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.
|Number of pages||14|
|Journal||Journal of the Association for Information Science and Technology|
|Publication status||Published - 2018 Feb 1|
Bibliographical noteFunding Information:
This work was supported by the Bio-Synergy Research Project (NRF-2013M3A9C4078138) of the Ministry of Science, ICT, and Future Planning through the National Research Foundation.
This work was supported by the Bio-Synergy Research Project (NRF National Research Foundation -2013M3A9C4078138) of the Ministry of Science, ICT, and Future Planning through the National Research Foundation.
© 2017 ASIS&T.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Networks and Communications
- Information Systems and Management
- Library and Information Sciences