Main path analysis considering citation structure and content: Case studies in different domains
Dejian Yu and
Zhaoping Yan
Journal of Informetrics, 2023, vol. 17, issue 1
Abstract:
Main path analysis (MPA) is an effective method widely accepted in science and technology for extracting knowledge diffusion paths. Traditional citation analysis assumes that all citations are treated equally. In contrast, this paper proposes a new MPA framework from the perspective of citation structure and content. Three indicators are considered to adjust edge weight: (1) Structural similarity, (2) Topic similarity and (3) Sentiment analysis. This study takes the bullwhip effect and the Internet of Things domain as examples to verify the reliability and feasibility of improved MPA. The results show that the improved main path uncovers the knowledge trajectories appropriately, which has an ability to distinguish citations and detect important papers. This research enriches MPA theory and provides future research directions from perspective of citation structure and content.
Keywords: Main path analysis; Citation structure; Citation content; Structural similarity; Topic similarity; Sentiment analysis (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1751157723000068
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:17:y:2023:i:1:s1751157723000068
DOI: 10.1016/j.joi.2023.101381
Access Statistics for this article
Journal of Informetrics is currently edited by Leo Egghe
More articles in Journal of Informetrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().