Topic diffusion analysis of a weighted citation network in biomedical literature

Munui Kim, Injun Baek, Min Song

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)329-342
Number of pages14
JournalJournal of the Association for Information Science and Technology
Volume69
Issue number2
DOIs
Publication statusPublished - 2018 Feb 1

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Electric network analysis
Proteins
network analysis
literature
Citations
knowledge
Values

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences

Cite this

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Topic diffusion analysis of a weighted citation network in biomedical literature. / Kim, Munui; Baek, Injun; Song, Min.

In: Journal of the Association for Information Science and Technology, Vol. 69, No. 2, 01.02.2018, p. 329-342.

Research output: Contribution to journalArticle

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