied to each of these matrices, respectively. Similarity measures (like the Pearson correlation coefficient or the cosine) should not be applied to the symmetrical co-citation matrix, but can be applied to the asymmetrical citation matrix to derive the proximity matrix. The argument is illustrated with examples. The study then extends the application of co-occurrence matrices to the Web environment where the nature of the available data and thus data collection methods are different from those of traditional databases such as the Science Citation Index. A set of data collected with the Google Scholar search engine is analyzed using both the traditional methods of multivariate analysis and the new visualization software Pajek that is based on social network analysis and graph theory.

Subfields: Ecology, Evolution, Behavior and Systematics, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/aa60c6c125372952d251c846591e2c4dfccd1aa9

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Citations in the corpus

Fundamentals of Information Studies: Understanding Information and Its Environment, Second Edition (2009, methodology): “cocitation) matrices, the normalization of data should be avoided altogether ( Leydesdorff & Vaughan, 2006).”
International collaboration in science and the formation of a core group (2008, methodology): “Data were harvested from the CD-Rom version of the Science Citation Index for articles, reviews, letters, and notes for 1990, 2000, and 2005.1 In his study entitled Evaluative Bibliometrics, Narin (1976) proposed counting only articles, reviews, and notes as indicators of scientific performance.”
Impact of license choice on Open Source Software development activity (2009, methodology): “(r 1) / 2 (Ahlgren et al., 2003, at p. 552; Leydesdorff and Vaughan, 2006, at p.1617).”
How to normalize cooccurrence data? An analysis of some well-known similarity measures (2009, neutral): “We note that in some studies similarities between objects are determined by comparing columns of the occurrence matrix O (e.g., Leydesdorff and Vaughan, 2006; Schneider , Larsen, and Ingwersen, 2008).”
Digital transformation in business and management research: An overview of the current status quo (2022, background): “More generally, these articles are expected to depict central topics and intellectual structures of an area of knowledge (Leydesdorff & Vaughan, 2006).”
Informetrics at the beginning of the 21st century - A review (2008, background): “Leydesdorff and Vaughan (2006) claim that the use of Pearson’s r is not appropriate for symmetrical matrices (cocitation matrices), while it can be used in the asymmetrical case (citation matrices).”