Investigating the effect of global data on topic detection

Kevin W. Boyack ()
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Kevin W. Boyack: SciTech Strategies, Inc.

Scientometrics, 2017, vol. 111, issue 2, No 21, 999-1015

Abstract: Abstract A dataset containing 111,616 documents in astronomy and astrophysics (Astro-set) has been created and is being partitioned by several research groups using different algorithms. For this paper, rather than partitioning the dataset directly, we locate the data in a previously created model of the full Scopus database. This allows comparisons between using local and global data for community detection, which is done in an accompanying paper. We can begin to answer the question of the extent to which the rest of a large database (a global solution) affects the partitioning of a smaller journal-based set of documents (a local solution). We find that the Astro-set, while spread across hundreds of partitions in the Scopus map, is concentrated in only a few regions of the map. From this perspective there seems to be some correspondence between local information and the global cluster solution. However, we also show that the within-Astro-set links are only one-third of the total links that are available to these papers in the full Scopus database. The non-Astro-set links are significant in two ways: (1) in areas where the Astro-set papers are concentrated, related papers from non-astronomy journals are included in clusters with the Astro-set papers, and (2) Astro-set papers that have a very low fraction of within-set links tend to end up in clusters that are not astronomy-based. Overall, this work highlights limitations of the use of journal-based document sets to identify the structure of scientific fields.

Keywords: Global data; Direct citation; Clustering; Cluster characterization (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (12)

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DOI: 10.1007/s11192-017-2297-y

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