 Topic diffusion analysis of a weighted citation network in biomedical literatureMunui Kim, Injun Baek and Min Song Journal of the Association for Information Science & Technology, 2018, vol. 69, issue 2, 329-342 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. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jinfst:v:69:y:2018:i:2:p:329-342 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Journal of the Association for Information Science & Technology from Association for Information Science & Technology |
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