Document (#41037)

Author

Kim, M.
Baek, I.
Song, M.

Title

Topic diffusion analysis of a weighted citation network in biomedical literature

Source

Journal of the Association for Information Science and Technology. 69(2018) no.2, S.329-342

Year

2018

Abstract

In this study, we propose a framework for detecting topic evolutions in weighted citation networks. Citation networks are important in studying knowledge flows; however, citation network analysis has primarily focused on binary networks in which the individual citation influences of each cited paper in a citing paper are considered identical, even though not all cited papers have a significant influence on the cited publication. Accordingly, it is necessary to build and analyze a citation network comprising scholarly publications that notably impact one another, thus identifying topic evolution in a more precise manner. To measure the strength of citation influence and identify paper topics, we employ a citation influence topic model primarily based on topical inheritance between cited and citing papers. Using scholarly publications in the field of the protein p53 as a case study, we build a citation network, filter it using citation influence values, and examine the diffusion of topics not only in the field but also in the subfields of p53.

Content

Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23960/full.

Theme

Informetrie

Field

Medizin

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5. Li, J.; Willett, P.: ArticleRank : a PageRank-based alternative to numbers of citations for analysing citation networks (2009) 0.25

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