Comparer des méthodes

	Analyse de co-citation assistée par bibliometrix	Analyse de co-citation basée sur les réseaux
Domaine	Scientométrie	Scientométrie
Famille	Process / pipeline	Process / pipeline
Année d'origine	2017 (bibliometrix implementation); 1973 (co-citation concept)	1973 (co-citation); network-analytic extension widely adopted 2000s–2010s
Auteur d'origine	Co-citation: Henry Small (1973); bibliometrix package: Massimo Aria & Corrado Cuccurullo (2017)	Henry Small (co-citation foundation); network visualization extended by Chaomei Chen and others
Type	Computational scientometric pipeline	Bibliometric network analysis
Source fondatrice	Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.	Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.
Alias	R bibliometrix co-citation, bibliometrix CCA, co-citation network analysis with bibliometrix, bibliometrix cocitation mapping	co-citation network analysis, bibliometric network co-citation, co-citation mapping, CCA network approach
Apparentées	6	6
Résumé	bibliometrix-assisted co-citation analysis combines Henry Small's co-citation measure with the open-source R package bibliometrix to map the intellectual structure of a research field. When two documents are frequently cited together by third papers, they are considered intellectually linked; the bibliometrix package automates construction of the co-citation matrix, similarity normalization, community detection, and network visualization, turning raw bibliographic exports into interpretable science maps.	Network-based co-citation analysis is a bibliometric technique that measures how often pairs of documents are cited together by later works, then models those relationships as a weighted network. Nodes represent documents (or authors or journals), edges represent co-citation frequency, and network algorithms identify clusters of intellectually related literature. It is widely used in systematic and scoping reviews to map the intellectual structure of a research field.